Additive Manufacturing at the Crossroads: Costs, Sustainability, and Global Adoption

Abstract

Additive manufacturing (AM) is positioned at a pivotal moment, where its long-promised advantages, e.g., lower cost, reduced environmental burden, and accelerated production, are increasingly tangible yet unevenly realized across industries and regions. This review synthesizes evidence from AM processes for different materials to clarify the technical and economic levers that drive outcomes. Cost performance is shown to depend strongly on design choices, deposition rate, post-processing requirements, and feedstock pricing. Environmental impacts hinge on material production routes, regional energy mix, build utilization, and the extent of material reuse. Lead-time reductions are most significant when components are redesigned for AM, when high-throughput processes are applied to compatible geometries, and when production is geographically localized. Emerging digital tools including machine learning, in situ monitoring, and digital twins are accelerating process stabilization and shortening qualification cycles, while hybrid manufacturing lines demonstrate the value of integrating near-net-shape printing with precision finishing. Drawing from these insights, a pragmatic roadmap is proposed: align parts and supply chains with the most suitable AM processes, decarbonize and streamline feedstock production, and increase system utilization. When these conditions are met, AM can deliver broad, quantifiable improvements in cost efficiency, sustainability, and global adoption. By consolidating fragmented evidence into a unified framework, this review responds to the growing need for clarity as AM moves toward broader industrial deployment.

1. Introduction

Additive manufacturing (AM) has moved beyond prototyping to become a production technology at a critical inflection point [1,2,3]. Its appeal is clear: complex parts without molds [4,5], customized components without large batches, production nearer to the point of use, and reduced logistics and inventory burdens [4,5]. Yet key questions persist: can AM compete on cost and scale without undue environmental impact and be adopted globally in a fair and reliable way? While some organizations have progressed from prototypes to certified parts, others still face high costs, slow builds, uncertain quality, and unclear environmental benefit. A coherent framework is therefore needed so that decisions are driven by measurable criteria rather than generic expectations about “printing”. A review at this stage is timely for two reasons. First, evidence across metals and polymers has deepened and diversified, but reported outcomes diverge when the context changes. Gains depend on process class, geometry, feedstock, and energy mix, whereas losses often trace back to unoptimized designs or underutilized equipment. Second, the literature now supports an integrated view in which costs, sustainability, and adoption are treated together rather than separately [6,7]. This view links the physics of deposition and consolidation to design choices, post-processing, and supply-chain placement, using quantitative indicators such as cost per part [8], build time [9], specific energy consumption [10,11], and cradle-to-gate footprint [12].

Costs form the first major challenge. AM systems are capital intensive, and feedstocks are expensive [13,14]. Metal powders must satisfy tight specifications and handling rules [15], while polymers and resins are supplied in premium AM grades [16]. Energy use during builds can be substantial and often dominates when utilization is low or thermal cycles are long [10,11]. Labor is spread across the workflow, design preparation, and setup, and post-processing frequently exceed the direct touch time of the build [17,18]. Quality control introduces additional time and expense through inspection, review of in situ monitoring, and requalification when parameters change [19,20]. Unplanned downtime and failed builds further raise costs, motivating the use of monitoring and feedback control to reduce scrap and stabilize output [21,22].

A realistic assessment must therefore adopt a total cost of ownership (TCO) perspective. TCO encompasses equipment, materials, labor, energy, shielding gas, maintenance, software, training, floor space, scrap, and rework, as well as the cost of capital and risk over the system’s life [2,23]. When these elements are included, unit economics depend more on design and utilization than on a nominal machine-hour rate [24,25]. Process choice then redistributes costs in distinct ways. Powder bed fusion offers high resolution and strong parts but typically requires supports, heat treatment, and careful powder management, all of which add time and cost [26,27]. Directed energy deposition delivers material rapidly and enables large repairs but demands precise paths and downstream finishing to meet tolerance [28,29]. Binder- and sinter-based routes print quickly while deferring cost to debinding and furnaces; their yield hinges on shrinkage control and densification schedules [30,31]. Material extrusion has a low entry cost but can be slow and sensitive to tuning for strength and surface finish [32,33]. Vat photopolymerization provides fine detail, with resin price, post-cure steps, and safety protocols shaping its final cost profile [34,35]. No single process is universally optimal; the appropriate choice depends on geometry, tolerances, mechanical loads, surface requirements, and production volume [36,37].

Hidden costs are equally important. Many studies focus on print time, yet real programs devote substantial effort before and after the build. Design for AM (DfAM) demands time, but it can reduce volume, supports, and finishing, thereby lowering both cost and schedule risk [38,39]. Poor orientation increases supports and waste; weak nesting reduces throughput and raises energy use per part [40]. In metals, support removal can dominate touch labor, and downstream operations such as heat treatment, hot isostatic pressing, machining, and polishing require additional fixtures, tools, and inspection plans [41,42]. DfAM changes the cost equation by enabling part consolidation: multiple components can become one, eliminating joints, fasteners, and seals, reducing assembly time and leak paths and improving alignment [43,44]. Weight can be reduced without sacrificing strength; lattices and topology optimization maintain stiffness with less mass, shifting value from the unit part to the system level through fuel savings and performance gains [45,46]. The true target is not the “cheapest single part” but the “lowest cost for the function” across the assembly and its life cycle [47]. This perspective calls for updated cost models. Break-even curves vary with batch size, learning rate, scrap rate, and yield; early studies and factory data indicate steep learning in setup and parameter selection that can be shortened through feedback and analytics [3,48]. Digital twins and data-driven calibration accelerate convergence and reduce the number of trial builds required to meet specification [49].

Supply chains introduce another dimension. AM can support on-demand spares and long-life platforms, reduce storage of slow-moving items, and enable production near the point of use during outages or crises [5,50]. Digital inventories replace physical stock, but certification and liability remain; the digital file must serve as the single source of truth with version control, access governance, and traceability [51]. The cost of secure data management, verification, and track-and-trace is part of TCO; the benefit is tangible, but not free. When decentralized production nodes are paired with verified processes and cleaner feedstocks, lead times can fall and transport burdens shrink without simply shifting impacts upstream [5,52].

Sustainability is the second major challenge. AM is often viewed as inherently “green,” but its reality is nuanced. Some routes are energy intensive: lasers or electron beams may operate for long thermal cycles, and inert-gas or vacuum systems consume additional resources [10,53]. Powder and filament production carry embodied energy and scrap, while post-processing introduces further heat and chemicals that can move impacts upstream if not carefully managed [54,55]. Credible assessment requires life cycle assessment (LCA) with clear system boundaries and an explicit energy mix, tracing impacts from raw material to end-of-life rather than inferring them from a single process step [56,57]. Results then vary with material, geometry, yield, and region: consolidation and topology optimization can reduce mass in moving parts and lower use-phase burdens, and localized production can reduce transport where demand and utilization justify it. Conversely, poor yields, heavy supports, or high scrap can offset these gains, increasing build-phase electricity use and material losses [58,59,60].

Material management lies at the center of sustainability. Powder reuse reduces waste and cost but must be controlled, since oxygen pickup, particle size drift, and contamination can alter quality; rules and sensors are needed to determine when to refresh or retire lots [61,62]. For feedstocks, structural improvements are emerging: non-melting routes for Ti powders and mechanical upcycling of Ti-6Al-4V swarf both report substantial embodied energy reductions while preserving printability, thereby resetting the baseline for metal PBF [14,63]. On the polymer side, controlled mechanical recycling has demonstrated stable thermal behavior and maintained printability over several loops, with large reductions in cost and cradle-to-gate CO₂ per kilogram of filament [64]. Safer binders and water-based or low-VOC chemistries can reduce operator exposure and emissions during post-processing and cure [65,66,67,68]. End-of-life strategies also matter [69]: remelting for alloys [70,71], grinding for filled polymers [72,73], and separation of embedded lattices show promise but require application-specific testing and standards to safeguard properties and regulatory compliance [74,75].

Process innovations can improve sustainability and cost simultaneously when they target dominant contributors. Multi-laser strategies, refined laser operation modes, and improved scan paths increase throughput and can reduce residual stress and support demand, lowering both energy per part and finishing effort [43,76,77]. In situ monitoring helps avoid wasted builds by detecting defects early, while adaptive control stabilizes melt pools and reduces rework and idle energy [49,78]. For sinter-based routes, optimized debinding and sintering schedules can lower peak temperatures and shrinkage scatter while protecting yield [79,80]. Hybrid process chains that combine AM with conventional manufacturing place material only where needed and then finish efficiently, reducing buy-to-fly ratios and machining waste for large parts [81,82]. When these measures are combined with low-carbon electricity and verified data systems that track each batch, build-phase footprints decline and continuous improvement becomes auditable rather than assumed [60].

Global adoption forms the third challenge. Adoption patterns are uneven across sectors and regions. Aerospace and medical devices lead in safety-critical applications, where higher unit costs are acceptable when weight savings or patient outcomes are significant [50,83,84,85,86]. Automotive uses AM mainly for tooling and selected high-value components, while consumer goods leverage it for customization and rapid style changes [87,88]. Construction explores large-scale printing for speed and geometric freedom, and energy and maritime sectors validate repair and spares, including in-place restoration through wire-arc and laser deposition [23,57]. Education and research continue to build skills and awareness, yet many small and medium enterprises hesitate because capital intensity, learning requirements, and uncertain returns remain prominent barriers [89,90]. Adoption progresses fastest where value density is high, certification pathways exist, and production windows align with AM’s strengths [91,92].

Regional trajectories differ as well. Some countries invest in national centers, testbeds, and standards activities to de-risk scale-up; others present AM as a tool for supply-chain resilience, defense, or local entrepreneurship and skills [5]. Access to finance and to qualified materials and reliable service providers is a recurring bottleneck; feedstock routes and pedigree directly influence both quality assurance and cost [48]. Standards and certification capacity constrain speed in regulated domains, and workforce training often lags demand because AM integrates materials, design, software, and metrology in a single workflow [93,94]. Integrated training that couples DfAM with process control and inspection can shorten the learning curve and reduce scrap in early production [95,96,97].

Policy can accelerate safe adoption when it reduces uncertainty without prescribing specific technological paths. Clear standards and harmonized rules lower buyer–supplier risk and facilitate cross-border trade [98,99], while open benchmarks and transparent reporting of energy use, yield, and quality build trust across sites [100]. Health, safety, and environmental regulations must keep pace with new materials and post-processing chemistries, enabling localized production only where it genuinely reduces risk and impact [101,102,103,104,105].

The crossroads is therefore real. Costs fall in some contexts and rise in others; sustainability gains are demonstrable in certain use cases yet elusive elsewhere; adoption accelerates in a few sectors while stalling in many, widening the gap between leaders and late adopters. Clear decisions require a simple but rigorous framework: start from the function and constraints, select the process–material pair that best serves them, model total cost across the entire workflow, run an LCA with actual yields and the local energy mix, design to remove mass and waste, plan quality from the outset, and maintain a digital thread for traceability. Measure, learn, and iterate. This review is written to support that approach. It offers a practical, evidence-based view of costs, sustainability, and global adoption; shows where AM creates value and where it does not; explains how DfAM and workflow choices shape total cost; clarifies why LCA results depend on geometry, process, and region; maps current adoption patterns and barriers; and outlines policy and education levers that work. The aim is clarity, not hype; thus, engineers, managers, and policymakers can make informed choices at a moment when those choices will shape the next decade of manufacturing. Therefore, while numerous reviews have examined individual AM processes, materials, or environmental aspects, fewer studies have jointly assessed cost performance, sustainability, and global adoption within a unified framework. As AM transitions from prototyping to industrial production, such an integrated perspective is increasingly necessary to support evidence-based decision-making across design, manufacturing, and policy domains. The current review study addresses this gap by synthesizing techno-economic, environmental, and adoption-related evidence across metal and polymer AM, highlighting the conditions under which AM delivers measurable value and where limitations remain.

2. The Additive Manufacturing Techniques

AM processes are generally classified as fusion-based or non-beam-based, depending on the energy source and material consolidation method. Figure 1 demonstrated each 3D printing method. A general comparison of the AM processes is summarized in Table 1.

2.1. Fusion-Based AM

Fusion-based AM processes rely on high-energy sources, such as lasers, electron beams, or electric arcs, to melt and fuse material powders and wire feedstocks. These methods have been pivotal in industries requiring precision and strength, such as aerospace, medical, and automotive.

Powder bed fusion (PBF) [106] is one of the most widely adopted fusion-based AM processes. In PBF, a thin layer of powder is spread across a build platform [107] with a spreading tool [108], and the quality of this layer (its uniformity and packing density) is crucial [109] for achieving consistent part properties and reliable builds [110,111]. Layer quality is strongly influenced by the powder spreading speed and the build chamber temperature [112]. In addition, the temperature [113] and moisture content [114] of the powder feedstock must be controlled to avoid powder agglomeration. A high-energy laser or electron beam then selectively fuses the powder according to the CAD model; the platform is lowered, a new powder layer is applied, and the sequence is repeated until the part is complete. A major advantage of PBF is its capability to produce parts with highly complex geometries, including internal channels and lattice structures that are difficult or impossible to fabricate conventionally [115]. However, the process also presents challenges. Rapid thermal cycling [116] generates residual stresses [77,117] that can cause distortion or cracking [118], and achieving uniform density and mechanical performance requires tight control over process parameters [119] such as laser power, scan speed, hatch spacing, and layer thickness. Current research focuses on process optimization and in situ monitoring to reduce defects and improve repeatability. PBF is now applied to a wide range of metals (e.g., Ti, Al, and stainless steels alloys), as well as selected polymers and ceramics. Its successful use in aerospace components and patient-specific biomedical implants highlights its role as a key technology for high-value, performance-critical manufacturing.

Directed energy deposition (DED) is a versatile AM process in which a laser (L-DED) or electron beam (EB-DED) melts powder or wire as it is delivered into the melt pool [106]. This directed-feed approach makes DED particularly well suited for part repair, feature addition, and large-scale fabrication. The process supports a wide range of materials and enables multi-material builds and composition gradients, offering design and functional flexibility beyond many other AM techniques. Common applications include wear-resistant coatings [120], repair of high-value components [121], and the fabrication of parts with graded or tailored material properties [122]. Despite these advantages, DED faces several challenges. Compared to PBF, it typically offers lower geometric resolution and can exhibit variability in material properties across a build. Achieving consistent quality requires tight control of deposition rate [123], building strategy [121], and energy input [124].

To address these issues, advanced computer-aided DED systems [125] increasingly integrate high-speed X-ray imaging [126], infrared thermography, and other sensor modalities [127], along with deep learning-based process analytics [128]. These tools enable real-time monitoring of melt-pool behavior, defect detection, and dynamic adjustment of process parameters [129], helping to mitigate porosity, residual stress, and geometric deviations.

Wire arc additive manufacturing (WAAM) is a wire-fed process that is often categorized within DED when an electric arc is used as the heat source [106]. WAAM employs a metal wire feedstock and an arc-based energy source to deposit material, enabling the rapid fabrication of large, structurally robust components with minimal material waste [130]. Its scalability makes it particularly valuable in shipbuilding, construction, and aerospace, where component size and structural integrity are key requirements [131]. In WAAM, wire is continuously fed into the arc, melted, and deposited layer by layer onto a substrate. The process is comparatively fast and cost-efficient, making it suitable for applications where fine geometric precision is not the primary constraint. However, WAAM also presents challenges, including limited dimensional accuracy [132], coarse surface finish [133], and susceptibility to thermal distortion due to significant heat input [134]. As a result, post-processing such as machining or polishing is frequently needed to achieve final specifications. Current research is focused on enhancing process control and expanding capability through advanced robotic platforms [135] and digital twin-enabled systems [136]. These approaches, along with integrated sensing and real-time monitoring, aim to improve reliability, reduce variability, and broaden the range of materials and applications suitable for WAAM.

2.2. Non-Beam-Based Processes

Non-beam-based AM processes provide alternatives to concentrated-energy source methods, generally offering lower cost, simpler operation, and wider accessibility.

Binder jetting selectively deposits a liquid binder onto a powder bed to create green parts layer by layer [137,138,139]. Because no melting occurs during printing, thermal stresses are reduced and heat-sensitive materials can be used; final properties are then developed through post-processing such as sintering or infiltration [140]. The process is fast and relatively economical, and it can be applied to metals [141,142,143,144,145,146,147,148,149,150,151,152], ceramics [153,154,155], and polymers [156]. However, the as-printed mechanical properties are typically inferior to those from fusion-based routes, so current work focuses on optimizing binder chemistry and powder characteristics, as well as applying advanced post-processing such as hot isostatic pressing [157,158] and tailored infiltration strategies [159]. Multi-material binder jetting is also emerging, with the potential to enable more complex, functional parts [160].

Material extrusion deposits material through a nozzle to build parts layer by layer, commonly using thermoplastic filaments but also pellets, pastes, or slurries that are bonded by thermal or chemical mechanisms [106]. Its low cost and ease of use have made it the dominant choice for prototyping and many entry-level applications, while ongoing material developments are extending the palette of thermoplastics and composites [161,162,163,164], including carbon fiber-reinforced polymers for lightweight, high-strength components in the aerospace and automotive sectors [165,166,167]. Key challenges remain in resolution, mechanical consistency, and surface finish [168]. In response, research is advancing precision nozzle designs [169], improved cooling and thermal management [170], more sophisticated slicing and path-planning algorithms [171], and the integration of smart sensors and real-time monitoring to enhance reliability and dimensional accuracy [172,173].

Table 1. Overview comparison of AM processes.

AM Process	Printable Materials	Feedstock Type	Production/Deposition Rate	Advantages/Characteristics	Drawbacks	Common Defects	Applications/Industries	Notes
PBF	Metals, polymers, ceramics [174,175,176,177,178,179]	Powder (fine engineered particles)	Low (10–50 cm3/h) [180,181,182]	High resolution, good surface finish, high accuracy	Expensive, slow build speed, limited build size	Porosity, lack of fusion, residual stress, cracking	Aerospace, medical implants, automotive, dental	Laser or electron beam used, high energy density
DED	Metals, some polymers and ceramics [120,183,184,185,186,187,188]	Powder or wire	Moderate (60–1000 cm3/h) [189,190,191]	Repair and hybrid manufacturing, multi-material capability	Surface finish not optimal, complex system	Porosity, dilution, residual stresses	Aerospace, repair, oil and gas, tooling	Can build on existing parts, flexible head movement
WAAM	Metals [121,123,192]	Wire	High (>1000 cm3/h) [131,193,194]	Very high deposition rate, low cost for large parts	Poor surface finish, low precision, post-processing needed	Porosity, spatter, cracking, warping	Marine, construction, aerospace (large parts)	Best for large-scale metal components
Binder Jetting	Metals, ceramics, sand, polymers [142,147,156,195,196]	Powder (binder + powder system)	Moderate to high (160–1600 cm3/h (depending on binder curing and post-processing) [137,197,198]	No thermal distortion, scalable to large builds	Requires sintering/post-processing, lower mechanical strength	Delamination, binder bleeding, low green part strength	Tooling, sand molds, biomedical scaffolds	Post-processing is essential (sintering, infiltration, HIP)
Material Extrusion	Thermoplastics, composites [162,199,200]	Filament or non-filament (pellet /paste/slurry) [201,202,203]	Low to moderate (5–250 cm3/h) [204,205,206]	Low cost, easy to use, widely available	Limited to thermoplastics, anisotropy, low strength	Warping, layer adhesion issues, stringing	Prototyping, education, hobbyist, tooling	Good for functional prototypes and education

Table 1. Overview comparison of AM processes.

AM Process	Printable Materials	Feedstock Type	Production/Deposition Rate	Advantages/Characteristics	Drawbacks	Common Defects	Applications/Industries	Notes
PBF	Metals, polymers, ceramics [174,175,176,177,178,179]	Powder (fine engineered particles)	Low (10–50 cm3/h) [180,181,182]	High resolution, good surface finish, high accuracy	Expensive, slow build speed, limited build size	Porosity, lack of fusion, residual stress, cracking	Aerospace, medical implants, automotive, dental	Laser or electron beam used, high energy density
DED	Metals, some polymers and ceramics [120,183,184,185,186,187,188]	Powder or wire	Moderate (60–1000 cm3/h) [189,190,191]	Repair and hybrid manufacturing, multi-material capability	Surface finish not optimal, complex system	Porosity, dilution, residual stresses	Aerospace, repair, oil and gas, tooling	Can build on existing parts, flexible head movement
WAAM	Metals [121,123,192]	Wire	High (>1000 cm3/h) [131,193,194]	Very high deposition rate, low cost for large parts	Poor surface finish, low precision, post-processing needed	Porosity, spatter, cracking, warping	Marine, construction, aerospace (large parts)	Best for large-scale metal components
Binder Jetting	Metals, ceramics, sand, polymers [142,147,156,195,196]	Powder (binder + powder system)	Moderate to high (160–1600 cm3/h (depending on binder curing and post-processing) [137,197,198]	No thermal distortion, scalable to large builds	Requires sintering/post-processing, lower mechanical strength	Delamination, binder bleeding, low green part strength	Tooling, sand molds, biomedical scaffolds	Post-processing is essential (sintering, infiltration, HIP)
Material Extrusion	Thermoplastics, composites [162,199,200]	Filament or non-filament (pellet /paste/slurry) [201,202,203]	Low to moderate (5–250 cm3/h) [204,205,206]	Low cost, easy to use, widely available	Limited to thermoplastics, anisotropy, low strength	Warping, layer adhesion issues, stringing	Prototyping, education, hobbyist, tooling	Good for functional prototypes and education

3. Techno-Economic and Sustainability Assessment

Here, key perspectives on AM are outlined, including material utilization and waste, highlighting its potential to reduce raw material losses relative to conventional processes, and production costs, with an emphasis on major drivers such as machine investment, feedstock price, and labor requirements. Energy consumption is assessed in comparison with traditional manufacturing across relevant life cycle stages. The economic impact and adoption trajectory of AM are considered in terms of cost-effectiveness for small-batch production and the gradual diffusion of the technology. Finally, environmental implications are examined through life cycle assessments, focusing on energy demand, material efficiency, and waste reduction [1,6,7,10,23,36,207,208,209,210].

3.1. Material Use and Waste

One of AM’s most notable advantages over conventional manufacturing is its potential for superior material efficiency. AM fundamentally changes how material is used in production, offering substantial reductions in waste while also presenting its own challenges [10]. By building components layer by layer, AM enables near-net-shape fabrication, which is particularly beneficial for complex geometries that would incur high waste in subtractive processes such as CNC (Computer Numerical Control) machining, welding, or forging [7,209,211]. Subtractive methods often remove large volumes of material from an initial billet, with material losses reaching as high as 90% in some cases [212]. In contrast, AM typically achieves material utilization rates above 90%, lowering raw material costs and reducing the environmental footprint associated with excess scrap.

AM’s efficiency is especially evident when the solid-to-envelope ratio is low, i.e., when the final component occupies only a small fraction of its bounding volume. This is reflected in improved buy-to-fly ratios, where AM provides clear environmental and economic benefits in cases where conventional methods require five to seven times more starting material than the final part mass [7,10,36,208]. However, AM processes are not free from waste. Techniques such as PBF and DED often require support structures that must be removed and may not always be recyclable. Feedstock production (particularly metal powder atomization) is energy-intensive and generates its own material losses. In PBF, unused or degraded powder may need to be discarded, and build failures or machine errors contribute further waste. Thus, while AM offers substantial material utilization advantages, realizing these benefits requires careful control of both feedstock and process efficiency.

Recycling and reuse are emerging strategies to address material efficiency challenges in AM. Failed prints, support structures, unused powders, and end-of-life parts can be reprocessed into new feedstock. Recycled polymers, especially when reinforced, can recover sufficient performance for AM applications, and many metal alloys can also be re-melted and reused, though recycling pathways carry their own environmental burdens. Well-managed recycling operations can reduce energy use by up to 80%, significantly improving the sustainability profile of AM systems [6,10,213,214,215,216,217]. Design optimization, particularly topology optimization, further enhances material efficiency by reducing overall volume and limiting the need for supports while preserving functionality [10,38,39]. Nonetheless, some AM routes still generate substantial waste: support structures (especially in PBF and DED) can notably increase material consumption and are often difficult or uneconomical to recycle [58], in some cases driving production costs upward by more than 170% [43]. Overall, AM shows strong potential to reduce material waste—especially for complex geometries and lower production volumes—but persistent issues related to supports, raw material production, and process-specific losses underscore the need for continued advances in design strategies and recycling to fully realize these benefits [208].

Process-level studies demonstrate how targeted control can further enhance utilization. One study improved height stability in CuCrZr WAAM using a dual control scheme combining lift-height regulation and wire-feed iterative learning, reducing energy input and height fluctuation while increasing material-forming utilization by about 50% and improving surface quality [218]. Another study applied multi-objective optimization to high-power L-DED of TC4, tuning laser power, scan speed, and powder-disk rotation to keep powder utilization within a controlled window, maintain bead geometry, and raise effective powder use without sacrificing mechanical properties [76]. At the fixture level, modular clamp-assembled substrates made from welding scrap [219] were shown to reduce distortion, simplify part removal, lower substrate and separation costs, and valorize waste stubs. Together, these examples show how control, optimization, and circular substrate concepts can simultaneously improve material efficiency, cost, and practical sustainability in DED-based processes. Together, these studies underline a practical rule: when designs enforce self-support and exploit hollow sections, orientation, path strategy, and over-thickness become small but powerful levers that sharply reduce waste, input stock, and associated energy use.

Efficient material use is a central promise of AM, and recent work shows how process planning can turn this into measurable savings. A process-planning strategy for extrusion-based printing [220] minimizes supports by imposing limits on overhang angle and bridge length, allowing many internal surfaces to become self-supporting and reducing infill. In tests on “U”, “O”, and “A” geometries, this approach cut material use for the “O” part by 29.3%, 39.6%, and 46.3% relative to 20%, 50%, and 80% infill, respectively, while significantly shortening build time and lowering energy demand. At a larger scale, a comparison of a fully machined aeronautical tool with a WAAM-plus-machining route (see Figure 2) [221] showed that WAAM reduced raw input from 291.345 kg to 173.091 kg and final mass from 152.747 kg to 90.960 kg—about a 41% reduction in both. The initial 10 mm over-thickness proved conservative; reducing it to 3 mm would yield a theoretical 56.3% raw-material saving relative to pure machining. Enabled by hollow internal channels, the WAAM route achieved comparable geometry with far less input stock and lower total energy use.

3.2. Production Costs

Production costs are a core determinant of AM’s economic viability and are driven primarily by machine and material expenses, followed by labor, build orientation, envelope utilization, build time, energy use, and design choices [1]. Support structures and post-processing can add substantially to total cost in many processes [10], and non-recurring engineering (e.g., design and development) remains a significant early-stage cost [36]. In 3D construction printing, materials can represent up to 70% of direct costs, with machinery, labor, transport, warehousing, and installation comprising the remainder [23]. Overall cost-effectiveness depends strongly on production volume and part complexity: AM is often competitive for low-volume, customized, or high-value density parts, particularly where conventional methods face high tooling costs or large material losses [1,7,10,36].

AM does not benefit from economies of scale in the same way as traditional manufacturing. Metal powders can cost 5–10× more than bulk feedstock for machining, and polymer filaments can be 50–100× more expensive than commodity pellets [207]. With slower build rates, machine costs per part are also higher. However, AM can outperform machining or molding in specific regions of the design–volume space—for example, for parts with very low solid-to-envelope ratios or when machining requires large billets with high scrap [1,7,10,36]. Break-even points vary widely: for some bearing blocks, PBF is more economical than CNC milling below 5000 units; in injection molding, break-even points range from 40 to 87,000 units depending on geometry and process parameters. In aeronautical cases, AM becomes viable above ≈15 parts, whereas simple steel walls remain cheaper to machine unless the buy-to-fly ratio exceeds 5, favoring WAAM [1,36].

Cost structures differ by process. In PBF, material cost dominates; in stereolithography and material extrusion, machine cost is the major contributor [1]. For WAAM, labor and machine cost account for 44% and 35%, respectively, while for PBF, machine and labor contribute 51% and 41%. In CNC milling, labor remains the largest share at 56%, with machine costs at 22% [36].

Recycling and design for AM can yield notable cost improvements. A study [64] show that recycled PMMA filament can be produced for ~EUR 21.13/kg, less than half the ~EUR 50/kg price of commercial filament, with mechanical properties retained across multiple reuse cycles. In aerospace, topology optimization and life cycle cost analysis was applied to a Ti-6Al-4V fitting produced by L-PBF (see Figure 3), reducing part mass from 920 g to 542 g, raw material demand from 14 kg to 0.673 kg, and manufacturing cost from EUR 725.30 to EUR 440.80 per part, a 39% reduction at an annual volume of 108 parts. Print orientation alone changed the cost by 32%, and batching four parts together reduced unit cost by 21% [103].

Together, these results show that AM costs arise from a complex interaction of materials, machines, labor, and design decisions. While AM rarely matches the per-part cost of high-volume conventional manufacturing, it can offer strong economic advantages when material efficiency, design optimization, recycling, and batch planning are properly leveraged.

3.3. Energy Consumption

Energy consumption is a critical factor for both the sustainability and cost-effectiveness of AM. While AM can reduce material waste, the production of printable feedstocks, especially metal powders via atomization, is highly energy intensive [10,207]. For small, complex parts with high material waste in conventional machining, AM can offer energy advantages (e.g., PBF can have a lower energy and environmental impact when the milling raw material-to-part-volume ratio exceeds about 7). However, in other cases AM is more demanding: an EB-PBF turbine build, for instance, required about 25% more energy than milling due to slower processing, and PBF steel walls consumed more energy than WAAM or CNC for simple geometries [10].

Total AM build energy is often decomposed into primary energy (from core systems such as lasers/electron beams and heaters) and secondary energy (from chillers, motors, ventilation, control electronics, and idle loads). Both together define the total energy per part. Several process factors strongly influence this total. Higher capacity utilization (printing multiple parts in one build) can lower energy per part by roughly one-third; one study reported about a 32.7% reduction with increased loading, mainly through better use of ancillary systems [56]. Layer thickness and orientation also matter: coarser layers reduce the number of slices and auxiliary runtime, and reorienting parts from a tall (Z) to a shorter (Y) direction cut energy usage by about 50% in one PBF case, even though laser exposure time remained similar, while idle power still contributed around 40% of total energy [56]. Post-processing adds further demand: adding wire-EDM to metal PBF can raise energy use by 36–49%, and in WAAM, post-processing can account for about 21% of the total environmental impact [10,36].

Across the cradle-to-gate life cycle, material production (especially metals and polymers) typically dominates energy demand, with AM machine operation and post-processing as the next largest contributors. Process choice (PBF vs. WAAM vs. CNC), material properties, part geometry (solid-to-envelope ratio), and production volume all modulate energy intensity. AM often has higher per-part energy at low volumes due to fixed overheads but can become competitive where geometry is complex and conventional routes would waste large amounts of material [10,36].

Energy use feeds directly into economics through electricity cost and indirectly through longer machine times; machine-related costs can reach 20–44% of total production cost in metal AM. Design and process optimization can therefore shift energy and cost break-even points. Strategies include reducing support and part mass (e.g., via topology optimization), tuning parameters to shorten builds, improving feedstock production and recycling (recycled polyethylene filament, for example, can cut energy use by up to 80%), and using lower-carbon electricity [7,10].

System-level and decision-support studies highlight how these factors combine. One cradle-to-gate, partial LCA comparing AM and CM for a seven-component electric ducted fan found that, across lower, median, and upper specific energy consumption bounds, AM consumed less energy per unit mass overall than CM once transportation was included: logistics increased CM energy use by a factor of 3–4.5 and transport costs by 6.4 compared with AM. The energy to transport three CM parts was roughly equivalent to the energy needed to manufacture one similar part by AM. Geometry, expressed as solid-to-envelope ratio α, governed the choice: AM was preferred for α ≈ 0.08–0.22, with the critical threshold α_crit shifting higher when transport was included, making AM more favorable in longer CM supply chains [11].

A complementary decision-support tool (DST) uses Cumulative (primary) Energy Demand to compare PBF and machining on a cradle-to-gate plus end-of-life basis [40]. A simplified analytical model (Model III) retains only key inputs (embodied energy, specific energy consumption, and a few geometric/design ratios) and represents the choice as a linear threshold in a 2D plot: points below the line favor AM, those above favor machining. Validation for Ti-6Al-4V, 316L, and AlSi10Mg shows clear trends: lightweighting (lower mass factor k), higher geometric complexity (lower solid-to-cavity ratio), and high-embodied energy materials (e.g., titanium) all expand the region where AM is greener, while steels and aluminum tend to favor machining unless AM’s specific energy is very low.

At the process-control level, energy and material efficiency can be improved together. One WAAM study on thin-walled parts introduced a variable-polarity TIG and fused-deposition strategy with closed-loop, time-varying control. Matching parameters to local geometry increased material utilization from 65.7% to 99.5% and shortened production time from 1980 s to 868 s, while achieving ~98% forming accuracy and stable, defect-reduced walls [222]. These results show that targeted control, smarter design, and supply-chain integration can all substantially improve the energy performance of AM relative to conventional manufacturing.

A new melt-less route for spherical Ti-6Al-4V powder, the Direct Reduction and Alloying (DRA) process, has been demonstrated as a lower-energy alternative to conventional Kroll + atomization feedstock production (see Figure 4) [14]. The route combines hydrogen-assisted magnesiothermic reduction of oxides with granulation–sintering–deoxygenation, producing spherical powder with low interstitials (O < 0.12 wt%) and homogeneous composition. Flowability of 15–45 µm powder is excellent at 25 s/50 g, comparable to plasma-atomized powder (PA) at 28 s/50 g. Fine-powder yield (<45 µm) reaches ~60%, versus ~25% for electrode induction melting inert gas atomization (EIGA), which directly lowers the embodied energy per kilogram of usable feedstock. The embodied energy of DRA Ti-6Al-4V powder is ~366 MJ/kg, a reduction of 54% relative to EIGA and 59% relative to plasma atomization; for PBF feedstock, the effective value is ~611 MJ/kg, 81% lower than EIGA (3159 MJ/kg) and 73% lower than plasma atomization (2246 MJ/kg). Process wastes are minimal and largely limited to an aqueous MgCl₂/NaCl stream after neutralization, supporting DRA as a promising lower-energy pathway for “green AM” [14].

Energy reduction at the process level is also achievable through parameter optimization. A multi-objective framework for high-power L-DED of TC4 [76] optimized laser power, scan speed, and powder-disk rotation (e.g., 3785 W, 24.05 mm/s, and 7 r/min) using an energy model that accounted for laser, chiller, feeder, and motion systems, with prediction errors within 4%. Total energy per build decreased from 1.10 kWh to 0.41 kWh (−62.64%) while maintaining deposition efficiency (22.35 vs. 22.84 mm³/s) and meeting geometric (aspect ratio 3–5) and powder utilization (30–80%) constraints. The microstructure and properties remained robust, with a grain size ~242.5–245.1 µm and tensile/yield strengths of 951 MPa and 869 MPa, respectively. These results show that substantial energy savings can be achieved without sacrificing productivity or performance [76].

A rapid, energy-efficient additive process for ceramics (REAP) has been demonstrated as a way to decouple consolidation energy from part geometry [223]. The method prints a reactive preform and then initiates a self-sustaining reaction that requires only a brief ignition, less than 10 s at 192 W (≈1920 J). After ignition, no external heating is needed, and the reaction propagates at roughly 130 cm/min. Because the process relies on stored chemical energy rather than prolonged furnace heating, total external energy input does not scale with part volume; for a 100 cm³ part, the normalized input can be as low as 19.2 J/cm³. Compared with conventional ceramic firing, consolidation energy is reduced by roughly three orders of magnitude, shrinking hours-to-days of sintering into seconds while still producing robust, freestanding structures. This enables energy-lean fabrication of complex ceramic components such as gyroid heat exchangers.

Zakaria and Mativenga [224] established a scientific basis for minimizing energy consumption in Fused Deposition Modeling (FDM) without compromising product quality. A structured Design of Experiments (DoE) was applied on an industrial Stacker S4 using polylactic acid, and material temperatures were selected from Differential Scanning Calorimetry (DSC) rather than vendor defaults. Power draw from sub-systems was measured with an Internet of Things (IoT) meter across warm-up, printing, and cooling. Signal-to-Noise (S/N) analysis and Analysis of Variance (ANOVA) isolated the parameters that govern energy use, Scope 2 emissions (indirect emissions from purchased electricity), and part performance. The heated bed was identified as the dominant load, responsible for 60–70% of the total energy; under certain conditions, basic (idle/maintenance) requirements reached 81.8% of total consumption. Parameter influence was ranked as layer thickness (46% contribution), printing speed (41%), and bed temperature (12%), showing that time at temperature drives demand. Energy-optimal settings were then derived while preserving tensile strength and surface quality. The bed temperature was set 11.49 °C below the glass transition temperature (Tg) determined by DSC, and the nozzle temperature was held above the melting temperature to ensure bonding. An operating point of a 50 °C bed, 0.35 mm layer thickness, and 40 mm s⁻¹ printing speed achieved a 48–72% reduction in energy use versus average or maximum cases and delivered a 72% decrease in Scope 2 carbon emissions (0.014 kg CO₂e vs. 0.051 kg CO₂e). These results indicate that scientifically selected temperatures, faster deposition, and thicker layers shorten thermal exposure and lower electricity demand while maintaining quality. Figure 5 illustrates that total electricity use is dominated by system upkeep and bed heating, while adjusting layer thickness, print speed, and bed temperature yields the largest energy reductions.

Overall, AM energy consumption depends on process type, feedstock preparation, part design, build parameters, and post-processing. AM can offer energy advantages for complex, low-volume parts where conventional methods incur large material waste but can also be more energy-intensive in other contexts. Improving efficiency across all stages (from feedstock production to build strategies and post-processing) is essential for the long-term sustainability and competitiveness of AM.

3.4. Lead Times

AM can significantly shorten lead times by enabling rapid design, testing, and production directly from digital models, eliminating tooling, mold fabrication, and much of the machine setup required in conventional manufacturing. Alloy or geometry changes can be implemented quickly by updating the build file, creating a fast design–feedback loop and supporting in-house development of new materials without waiting for vendor-specific processing. AM is particularly advantageous for prototypes, low-volume, customized parts, and spares, where on-demand production reduces inventories, storage costs, and changeover time, benefits that are especially valuable in aerospace, defense, and medical applications. Its decentralized deployment can also shorten supply chains, lowering transport time and logistics risk [24,225,226].

Quantitative studies show when AM actually delivers shorter lead times and when it does not. In one comparison, an AM route achieved a lead time of 3 days versus 6 days for a conventional freeze-drying process, whose production rate was 24.24% lower [48]. Another study on quenching tools reported 220 h for the current route, 144 h for a conventional multi-operation automated cell, 204 h for an initial AM route without redesign, and 116 h after redesign and hollowing (−67% material), making the improved AM option the fastest overall [227]. Scheduling rules also matter: in a make-to-order AM system, a complexity-based dispatching rule reduced average order lead time from 16.84 to 16.57 h compared with FIFO by better handling highly complex parts [228].

Comparisons across processes show that AM is not always faster. For prismatic aluminum parts from rod blanks, both PBF and high-speed machining (HSM) can deliver short lead times at low volume, but PBF would need more than an 8× speed increase to be economically favorable [37]. Benchmarks also indicate that PBF can be about 10× slower and 5× more expensive than DED, implying that high-rate DED/WAAM is better suited to large, coarse-featured parts, while PBF remains the choice for smaller parts with intricate internal features [229]. In service applications, process choice can collapse lead time dramatically: in-place WAAM repair of steel rollers, for example, can restore a single roll in under 3 h, avoiding the full cycle of producing and shipping a new part. At the supply-chain level, integrated LCC/LCA modeling shows that decentralized AM close to end users can remove days of transport and improve responsiveness, especially for products with stable demand and short manufacturing lead times, provided local capacity is managed to avoid queue congestion [5].

Ransikarbum et al. [230] developed a decision-support model for additive manufacturing (AM) that schedules batches across a printer network using multi-objective optimization and the Analytic Hierarchy Process (AHP). Process time was modeled by technology: material extrusion (ME) summed the print times of all assigned parts, while Stereolithography (SLA) and Selective Laser Sintering (SLS) used the maximum part time per batch because layers are scanned simultaneously. Preparation, cooling, and post-processing were included to reflect total time. Lead time was treated as lateness against due dates. Minimizing cost concentrated work on cheaper, smaller machines and produced 196 days of total lateness; prioritizing lateness cut delays to 138 days but raised operating cost from USD 6158 to USD 15,108. The lateness objective was the most computationally demanding, with average solve times above 2400 s. These results show that time and cost pull in opposite directions and that explicit scheduling rules are required when meeting deadlines is paramount.

Alogla et al. [231] mapped AM to supply-chain flexibility and compared Laser Sintering with injection molding (IM) for a pipe fitting. An EOS P770 AM run produced 1584 units in 82.87 h (build plus post-processing), while IM achieved a cycle of 74 s for 4 units. Changeover favored AM at 0.33 h versus 4.91 h for IM, confirming rapid product switching without tooling. Delivery flexibility—defined as the ability to compress lead time for rush orders—remained higher for IM (92.8%) than AM (75.35%) due to buffer inventory and immediate dispatch in the IM route. New product introduction strongly favored AM, where setup cost and time were effectively zero, compared with about EUR 30,000 and longer lead times for IM tooling. Taken together, these studies indicate that AM adoption advances where mix and new-product flexibility dominate, but volume and rapid delivery still favor IM unless AM capacity, scheduling, and inventory policies are aligned with demand. Placement: 3.4 lead time (primary) with links to 3.5 economic impact and adoption rate. Figure 6 contrasts the incumbent and AM-only supply chains and shows that AM increases flexibility, while unit cost favors injection molding at higher volumes.

Across these works, the trend is consistent: lead time improves when AM is paired with redesign to reduce material, when scheduling accounts for part complexity, when high-deposition-rate processes are selected for large components, and when production is located near the point of use. Conversely, AM that simply replicates a conventional design without optimization can be slower than a well-organized machining cell.

3.5. Economic Impact and Adoption Rates

AM is already creating a measurable, though still modest, economic impact. The global value added by AM is estimated at about USD 667 million, only 0.01% of total manufacturing output, with roughly USD 241 million in the United States [1]. Its influence is concentrated in high-value sectors such as aerospace, automotive, medical, and energy [6,7,209]. In aerospace alone, the AM market was valued at USD 1.76–2.66 billion in 2021, with a projected CAGR of ~19–20%, driven by developments in hydrogen aviation, urban air mobility, and electrification [50]. AM also underpins remanufacturing: U.S. remanufacturing businesses generate about USD 53 billion annually [6,232], and optimized AM use in aircraft engine MRO can cut unit repair costs by around USD 250 [233]. Businesses report adopting AM mainly to save time (23%) and money (24%), with estimated global savings by 2025 of USD 113–370 billion in manufacturing and USD 56–219 billion in use-phase, largely through lower material inputs, shorter supply networks, and lightweight parts that reduce operational energy demand [24,56].

Economic performance is strongly context-dependent. AM provides clear cost and time benefits in low-volume, customized, and rapid prototyping scenarios, enabling on-demand spares, make-to-order models, and reduced inventories. Simulation studies suggest production and supply-chain costs can fall by up to 31.46%, and in-house AM for high-demand enterprises can yield up to 20% savings through higher utilization and bulk feedstock purchasing [10,24,233]. However, AM is generally less competitive for mass production, where traditional methods still benefit from economies of scale. Machine costs fell by about 42% between 2001 and 2013, but material costs remain high: metal powders are typically 1–3× more expensive, and plastic filaments 50–100× are more costly than conventional bulk materials. Life Cycle Costing shows that AM can reduce total cost by 33.2% for some aeronautical components, yet increase it by 79.3% for certain industrial machinery parts due to expensive machines, materials, and post-processing [1,6,10,24,210]. Environmental impacts can also be higher where feedstock preparation is energy-intensive and support structures are substantial.

Adoption is advancing steadily, particularly in aerospace and medical sectors, where processes such as PBF and DED are used for direct part manufacture, tooling, and MRO and where lead-time reduction, design freedom, and localized production are highly valued [1,7,10,50,207,210,225]. AM’s current influence on supply chains is rated as moderate—it improves responsiveness and reduces logistics dependency, especially when deployed in distributed networks—but its role is expected to grow as key barriers are reduced. Persistent obstacles include slow build speeds, extensive post-processing, unstable outputs, limited multi-material capability, IP and certification concerns, lack of standards, and high capital and switching costs [6,10,50,207,210]. Economic drivers remain the primary motivator for adoption, with environmental and social benefits treated as secondary [7,207,209,210,226].

Firm-level analyses highlight where to invest for faster and more effective adoption. One study [234] identifies five critical success factors for AM-based competitiveness: “customized production with speed” accounts for nearly half the total weight (0.4966), followed by “sustainable production” (0.2414), “standardization in the AM industry” (0.1186), “flexible manufacturing for complex products” (0.1080), and “digital skills and infrastructure” (0.0354). Causal analysis shows that customization with speed is the primary driver, reinforced by standardization and digital capability, while sustainability emerges mainly as a resulting benefit.

Closed-loop recycling and business-driven planning can further strengthen AM’s economic case. A profit-oriented model for recycling thermoplastic filament in a closed-loop supply chain [235] shows that a daily profit-driven plan can increase profit by 31.02% compared with an order-driven plan, while a 10-day plan raises profit by 27.89% and improves product diversity. Sensitivity analysis indicates that a 20% change in material cost shifts profit by 9.19%, and a 20% change in electricity price shifts profit by 5.58%, highlighting where cost control matters most.

Jiang et al. [236] developed expert-validated scenarios for additive manufacturing (AM) in 2030 using a Real-Time Delphi study. Competitive advantage was projected to shift from scale and logistics toward design capability and access to user networks. Supply chains were expected to bifurcate, with critical spares centralized and non-critical parts produced locally, reducing warehousing and transport exposure. Consumer ownership of printers was judged unlikely, with a 32% probability assigned to widespread home adoption, while industrial uptake and localized production were viewed as more probable, including a >54% likelihood of “deglobalization” effects. Adoption drivers were linked to multi-material printing and embedded electronics, which broaden application scope and support new digital business models. Figure 7 shows expert-derived projections in which localized, design-led AM is the most probable 2030 outcome, while alternative consumer pathways remain plausible but uncertain.

Godina et al. [237] examined AM within Industry 4.0 and proposed a Balanced Scorecard (BSC) to measure economic, environmental, and social performance. Economic gains were associated with lower inventories, shorter development cycles, and on-demand spare parts that extend product life, aligning AM with circular economy goals. Market growth was described as rapid, with volume estimated at EUR 11 billion around 2020 and an optimistic trajectory toward EUR 130 billion within the following decade. Adoption was argued to accelerate when AM is integrated with the Internet of Things (IoT) and data analytics, which improve monitoring, utilization, and interoperability; however, slower build rates and higher cost per part still confine broad deployment to sectors with high-value density such as aerospace, medical, and automotive. Figure 8 demonstrates how AM capabilities expand when tightly integrated with IoT, AI, robotics, and digital twins, enabling faster learning cycles and more stable production.

Overall, AM’s economic and adoption trajectory is positive but uneven. Significant advances, such as a 450% reduction in printing time between 2004 and 2014, suggest that continued reductions in production cost, higher throughput, improved materials, and better part quality, supported by targeted policy measures and standards, will progressively expand AM’s role and impact across manufacturing sectors [24,56].

3.6. Environmental Impact

The environmental impact of AM is multifaceted and depends on process type, part geometry, and life cycle stage [238]. AM can offer clear advantages over conventional manufacturing by reducing material waste: in subtractive processes such as CNC machining or forging, up to 90% of raw material may be lost, especially in aeronautical applications, whereas near-net-shape AM reduces material use and associated impacts from extraction, transport, and disposal. However, a full assessment must be systemic and life cycle-based. Printable feedstocks, particularly metal powders produced by atomization, are highly energy intensive and can dominate the environmental burden, even when build-phase energy and material use are comparatively low [7,10,207].

Life cycle assessment (LCA) remains the standard tool for evaluating AM across stages from raw material to end-of-life [239]. Results vary by sector and application. In aeronautics, metal AM can cut the total environmental impact by >60%, largely due to material efficiency and lightweighting that reduces fuel consumption during use. By contrast, for some industrial machinery parts, AM can show higher overall burdens than conventional routes because of high energy demand and intensive material preparation [7,10,207]. Comparative studies highlight that process choice is critical: for a steel wall, CNC milling was found to be environmentally preferable to both WAAM and PBF, with raw material dominating impacts in WAAM and milling and build-phase electricity dominating in L-PBF [36]. Geometry is similarly decisive: AM becomes more favorable as the material removal ratio in conventional machining increases, and design strategies that reduce mass and supports shift the break-even point toward higher production volumes [7,10].

A complete cradle-to-grave analysis is essential to accurately assess AM’s environmental impact. Many studies overlook critical stages such as feedstock production or post-processing, leading to underestimated footprints. Advancing both environmental and economic sustainability will require continued innovation in AM materials, design tools, and processing techniques. Eliminating support structures, accelerating build times, and adopting low-impact feedstocks are particularly important for making AM viable at larger manufacturing scales.

Importantly, environmental and economic gains can align: reducing energy demand, minimizing waste, and improving material efficiency lower both emissions and production costs. Comprehensive assessments should therefore track indicators such as life cycle energy consumption, CO₂ emissions, and toxicity impacts to support informed, sustainable adoption of AM [7,10]. Figure 9 summarizes the main pathways through which AM influences environmental performance. Life cycle assessment provides system-wide visibility from feedstock to end-of-life. Energy analysis and process models quantify build-phase demand and guide parameter optimization. Environment-oriented practices (e.g., material reuse and cleaner post-processing) and eco-design strategies (e.g., mass reduction and support minimization) translate into measurable reductions in emissions and waste. Together, these pillars offer a practical roadmap for improving the sustainability of AM.

A cradle-to-grave LCA of ultra-tall wind turbine towers (140 m hub height, 7.5 MW) compared a tubular steel tower with three AM-enabled concrete alternatives: on-site 3D-printed concrete (35 MPa and 78 MPa) and a 3D-cast tower using printed formwork [57]. Using TRACI indicators, the 35 MPa on-site 3D-printed tower showed 23% lower life cycle CO₂ than steel and reduced smog and acidification but required 29% more energy. Material production dominated across all designs, contributing >92% of the total CO₂ and 67–93% of the energy use, highlighting the importance of mix design. Cement content was critical: the 78 MPa mix, with 33% more cement, had a 16% higher GWP than the steel tower, yet a 13% cement reduction would push its CO₂ below steel. On-site printing mattered too: the 35 MPa printed tower reduced CO₂ by 25% relative to the 3D-cast option due to avoided transport.

AM does not automatically guarantee low impact. PBF can generate up to 44% material waste, and the carbon footprint of some AM routes can be 2–20× higher per kg processed than conventional methods when feedstock and energy are fully counted. In laser-based metal AM, only 9–23% of laser energy is effectively used for melting, and just 0.8% of the total system energy is spent on actual powder melting [60]. In construction, digital fabrication can nearly double impacts compared to conventional methods, reaching 65–120 kg CO₂-eq/m³ versus 20–40 kg CO₂-eq/m³. Transportation benefits from AM are often modest unless combined with significant weight reduction and system-level redesign; raw materials still need to be shipped, so logistics gains alone are usually small [60,240].

Process-level optimization links directly to carbon and material efficiency. A multi-objective DED optimization framework minimized total carbon emissions, CT, maximized powder utilization, η, and controlled cladding quality via aspect ratio, φ (target φ ≈ 6 for a 2 mm spot), using laser power P, scan speed V_s, and powder feeding rate V_f as decision variables [241]. An improved evolutionary algorithm and entropy–gray relational selection produced parameter sets that reduced carbon emissions by 25%, increased powder utilization by 19%, and improved aspect ratio by about 6%. Surface quality and durability also improved: roughness dropped from R_a = 3.952 μm to 2.965 μm and wear depth from 59.77 μm to 44 μm, and porosity and cracking were reduced [241]. A dynamic carbon footprint model for L-DED decomposed the process into effective processing, rapid moving, and transformed delay and tracked energy and materials across five subsystems [242]. The total footprint ranged from 3624.68 g to 15,759.08 g CO₂, with about 55% from effective processing, 40% from transformed delay, and 5% from rapid moves, meaning non-effective states contributed ~45% of emissions. The powder-supply subsystem dominated at ≈68% of emissions, followed by cooling, laser, and CNC (each ~10–11%). Parameter sensitivity ranked scan speed V_s as most influential, then P, then V_f, pointing to clear levers: raise powder and gas reuse and reduce non-productive time.

For ceramics, a rapid, energy-efficient route has demonstrated extremely low footprints. A reactive-preform AM process (REAP) consolidates parts via a self-sustaining reaction, needing only <10 s at 192 W (≈1920 J) for ignition and no further furnace time [223]. The estimated footprint is 2.23 kg CO₂e/kg of ceramic, compared to 3086.13 kg CO₂/kg for typical binder-jetting and 616.40 kg CO₂/kg for stereolithography—reductions of roughly ~1000× and ~275×, respectively. The key is eliminating prolonged high-temperature pyrolysis and sintering; energy input is nearly size-independent once ignition occurs, supporting scalable decarbonization of complex ceramic components [223].

System-level strategies determine whether AM supports sustainable manufacturing. In digital construction, for example, 60–70% material savings may be needed to offset higher process impacts [60,240]. AM can also enable process consolidation and novel sustainable materials (e.g., solar-sintered sand, binder-jetted bio-composites) and recycling/circular strategies, particularly with compostable or multi-material prints. To improve sustainability, system-level DfAM, higher machine and process efficiency, renewable electricity, and more efficient subsystems (motors, chillers) are essential. LCAs must evaluate impacts per part, not only per kg, and include pre-/post-processing and utilization. Training designers to weigh sustainability trade-offs, using data-driven design tools, and expanding sustainable material options are all needed; social aspects such as worker safety and fair labor must also be addressed while the technology is still evolving [60,240,243].

Multi-criteria decision tools help integrate sustainability into material and process choices. A multi-criteria framework compared FDM (Fused Deposition Modeling), SLS (Selective Laser Sintering), and SLA (Stereolithography) using four MCDM (Multi-Criteria Decision Making) methods such as SAW (Simple Additive Weighting), MOORA (Multi-Objective Optimization based on Ratio Analysis), TOPSIS (Technique for order performance by similarity to ideal solution), and VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) with entropy-based weights [244]. It identified TPU Elastomer (FDM), Accura HPC (SLA), and Duraform EX (SLS) as top choices and showed that SAW and VIKOR produced stable rankings, while MOORA and TOPSIS exhibited rank reversal. The approach reduces decision noise and aligns technical performance with indicators like energy use and global warming potential, supporting cleaner, more consistent material selection [244].

At the system and network level, integrating collection, recycling, and AM into a single circular resource management (CRM) chain can sharply reduce impacts [59]. When the number of local recycling facilities increases, average transport distance falls as 1/√n, and a benchmark scenario showed 25× lower transport costs and similar CO₂ reductions with localized manufacturing. Using recycled instead of primary plastics saves up to 88% of energy (MWh/ton), particularly for HDPE and PP. Combined with shorter transport legs and on-demand AM, this yields compounded reductions in energy use and emissions and moves AM toward near-zero-waste, cradle-to-cradle flows [59]. At the material level, mechanically recycled PMMA can be reused for at least six thermo-mechanical cycles while maintaining printability and stable thermal behavior [64]. Producing 1 kg of recycled PMMA filament emits about 0.35 kg CO₂, versus roughly 5.5 kg CO₂/kg for pristine PMMA. Energy savings of 66–80% per ton and near-100% landfill diversion are reported when scrap is fully recirculated. Mid-cycle quality improves as well: by the fourth cycle, dimensional deviation and porosity fall markedly, and in the second cycle, tensile strength rises by 9.2% and flexural strength by 5.2%, supporting robust parts from recycled inputs [64].

Product-level frameworks show how AM can outperform conventional routes across multiple sustainability pillars. A life cycle sustainability framework for biomedical scaffolds compared FDM with freeze drying and produced a composite index over economic, environmental, and social dimensions [48]. The additive route scored a Sustainability Index of 0.9271 versus 0.0136 for the traditional route. Unit life cycle cost fell by 20% (USD 10.8 vs. USD 13.0), lead time dropped from 6 to 3 days, and the traditional route’s production rate was 24.24% lower. Fabrication-phase energy demand was about 11× higher for freeze drying, and environmental impacts were dominated by electricity, sterilization, and material processing. Social indicators favored AM for worker health and safety and customer satisfaction; the traditional route scored higher only on job satisfaction due to familiarity with conventional practice [48].

Functionally equivalent comparisons clarify when metal AM delivers climate benefits. For WAAM steel beams versus hot-rolled I-beams, topology-optimized WAAM beams were 53% lighter while carrying the same load, giving a 2.13× higher capacity-to-mass ratio and 7% (carbon steel) to 24% (stainless steel) lower climate impact; WAAM outperformed hot rolling when ≈50% weight reduction was achieved [245]. Printing contributed 50% of the impact for carbon steel and 32% for stainless, with shielding gas exceeding welding electricity. Increasing the deposition rate from 2 to 5 kg/h cut the WAAM process share of climate impact by 22%, and switching to fully renewable electricity reduced it by >30% [245]. For LDD remanufacturing of turbine blades, restoring only 10% of blade volume improved the carbon footprint by ≥45% and saved 36% total energy relative to casting a new blade; the energy balance remained favorable as long as the repair volume stayed below 18% [246].

Rapid assessment and hotspot analyses support fast, sustainability-aware design decisions. A rapid LCA model for L-PBF pump impellers using a BAG–XGBoost predictor achieved R² = 0.991 and MSE = 0.71, ranking carbon drivers as part volume, material type, and Z-height; shape complexity had a minor influence [100]. Median cradle-to-gate emissions were around 40 kg CO₂-eq for Ti-6Al-4V, 30 kg for 316L, and 20 kg for AlSi10Mg, guiding designers toward lower volume, lower Z-height, and lower-impact alloys [100]. Ecological hotspot analysis of L-PBF repair found raw material extraction, powder production, and post-processing as dominant contributors, while in-process melting had a relatively low share; reducing powder volume, build height, and post-processing effort were the most effective levers [61]. A multi-criteria comparison of L-PBF and CNC machining for Ti-6Al-4V showed that without weight reduction, machining led with a sustainability score of 0.778 vs. 0.696 for L-PBF; with a 20% weight saving enabled by AM, L-PBF rose to 0.767 vs. 0.714 for machining. Material utilization (0.80 for AM vs. 0.25 for machining), production speed, and labor cost were dominant drivers [247].

Feedstock pathways and reuse strategies are crucial where powder dominates the footprint. A multi-stage ball-milling route converts Ti-6Al-4V swarf into near-spherical AM powder (40–200 µm) without hydrogenation–dehydrogenation [63]. Compared to gas atomization, ball milling cuts the gross energy requirement by ~59%, global warming potential by ~68%, and eco-cost by ~82%. Recently, non-spherical powder produced via hydride–dehydride processing has been used in L-PBF to achieve a final relative density of 99.9%, with a fatigue performance comparable to that of gas-atomized Ti-6Al-4V powders [248,249,250,251,252,253,254]. Machining typically generates ~55% swarf from total Ti input, and melt-based recycling can lose 15–25% of metal; directly upgrading swarf to powder recovers this material and avoids re-melting. The recycled powder yields L-PBF single tracks with 4–7% higher hardness than those from commercial atomized powder and exhibits good flow and spreadability [63]. Powder reuse in Directed Laser Deposition for Ti-6Al-4V ELI has also been shown to cut impacts across all 18 ReCiPe Midpoint categories by 15–25% relative to a virgin-powder route, with powder production contributing 89% of impacts with reuse and 95% without [62]. After three reuse cycles, Hausner ratios remained <1.100 (excellent flow), porosity in built coupons fell by >80%, and chemistry stayed within specification apart from nitrogen approaching its upper limit, underscoring the need for routine compositional checks. These results provide quantitative thresholds (15–25% impact reduction and 89–95% hotspot share in powder production) that make a strong case for controlled powder reuse as a core sustainability strategy in metal AM [62].

In a study carried out on industrial gas atomization, a gate-to-gate LCA was used to quantify the environmental burdens of metal powder production for AM. The functional unit was 1 kg of powder in the 10–53 µm range suitable for PBF, with measurements taken on production systems using the German electricity mix. Global warming potential (GWP) and Cumulative Energy Demand (CED) were reported alongside process yields. The choice of inert gas dominated impacts: argon carried a GWP about six times and a CED about nine times higher than nitrogen, raising GWP from roughly 4.61 kg CO₂e/kg to 16.71 kg CO₂e/kg when substituted. Gas preheating reduced the total gas required by about one-third, and the energy used for heating was more than offset by lower gas consumption. Under a 100% renewable electricity scenario, GWP fell by about 86% and CED by about 56%, showing strong sensitivity to the energy mix. Process selection further shaped results through yield. Closed-Coupled Atomization (CCA) delivered a higher fraction of AM-usable powder (~40%) than Free-Fall Atomization (FFA) (~25%). Although CCA used more batch energy, its higher yield lowered impacts per functional unit: CCA with preheated nitrogen achieved 4.61 kg CO₂e/kg (GWP) and 77.94 MJ/kg (CED), while FFA with preheated nitrogen reached 5.58 kg CO₂e/kg and 94.09 MJ/kg. The study concluded that environmental performance improves by prioritizing nitrogen over argon, preheating and recirculating gas, maximizing usable yield, and sourcing low-carbon electricity. These levers act without compromising the particle-size window required for AM feedstocks [255]. Figure 10 illustrates that CCA achieves lower GWP and CED per kilogram of usable powder than FFA, emphasizing gas handling and process yield as critical levers.

An increasingly relevant example of AM’s role in broader manufacturing is its use as an enabler for hybrid processes such as rapid investment casting. In these approaches, AM (often through binder jetting or vat photopolymerization) is used to produce complex patterns or molds that replace traditional tooling. Recent work has shown that integrating topology optimization with AM-supported investment casting for an automotive steering knuckle reduced part mass by about 30%, cut resin consumption and print time, and achieved roughly a 27% reduction in manufacturing environmental impact and a 31% reduction over the component life cycle compared with conventional casting workflows. Such AM-assisted casting routes can combine material and energy savings with improved geometry control and demonstrate another avenue where AM contributes to economic and sustainability improvements beyond direct part fabrication [256].

4. Future Prospects

The future of AM is closely linked to its integration with artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and digital twin (DT) technologies. These tools are expected to enhance process control, improve part quality, and enable real-time monitoring and predictive maintenance through sensor-rich, connected systems [19,20,21,22,49,51,257,258,259,260]. AI algorithms can optimize build parameters to reduce defects [261] and material waste, while IoT-enabled sensors supply the data needed for condition monitoring and process optimization. Hybrid manufacturing, which combines AM with conventional machining, is also gaining traction: complex geometries are additively built and then finished by subtractive methods to achieve tight tolerances and surface quality, particularly in aerospace and automotive applications [262].

AM is evolving toward smart manufacturing systems in which AI-driven DTs simulate, monitor, and control processes in real time. Concepts such as intelligent Wire Arc AM (IWAAM) envisage high levels of autonomy and adaptability, but coupling AI with DT remains challenging. Future systems aim to use AI-enabled DTs for online prediction, parameter optimization, and digital part qualification, which will require advances in standardization, data interfaces, and model simplification for real-time deployment [49,51,260].

AI and ML are already being applied to closed-loop control, in situ monitoring, and defect detection, where they process complex multi-sensor data streams to stabilize deposition and enable rapid correction. Offline models based on deep learning and ML are improving geometry compensation, defect classification, and defect remediation, moving AM toward more automated and efficient quality assurance. Design optimization for repair and remanufacturing is an emerging area, with hybrid AI methods (e.g., evolutionary algorithms combined with neural networks) proposed to improve restoration accuracy, efficiency, and failure-mode analysis before repair [19,20,21,22,257,258,259]. ML is expected to play a central role within the Industry 4.0/5.0 framework, enabling smarter, more connected AM systems that optimize designs, monitor processes in real time, ensure quality, and accelerate material innovation. Key application areas include data-driven DfAM, in-process optimization and control, automated defect evaluation, and faster material development via property prediction from composition and process conditions. ML is also seen as a lever for sustainability by optimizing energy usage, reducing cost, and supporting recycling and remanufacturing. Realizing this vision depends on overcoming several bottlenecks: the need for large, high-quality datasets; high computational cost and latency for real-time prediction (driving interest in cloud–edge collaboration); the lack of standardized frameworks and interfaces linking AI, DTs, and AM hardware; limited interpretability of ML models for engineering use; and concerns around data security and intellectual property. Future work is expected to focus on advanced algorithms, data fusion, physics-informed ML, transfer learning, and collaborative learning, as well as practical, validated industrial case studies. As these challenges are addressed, AI-driven AM systems are likely to make manufacturing more autonomous, flexible, and sustainable, enhancing production capability while reducing cost and environmental impact.

Figure 11 summarizes the main future directions of additive manufacturing at a critical transition point. It highlights three linked pillars—intelligent systems and digitalization, advanced production and materials, and sustainable industrial scaling—each supported by key enablers but limited by challenges such as data quality and availability, model interpretability, workflow integration, and IP concerns. Together, these trends aim to reduce defects, enable real-time monitoring and prediction, accelerate process and material development, and drive broader industrial adoption with improved sustainability.

5. Conclusions

Additive manufacturing currently stands at a realistic crossroads. Practical limits still slow wider industrial adoption: scalability varies across processes and materials, qualified material options are narrower than in conventional routes, and property scatter can occur across builds, machines, and sites. Costs remain high when utilization is low, post-processing dominates schedules, or feedstocks carry high embodied energy. Moreover, integrating AM into existing workflows still requires specialized expertise in design, monitoring, and qualification.

Progress is, however, evident along three main fronts:

• Process monitoring and data: In situ sensing and closed-loop control are reducing uncertainty at the layer scale, stabilizing melt pools, bead height, and porosity. Data pipelines increasingly link these signals to machine settings and part records, while machine learning shortens the parameter search, detects off-nominal behavior, and predicts defects before they form.
• Hybrid manufacturing: Near-net additive manufacturing combined with targeted machining is maturing, allowing tight tolerances and surface quality without sacrificing geometric freedom.
• Standards and qualification: Shared definitions of build time, utilization, and energy allocation, along with common test artifacts, are improving comparability across sites. Digital evidence may accelerate qualification when linked with a clear digital thread connecting design, parameters, monitoring, and inspection.

Looking ahead, a pragmatic path involves designing to minimize mass, height, and supports where possible, selecting processes based on geometry and finishing requirements, modeling costs across the full workflow, measuring energy and emissions consistently, planning capacity to maximize machine utilization, and developing skills across materials, design for AM, process control, metrology, and data management.

If these steps are followed, AM will become more predictable, less costly, and easier to certify, serving as a complement to machining, casting, and forming rather than a wholesale replacement. Otherwise, it may remain confined to niche applications. The next decade will largely be defined by how well design practice, process control, feedstocks, and standards are aligned in everyday industrial use.