Comparative Life Cycle Assessment of Topology Optimization and Generative Design for Sustainable Additively Manufactured Furniture

Abstract

Sustainability is an increasingly important objective in design and engineering, yet the environmental implications of advanced computational design methods remain insufficiently quantified. This study examines the contribution of topology optimization to sustainable product development when applied exclusively to a product’s internal structure, while preserving external geometry, mechanical performance, and design intent. The furniture sector was selected as a representative case due to its significant environmental footprint and the strong role of aesthetic requirements within the design methodology. A gate-to-gate Life Cycle Assessment was performed to compare a conventionally designed stool with an internally optimized counterpart, both developed under the same design constraints and manufactured via Fused Deposition Modeling using Carbon Fiber-reinforced PETG (CF-PETG). The results indicate that computational strategies can reduce material waste by 57.8% to 90% compared to traditional subtractive methods. However, these benefits may be partially offset by increased energy demand during additive manufacturing due to geometric complexity. An additional comparative assessment involving generative design demonstrates that alternative computational strategies can achieve more balanced trade-offs between material efficiency and manufacturing energy, supporting sustainability while respecting design methodology constraints.

1. Introduction

Sustainability has become a systemic imperative in both design and engineering, as decisions taken in early stages have a decisive influence on a product’s environmental footprint [1]. The furniture industry accounts for approximately 1% of total global carbon emissions [2], while traditional woodworking can lose up to 30% of raw material as waste [3]. In addition to aesthetics and functionality, contemporary design practice increasingly incorporates ecological and social criteria, supported by frameworks such as Eco-Design, Cradle-to-Cradle, and Circular Economy [4]. Within this evolving context, computational tools offer opportunities to embed sustainability directly into the design process by optimizing material use, improving structural performance, and reducing environmental impact.

Topology Optimization (TO) has been widely applied in high-performance engineering sectors such as aerospace and automotive, where its ability to reduce mass and maximize stiffness has been thoroughly demonstrated [5]. Yet, its adoption in product and furniture design has been limited. The majority of studies frame TO from a primarily engineering perspective, prioritizing structural efficiency and material reduction. Although such studies reveal substantial performance gains, they offer limited consideration of aesthetics, ergonomics, or user perception, which are central dimensions in design-led disciplines [5,6].

This limited adoption is primarily due to manufacturing and fabrication constraints. TO outputs often present geometries that are impractical to fabricate with conventional methods such as CNC or casting, due to thin struts, overhangs, or complex internal cavities [7,8]. Additive Manufacturing (AM) enables the realization of these forms, reducing material waste compared to subtractive approaches, but it may introduce higher energy demand and carbon emissions [9].

AM also introduces process-related factors that affect environmental performance and need to be considered during the design stage. In extrusion-based processes such as FDM, environmental impact depends on both material selection and geometry. Parameters such as build time, support structures, and toolpath characteristics can vary significantly between designs, even when mass or stiffness is similar, leading to differences in energy consumption and emissions [10]. This suggests that AM should not be considered only as a downstream manufacturing step, but as a design variable that interacts with geometry and affects sustainability outcomes [11]. As a result, claims of sustainability in computational design should be evaluated by considering both material efficiency and process-related impacts.

The carbon footprint of traditional wooden seating typically ranges from 10 to 30 kg CO2-eq per unit [12], highlighting the need for more efficient manufacturing strategies [13]. Furthermore, while TO is frequently assumed to be ‘sustainable’ based on reduced material consumption, empirical validation through Life Cycle Assessment (LCA) is often missing in design-led contexts. Although the integration of TO and environmental assessment has been explored in structural engineering and aerospace [14,15], such studies remain limited in furniture design and product development. In these fields, the environmental benefits of TO, particularly when coupled with AM, are often stated intuitively rather than quantified through rigorous LCA frameworks [11,16].

Recent developments in design research explore the integration of computational tools into early design stages to address performance and manufacturability requirements. Generative Design (GD) allows the generation of multiple design alternatives that combine structural efficiency with manufacturability [17]. However, aesthetic and ergonomic considerations are often not explicitly integrated, and instead tend to emerge as a by-product of the optimization process. This can be observed in early experimental and industrial cases, such as Laarman’s Bone Chair (2006) [18] or Starck’s A.I. Chair (2019) [19], where the resulting forms exhibit complex, organic and web-like geometries, consistent with those typically generated by TO. These forms are largely driven by exploratory, designer-led processes, with limited integration of aesthetic, ergonomic, or sustainability-related considerations. Some recent approaches incorporate ergonomic constraints and human-centered considerations into computational design workflows [20]; however, they still limit the designer’s control over the resulting geometry.

The reviewed studies indicate that TO is still primarily applied as a downstream engineering tool, resulting in limited consideration of user-related factors and a reliance on performance-driven criteria [5,6]. Consequently, environmental performance is often assumed rather than measured, and LCA integration into conceptual design remains scarce.

This study addresses these limitations by positioning TO within a design-led framework. This research adopts an explorative design approach where TO is used to generate internal structural variants while maintaining a fixed external aesthetic. To validate the sustainability of these forms, a manufacturing scenario was established where the optimized internal geometries were produced through AM using PETG-CF10 (Flashforge Technology Co., Ltd., Jinhua, China) and subsequently finished with wood veneer. This allows for a rigorous LCA based on real fabrication data from FlashPrint (FlashForge, Jinhua, China) without compromising the initial design intent. The wooden stool was chosen as a representative archetype because its volume provides a significant internal ‘design space’ for material reduction while maintaining a constant external silhouette.

The method is applied to the internal structure of a wooden stool, preserving the external geometry and maintaining the designer’s intent while incorporating manufacturability constraints. A gate-to-gate LCA is conducted to evaluate the integrated design–material–process shift between conventional and optimized manufacturing routes. This enables the evaluation of environmental performance based on measurable indicators while acknowledging that sustainability outcomes stem from the synergy of structural redesign, material substitution, and AM parameters. In addition, GD is explored as a complementary approach to examine the relationship between material reduction and energy consumption. This approach demonstrates how structural optimization can be integrated with design intent and environmental evaluation within a unified methodology.

2. Methodology

This study presents a framework that integrates topology optimization (TO) using Autodesk Fusion 360 v.2.0.21.47 (Autodesk Inc., San Francisco, CA, USA) as a design tool guided by functional, aesthetic, and sustainability criteria. Rather than allowing optimization algorithms to redefine the overall product morphology, TO is applied exclusively to the internal structure of the stool legs. The external geometry, interface dimensions, and visual characteristics are deliberately preserved, ensuring that the designer’s original intent and user-centered requirements remain unchanged. This internalized application of TO represents a key methodological innovation, as it decouples structural optimization from formal expression while still enabling substantial material redistribution along load-bearing paths.

Manufacturability is integrated as a core constraint by incorporating AM requirements, such as print orientation, minimum feature size, and self-supporting angles, into the optimization workflow [21,22]. This approach ensures that geometries are structurally efficient and ready for fabrication, eliminating the need for extensive manual correction or post-processing [23].

To objectively evaluate the environmental implications of this approach, a gate-to-gate LCA is integrated into the methodology as a decision-support tool. A gate-to-gate approach was chosen to focus on the manufacturing phase, which is frequently identified as a critical environmental hotspot in industrial furniture production due to high energy demand and waste generation [24,25]. The LCA conducted in this study followed the ISO 14040:2006 [26] and ISO 14044:2006 [27] standards (International Organization for Standardization, Geneva, Switzerland), encompassing the established phases of inventory analysis, impact assessment, and interpretation. Each case study was documented using a consistent methodological structure to ensure transparency and comparability. The assessment focused on evaluating the environmental performance of the baseline scenario, which served as the reference case for subsequent comparisons with internally optimized alternatives.

This scope allows for the collection of primary, high-fidelity data directly from the specific woodcraft workshop and AM facilities involved in this study, ensuring that the environmental metrics are grounded in actual workshop conditions rather than generic database averages.

This framework was utilized to perform a quantitative comparison between a conventionally manufactured red oak stool and digitally optimized alternatives produced through AM. Consistent functional units and midpoint impact indicators are applied across all scenarios, allowing the effects of design strategy and geometry to be isolated from manufacturing process variability. However, it is acknowledged that this boundary excludes the upstream embodied energy of raw materials. Given that the production of CF-PETG filament is significantly more energy-intensive than the harvesting of natural red oak [28,29], these results focus strictly on the comparative efficiency of the manufacturing process. This integration of LCA moves beyond qualitative sustainability claims and grounds design decisions in measurable performance metrics, while serving as a baseline for future research utilizing a broader cradle-to-grave boundary.

Generative Design (GD) (Autodesk Inc., San Francisco, CA, USA) complements TO by enabling a broader exploration of the design space. While TO converges on a single solution, GD evaluates multiple viable outcomes based on structural performance, manufacturing constraints, and material efficiency. This approach allows for a systematic comparison of geometry-driven differences in print duration and energy consumption, addressing the energy limitations of the TO scenario and highlighting geometry as a critical lever for sustainable AM.

By integrating TO, GD and LCA, the proposed methodology demonstrates how computational tools and designer-driven values can co-exist within a coherent decision-making framework. The approach advances current practice by treating sustainability not as a downstream validation step, but as an active design variable alongside structural efficiency, manufacturability, and user experience.

3. Case Study Analysis

3.1. Case Study 1: Baseline Scenario

The baseline scenario corresponds to a conventionally manufactured wooden stool defined through an approved client drawing, which specifies the external geometry, dimensions, and constructional features (Figure 1). To facilitate visual comparison with the computationally optimized designs, a three-dimensional CAD visualization of the conventional wooden stool is presented in Figure 2.

3.1.1. Materials of the Baseline Scenario

The baseline scenario consisted of four cylindrical solid red oak legs, each with a diameter of 60 mm and a height of 170 mm, finished with a lacquer coating (W01.1). The upholstered seat measured 370 mm in diameter and 300 mm in height, and was covered in fabric (NAMOS L1758, color UNI 014). The use of solid hardwood for the legs was a key factor in selecting this case, as it provides sufficient material volume for meaningful application of TO.

Several secondary materials were excluded from the detailed analysis as they remained constant across all scenarios. Specifically, the upholstery fabric and seat components were omitted from the comparison since no optimization was applied to these elements, ensuring that the study focused exclusively on the structural differences of the legs. The polyurethane adhesive (PU 501 Express) used for seat assembly was included in the material inventory but not analyzed separately, since it was applied in negligible quantities, had zero VOC emissions (according to Safety Data Sheets (SDS)), and remained constant across scenarios. Plastic gliders were also excluded due to their marginal contribution. By contrast, the lacquer coating (W01.1) was included in the baseline assessment, as it contributes directly to VOC emissions and energy demand.

3.1.2. Manufacturing Process of the Baseline Scenario

The baseline scenario was produced through a sequence of conventional woodworking and finishing operations, carried out in a small-scale workshop in Athens, Greece. The process included plank cutting, planning, thickness calibration, length and width division, end trimming, turning, assembly with polyurethane adhesive and screws, sanding, and final lacquering. The complete workflow is illustrated in Figure 3, which summarizes the order of operations and the main machines involved.

3.1.3. Life Cycle Assessment

Primary inventory data were collected on-site through direct observation of the manufacturing sequence. Electricity consumption was estimated using nominal power ratings provided by the machine tool manufacturers and measured operating times recorded manually with a stopwatch. Nominal power values were taken directly from the technical nameplates of the equipment. Because machines in small-scale, artisanal workshops do not operate continuously at full load, an uncertainty range of ±10–15% was applied to account for differences between nominal and actual power demand, as well as additional energy use during machine start-up and idling periods [30].

Material losses were quantified volumetrically per operation, assuming a red oak density of 0.75 g/cm³. Material waste is defined as the total mass loss originating from the initial raw plank (gross input) through all subtractive operations required to achieve the final net geometry of the stool legs. For manual processes without extraction (e.g., miter sawing), a 5% airborne dust loss was assumed, based on Environmental Product Declaration (EPD) standards for non-extracted woodworking. Subcontracted processes were documented through structured interviews, including lathe turning on a ZMM Inc. Sofia CU582 (46 min per pair). VOC emissions were quantified via SDS for the adhesives (0 g/L) and lacquers used.

Environmental burdens were assessed using midpoint indicators, including:

• Global Warming Potential (GWP) (kg CO₂e), based on electricity use and the Greek grid emission factor of 0.265 kg CO₂/kWh, reflecting the 2024–2025 verified average for the Greek energy mix [31].
• VOC emissions (g), derived from adhesive and lacquer application.
• Material waste (kg), generated during machining, turning, and sanding
• Energy consumption (kWh), reflecting the total electricity demand of all processes.

The detailed impact results per manufacturing process are shown in

Table 1. LCI and process-level environmental impacts for the baseline scenario (4 legs).

Process	Energy (kWh)	CO2e (kg)	Material Waste (g)	VOC Emissions (g)
Initial Cutting	0.05	0.01325	0 negligible	0
Length Reduction	0.15	0.03975	6143 landfills	0
First Planing	0.0292	0.00774	439 recycled	0
First Thicknessing	0.00833	0.00221	439 recycled	0
Thicknessing—Width Calibration	0.0167	0.00443	439 recycled	0
Secondary Cutting	0.0333	0.00883	0	0
Miter Sawing	0.0417	0.01105	878 landfills	0
Gluing	0	0	0	0
Screwing	0.00139	0.00037	0	0
Second Planing	0.0582	0.01542	236 recycled	0
Second Thicknessing	0.0167	0.00443	236 recycled	0
Lathe Turning	6.12	1.62180	312 landfills	0
Final Cutting	0.0111	0.00294	5332 kept/reuse	0
Sanding	0.00291	0.00077	196 landfills	0
Lacquering	2	0.53000	0	60
Total	8.53953	2.26298	14,650	60

. Life Cycle Inventory (LCI) is the phase of LCA in which all relevant input and output flows of a system are systematically quantified, including energy use, CO₂e emissions, material waste, and volatile organic compound (VOC) emissions throughout the manufacturing process within defined system boundaries. The resulting inventory dataset provides the quantitative basis for subsequent impact assessment and interpretation.

For clarity, aggregated values across all assessment categories are reported in

Table 2. Summary of aggregated environmental impacts for the baseline scenario.

Impact Category	Value	Main Sources
Global Warming Potential	2.26 kg CO2e	Electricity consumption, lacquering
Energy Consumption	8.54 kWh	Lathe turning, lacquering
Material Waste	~14.65 kg Total cumulative loss	Sawing, turning, sanding
VOC Emissions	~60 g	Lacquering (3 coats)
Material Use	~20.18 kg Raw Plank/Gross Input	(230 × 78 × 1500 mm segment; ρ ≈ 0.75 g/cm3)

.

The environmental assessment of the baseline scenario indicates that lathe turning and lacquering are the dominant contributors to both energy consumption and associated CO₂e emissions. Among the examined processes, lacquering is identified as the sole source of VOC emissions, while material waste is primarily generated during sawing and turning operations. These findings highlight the environmental hotspots of the conventional woodworking process and establish a quantitative reference against which the environmental performance of the optimized design scenarios is assessed.

3.2. Case Study 2: Topology TO Scenario

3.2.1. Materials of the TO Scenario

The TO scenario was constructed using a hybrid leg design consisting of the following.

Inner core: Carbon-fiber reinforced polyethylene terephthalate glycol (CF-PETG), processed via FDM. Since AM with FDM was selected as the production method, CF-PETG was identified as a suitable material due to its proven printability and compatibility with this process. The fabrication was performed using standardized settings to ensure structural consistency, including a 0.2 mm layer height, 20% triangular infill, and a print speed of 300 mm/s; a comprehensive summary of all FDM parameters is provided in in Section 3.2.4. In addition, it offers a favorable balance of mechanical properties (Young’s modulus 3.5–5.5 GPa; tensile strength > 40 MPa) [32] and recyclability through reprocessing [33], providing structural performance comparable to red oak while enabling material recovery. It should be noted that while the TO was performed assuming isotropic material behavior, FDM-produced parts are inherently anisotropic due to layer-wise deposition. However, the high tensile strength of CF-PETG (>40 MPa) and the conservative loading conditions provide a structural safety margin that accommodates the expected lower interlaminar (Z-axis) strength typical of the process.

Outer surface: 2 mm red oak veneer, bonded to the printed core with contact adhesive. Veneer provides the natural appearance and tactile quality of wood, ensuring visual continuity with the baseline scenario. Veneers were chosen for their material efficiency and low environmental footprint compared to solid hardwood [34].

Adhesives and coatings: A polyurethane-based contact adhesive (same as in the baseline case, PU 501 Express) was applied to secure the prefinished veneer to the printed legs. According to the manufacturer’s safety data sheet, the adhesive is classified as low-VOC, and given the small quantities applied, its contribution to overall emissions was considered negligible compared to lacquer. The use of prefinished red oak veneer eliminated the need for additional lacquering at the workshop, resulting in zero VOC emissions within the defined gate-to-gate boundary. At cradle-to-gate level, some VOCs would still be associated with the industrial veneer finishing process, but these are typically lower than those from conventional solvent-based lacquers applied in small-scale workshops [35]. In contrast, the baseline scenario required three coats of lacquer (W01.1), which contributes approximately 60 g of VOC emissions.

Upholstery and gliders: Identical to the baseline scenario (NAMOS L1758 fabric cover; plastic gliders). These were excluded from comparative analysis since they remained unchanged across case studies and do not affect relative differences.

3.2.2. Manufacturing Process of the TO Scenario

The TO scenario legs were assumed to be produced via AM using FDM. To ensure that reliable production data could still be obtained, slicer (Flashforge FlashPrint (v. 5.8.0)) was employed as a digital manufacturing proxy. This choice was also informed by the baseline interpretation: since subtractive processes were identified as a major source of waste, AM was selected as a logical alternative to improve material efficiency.

Slicers generate detailed estimates of material consumption, support structures, build time, and energy demand based on slicing parameters, which closely approximate real-world AM performance. While slicer estimates are subject to a known uncertainty of ±10–20% due to machine-specific thermal dynamics [36], they are utilized here as a consistent digital proxy for comparative analysis. To ensure compliance with ISO 14044 guidelines regarding data quality, this uncertainty is further evaluated through a formal sensitivity analysis in Section 4.3. By extracting these data directly from the software, the study ensures that the LCI reflected realistic process values rather than purely theoretical assumptions.

3.2.3. Topology Optimization and Design for Additive Manufacturing

TO was performed in Autodesk Fusion 360 with the objective of minimizing material usage while maintaining structural stiffness under typical seating loads. To reduce computational cost, only one leg was simulated, exploiting the geometric and load symmetry of the four-leg stool. A static vertical force of 334 N was applied to the seat–leg interface, corresponding to one quarter of a 136 kg user, consistent with EN 1728:2012 [37] and ANSI/BIFMA X5.4-2020 [38] standards. The ground-contacting surface was fully constrained, replicating real boundary conditions.

The leg was modeled as a dual-body system consisting of: (i) an inner printable core subjected to optimization, and (ii) a preserved 5 mm outer shell to ensure veneer adhesion and contribute to stress distribution. Additional preserved features included a Ø10 mm through-hole for fastening. The optimization results demonstrated that material was efficiently redistributed along load paths, enabling a mass reduction of approximately 57.8% without compromising structural performance.

A concise overview of the model configuration and boundary conditions is provided in

Table 3. Summary of model set-up for TO of the stool leg.

Feature/Element	Function & Justification
Single-leg simulation	Due to symmetry, reduces computational cost by 75% without loss of fidelity.
334 N vertical force	1/4 of user weight (136 kg), consistent with EN 1728 and ANSI/BIFMA X5.4.
No gravitational field	Omitted to isolate vertical static load; avoids redundant loading.
Dual-body model	Separates printable core from preserved veneer surface.
Outer 5 mm shell	Enables veneer adhesion and contributes to stress distribution.
Central hole (Ø10 mm)	Preserved for mounting/mechanical fixation.
Bottom surface (Flat)	Fully fixed constraint simulating ground contact.

.

The optimization retains material along critical load paths and removed it from low-stress regions, producing geometric configurations inherently suited to AM. The resulting design achieves a potential mass reduction of approximately 57.8% while maintaining structural performance under the specified loading conditions. The TO was executed with a target volume fraction of 40%, focusing on maximizing stiffness while staying within the fabrication constraints of FDM. To ensure a valid LCA, the raw mesh was reconstructed in SolidWorks reconstructed in SolidWorks 2023 (Dassault Systèmes, Vélizy-Villacoublay, France) into a watertight solid body, eliminating non-manifold edges that would otherwise compromise the material and energy estimates in the slicing software. The optimization results are illustrated in Figure 4.

Post-processing was performed in CAD, where the raw mesh output (OBJ) was repaired, reconstructed, and converted into a watertight solid model suitable for slicing. Surface reconstruction and knitting operations were used to eliminate non-manifold edges and gaps, followed by solidification through the Thicken command. A parametric cylindrical shell was incorporated to represent the veneer-supporting region, ensuring the final design was both manufacturable and structurally consistent with the optimization assumptions. The final STL model was subsequently prepared for AM (Figure 5).

3.2.4. Slicing and Printing Parameters for the TO Scenario

The TO scenario legs were digitally prepared for AM using Flashforge FlashPrint (v.5.8.0). The geometry was oriented to rest flat on the build plate, minimizing support and warping risk, while ensuring stable adhesion during printing. The build volume of 350 × 350 × 600 mm, enabled all four legs to be produced in a single batch, reducing redundant energy cycles and ensuring dimensional consistency across parts.

Slicing parameters were tuned for Carbon Fiber-reinforced PETG (CF-PETG) based on the manufacturer’s recommendations and previous studies on FDM processing of CF-reinforced polymers [39,40]. The selected settings, 250 °C nozzle temperature, 80 °C bed temperature, 0.2 mm layer height, 20% triangular infill, three shells, and supports limited to the build plate, balanced structural strength and print fidelity while minimizing filament waste. The outputs of the slicing software are summarized in

Table 4. FDM Slicing and Fabrication Parameters.

Parameter	Value	Rationale/Source
Nozzle/Bed Temp	250 °C/80 °C	Optimized for CF-PETG flow and adhesion.
Layer Height	0.2 mm	Balance between resolution and structural strength.
Print Speed	300 mm/s	High-speed batch production (Guider 3 Ultra).
Infill Density	20% (Triangle)	Optimal load distribution with minimal mass.
Shell Count	3	Ensures surface integrity for stress distribution.

and depicted in Figure 6 (print screen from Flashforge FlashPrint (v.5.8.0). After printing, a 2 mm red oak veneer was applied to each leg using PU adhesive, preserving the tactile and visual identity of solid wood furniture.

3.2.5. LCA of the TO Scenario

The LCA of the TO scenario focused on evaluating the environmental performance of the stool legs redesigned through TO and manufactured using AM with PETG-CF10 filament. This study functions as a proof-of-concept, aiming to evaluate whether a reduction in environmental footprint is feasible while preserving the original design intent. This scenario was directly compared with the baseline case of conventionally manufactured red oak legs. The comparative LCA evaluates an integrated design–material–process shift rather than isolated geometry, accounting for the combined transition in material, manufacturing route, and structural design. The baseline scenario (solid red oak) assumes a ~30% material loss typical of CNC milling [41], whereas the optimized AM scenario (PETG-CF10 core + veneer) limited material waste to <10%, primarily consisting of build-plate supports. Due to the exploratory nature of the redesign, inventory data for the optimized AM scenario relied on high-fidelity slicer-based estimates for material consumption and energy use, providing a reliable predictive baseline for the comparative analysis. Inventory data for the baseline scenario were derived from measured workshop parameters. A gate-to-gate boundary was applied, covering the 3D printing process, electricity use, support waste, and application of a 2 mm oak veneer wrap for aesthetic continuity. Upstream filament production, transportation, use phase, and end-of-life scenarios were excluded. The functional unit was defined consistently as one stool comprising four legs.

Primary data were obtained from slicing outputs for a single build job of four stool legs on a 3D Printer. Inventory inputs included print time (36.6 h), filament consumption (1.51 kg PETG-CF10), and estimated support/brim waste (~121 g, ~8%). Average machine power draw (275 W) was applied to calculate energy use (10.07 kWh). The oak veneer mass was estimated at 0.246 kg for four legs, with corresponding CO₂e emissions of 0.133 kg CO₂e based on LCA database factors. VOC emissions were negligible, remaining below 0.15 g from material extrusion and zero from adhesive use.

Environmental burdens were assessed using the following midpoint indicators.

• Global Warming Potential: 2.8 kg CO₂e (2.67 kg from electricity, 0.13 kg from veneer).
• Energy Consumption: 10.07 kWh.
• Material Use: 1.51 kg PETG-CF10 + 0.246 kg oak veneer.
• Material Waste: ~121 g (supports and brim).
• VOC Emissions: <0.15 g.

The detailed results are summarized in

Table 5. Environmental impact results for the Topology-Optimized (TO) stool legs.

Impact Category	Value	Primary Contributors
Global Warming Potential	2.8 kg CO2e	Electricity use, veneer production
Energy Consumption	10.07 kWh	Printer power demand
Material Use	1.76 kg total	PETG-CF10 filament, oak veneer
Material Waste	~121 g	Support/brim material
VOC Emissions	<0.15 g	PETG-CF10 extrusion

.

It is acknowledged that the observed environmental differences stem from the synergy of material substitution, manufacturing technique, and structural optimization. Therefore, the results reflect the impact of a holistic system-level transition rather than the effect of geometry alone. This integrated comparison ensures that the sustainability assessment accounts for the practical realities of shifting from traditional woodworking to AM.

The environmental assessment of the TO scenario revealed clear benefits in terms of material efficiency and reduced VOC emissions but also highlighted that total energy consumption increased compared to the baseline scenario. This result demonstrates that, although TO is effective in achieving material savings, its application alone does not necessarily lead to improvements in the energy performance of the manufacturing process.

In light of this finding, it becomes necessary to further investigate alternative computational design approaches, such as GD. Unlike TO, GD allows for a broader exploration of design constraints and objectives, enabling the generation of alternative geometries that may influence manufacturing efficiency and associated energy demand. This additional analysis provides the opportunity to examine whether different geometry-driven strategies can lead to measurable differentiation in the environmental performance of additively manufactured components.

A detailed comparison of environmental results for both the baseline and TO scenarios will be further analyzed in Section 3.

3.3. Case Study 3: Generative Design Stool

3.3.1. Material

The GD stool was constructed using the same hybrid leg configuration as the topology-optimized version, to maintain methodological consistency and ensure that any observed differences in environmental performance could be analyzed within the context of the integrated design-material-process framework, ensuring that any variation stems from the structural configuration and its associated production requirements.

3.3.2. Manufacturing Process of the GD Scenario

The manufacturing process for the GD stool legs followed the same digital workflow as applied in the TO case, ensuring comparability of results and consistency in LCA data. The geometries generated in Fusion 360 were exported as watertight STL models and processed in Flashforge FlashPrint (v.5.8.0), which was used as a proxy for real AM. The decision to replicate the same slicing configuration and printer setup allowed the study to evaluate the environmental impact of the GD strategy as a whole. By adopting an identical manufacturing framework, the environmental performance of the GD legs can be directly evaluated against the topology-optimized scenario, acknowledging that the results reflect the synergy between geometry and the AM route.

3.3.3. Generative Design Workflow and Post-Processing

The design process was conducted in Autodesk Fusion 360 using the GD workspace. Unlike TO, which reduces material from an initial geometry, GD generates entirely new geometries based on constraints, preserved volumes, and load conditions.

The generative model was defined as follows.

• Preserved geometry:

  ○

  Two circular disks (Ø140 mm, 5 mm thick) representing the top and bottom interfaces with the seat and ground.

  ○

  A cylindrical outer shell (5 mm thick) reserved for oak veneer adhesion and structural contribution.

• Obstacle geometry:

  ○

  A central through-hole (Ø10 mm) reserved for fastening.

• Design space:

  ○

  The internal leg volume, left unassigned to allow the solver to generate optimal material placement.

• Boundary conditions:

  ○

  Vertical load of 334 N applied to the top preserved disk (¼ of a 136 kg user weight, consistent with EN 1728 and ANSI/BIFMA X5.4).

  ○

  Ground-contacting surface of the bottom disk fully constrained.

  ○

  Gravity enabled to account for self-weight.

• Manufacturing constraints:

  ○

  Additive (FDM, Z+ orientation).

  ○

  Maximum overhang angle: 45°.

  ○

  Minimum thickness: 3 mm.

The outcomes of the GD study are illustrated in Figure 7, which presents the two converged solutions within the Fusion 360 GD workspace. GD Outcome 1 (left) exhibits a truss-like, star-shaped internal structure that promotes efficient load transmission through triangulated ribs, while GD Outcome 2 (right) displays a smooth hemispherical cavity optimized for compressive load paths. Both geometries were generated under identical boundary conditions, thereby highlighting the algorithm’s ability to explore distinct yet mechanically valid structural strategies within the same design space.

A key advantage of the GD workflow is the manufacturing readiness of the exported geometries. Both outcomes were exported as. STL files and validated in CAD. Unlike the TO process, which produced meshes with numerous defects, the generative models were watertight and free of mesh errors.

This distinction underscores the parametric and simulation-ready nature of GD outputs, which are inherently cleaner than the raw triangular meshes typically produced by TO and often require post-processing before use in CAD or manufacturing workflows [42]. In CAD, both models were successfully converted into solid bodies without the need for manual surface repair or reconstruction. Sectional views were then generated to examine the internal geometry and confirm structural continuity. The convergence on these two specific outcomes is attributed to the stringent manufacturing and orientation constraints applied (FDM, Z+ orientation, and 45° overhang limits), which naturally filtered the solution space toward the most printable and structurally efficient configurations for the intended application.

Figure 8 and Figure 9 illustrate sectional mesh views of two GD solutions (Outcome 1 and Outcome 2), highlighting the robust and error-free geometry that ensures readiness for subsequent slicing.

3.3.4. Slicing Results of the GDs

To ensure full comparability between the computational strategies, the GD outcomes were processed using the identical manufacturing setup applied in the TO scenario. The printing parameters, including a 250 °C nozzle temperature, 300 mm/s print speed, and 20% triangular infill, were maintained as detailed in Table 4 (Section 3.2.4). This consistency ensures that the observed differences in energy and material consumption are a direct result of the GD configurations and their specific support requirements, rather than variations in machine performance. Figure 10 and Figure 11 present the slicing previews and estimated build parameters for each outcome. The slicing outputs provide quantitative data on material usage, printing time, and energy demand, which, consistent with the TO case, form the numerical basis for the subsequent LCA.

3.3.5. LCA

The LCA of the generatively designed stool legs was conducted to evaluate their environmental performance when manufactured using AM. The same gate-to-gate system boundary applied in the baseline and topology-optimized scenarios was retained to ensure full comparability. The functional unit was defined as one stool consisting of four legs. The two GD outcomes were assessed independently in order to capture potential differences in material use and energy consumption.

Primary data were obtained from slicing outputs, using identical printing parameters to those applied in the TO case. Inventory values are reported separately for GD Outcome 1 and GD Outcome 2, including filament mass, veneer application, electricity consumption, and support waste. This approach allowed for a consistent evaluation of how different GD configurations influence the overall environmental profile of the manufacturing process. While the production setup remained identical, the results reflected the integrated impact of geometry on material throughput and energy demand. Additionally, it is acknowledged that estimates derived from build preparation software are subject to a known uncertainty of ±10–20% [36], the systematic application of identical parameters across all AM scenarios ensures a valid comparative baseline for evaluating the integrated performance of the GD system. Environmental impacts were assessed using the same midpoint indicators as in the baseline and topology-optimized scenarios: global warming potential, energy consumption, material use, material waste, and VOC emissions. This allows for direct comparison across all case studies. The detailed results for the two generative outcomes are summarized in

Table 6. Environmental impact results for GD Outcome 1 (4 legs).

Impact Category	Value	Primary Contributors
Global Warming Potential	~2.23 kg CO2e (2.10 + 0.13)	Electricity use, veneer production
Energy Consumption	7.93 kWh	Printer power demand
Material Use	1.357 kg PETG-CF10 + 0.246 kg oak veneer	Filament, veneer application
Material Waste	~108 g	Support/brim material
VOC Emissions	~0.15 g	PETG-CF10 extrusion, adhesive

and

Table 7. Environmental impact results for GD Outcome 2 (4 legs).

Impact Category	Value	Primary Contributors
Global Warming Potential	~1.88 kg CO2e (1.75 + 0.13)	Electricity use, veneer production
Energy Consumption	6.60 kWh	Printer power demand
Material Use	0.933 kg PETG-CF10 + 0.246 kg oak veneer	Filament, veneer application
Material Waste	~74.6 g	Support/brim material
VOC Emissions	~0.15 g	PETG-CF10 extrusion, adhesive

.

The environmental results of the two generative outcomes confirm notable reductions in both energy consumption and CO₂e emissions compared to the topology-optimized scenario. These improvements stem from the synergy between the optimized geometric forms and the resulting reduction in material usage and printing duration. This confirms that the environmental profile of the component is an outcome of an integrated design–process relationship, where geometry dictates the operational intensity of the AM route. Nevertheless, consistent with the previous cases, electricity consumption remains the dominant contributor to the overall environmental footprint, underscoring the significant influence of process parameters on the sustainability profile of additively manufactured components.

4. Results

4.1. Results and Interpretation of Case Study 1

The Life Cycle Impact Assessment (LCIA) results for the baseline scenario are summarized in Figure 12, Figure 13 and Figure 14. Figure 12 illustrates the distribution of CO₂e emissions across the individual manufacturing stages, while Figure 13 presents the corresponding material waste. Figure 14 highlights the contribution of each process to VOC emissions, and Figure 15 summarizes the electricity demand associated with the production sequence. Together, these figures provide a consolidated overview of the main environmental burdens within the defined system boundaries.

The analysis of the baseline scenario manufacturing process revealed distinct environmental characteristics associated with conventional woodworking. Material waste was primarily generated during early, coarse processing stages, with band saw cutting (approximately 6.16 kg) and final height cutting (approximately 5.77 kg) together accounting for nearly 81% of the total wood waste. This distribution indicated that the largest material losses occurred during initial sizing operations rather than during fine finishing steps, reflecting the inherently subtractive nature of traditional woodworking processes.

Energy consumption was dominated by shaping and surface treatment operations. Lathe turning represented the most energy-intensive process, consuming 6.12 kWh, followed by lacquering at 2.00 kWh. In contrast, the remaining manufacturing stages each consumed less than 0.06 kWh, suggesting that, aside from extended-runtime shaping operations, the overall process chain operates with relatively low energy demand.

VOCemissions originated exclusively from the lacquering stage, amounting to approximately 60 g per stool. No VOC emissions are associated with adhesive application, as the polyurethane adhesive used (PU 501 Express) has a certified VOC content of 0 g/L. Total manufacturing-related CO₂e emissions were estimated at approximately 2.26 kg CO₂e and were driven primarily by a limited number of high-energy processes, with results influenced by the Greek electricity grid emission factor of 0.265 kg CO₂/kWh [31]. It should be noted that upstream material production and downstream use and end-of-life phases are excluded from this assessment.

From a sustainability perspective, the baseline scenario highlights several structural limitations inherent to conventional subtractive woodworking. Although red oak is a renewable material, the manufacturing process results in substantial material losses, amounting to approximately 14.65 kg, or about 72.6% of the initial material input. While the majority of this waste is recovered as sawdust and recycled, a non-negligible fraction is ultimately sent to landfill, reducing overall material efficiency.

At the same time, the modular and largely artisanal production workflow offers flexibility and customization potential, which are valued characteristics in furniture manufacturing. However, this approach also introduces process redundancy and inefficiencies, including repeated planning, thicknessing, and handling steps. These findings underline the trade-off between craftsmanship-driven adaptability and resource efficiency, reinforcing the need for design and manufacturing strategies that balance customization with sustainability objectives.

4.2. Comparative Analysis of Baseline and TO Scenario

The comparative LCA highlights the environmental trade-offs between the conventional red oak stool and the topology-optimized PETG-CF10 stool across four impact categories. With respect to CO₂e emissions (Figure 16), the analysis showed that, despite a substantial reduction in material mass, total emissions increased from 2.26 kg CO₂e in the baseline configuration to 2.8 kg CO₂e in the topology-optimized design, corresponding to an increase of approximately 24%. This outcome was attributed to the prolonged electricity demand of FDM and the additional contribution of oak veneer applied for aesthetic continuity.

Energy consumption (Figure 17) increased from 8.54 kWh in the baseline scenario to 10.07 kWh in the topology-optimized configuration, corresponding to an increase of approximately 18%. The increase was primarily associated with the time-intensive nature of AM compared to the faster subtractive processes used in woodworking. Although TO substantially reduces material usage, the extended printing durations required by FDM lead to higher electricity demand. This finding was consistent with previous studies reporting that AM processes, particularly FDM, generally exhibit higher electricity demand than conventional manufacturing, despite their advantages in material efficiency [43,44].

Material use (Figure 18) was substantially reduced through TO, with the total mass from 20.18 kg of solid oak to 1.77 kg of PETG-CF10 and veneer in the TO scenario, corresponding to a reduction of approximately 91%. This reduction was attributable not only to the optimized geometry, which redistributed material along load-bearing paths, but also to the manufacturing shift from subtractive woodworking to additive deposition. In the baseline, oversized planks were dimensioned and machined, generating large offcuts, whereas in the optimized case, material was deposited only where structurally required. This combined effect underscored that the efficiency gains result from both the applied design strategy and the inherent characteristics of AM.

Material waste (Figure 19) was drastically reduced, decreasing from 14.65 kg in the baseline scenario to only 0.121 kg in the topology-optimized case. This significant decrease was attributable to the near-net-shape nature of AM, which limits material losses to minor amounts associated with support structures and brims.

VOC emissions (Figure 20) were significantly reduced, decreasing from approximately 60 g in the baseline scenario, originating exclusively from the lacquering process to negligible levels below 0.15 g in the TO scenario. This reduction was linked to the elimination of solvent-based coatings and the use of a zero-VOC adhesive for veneering process, demonstrating a clear environmental benefit of the optimized manufacturing approach with respect to air-quality-related impacts.

The comparative results demonstrated a clear paradox. While TO (TO) achieved substantial reductions in material use (−91%), waste (−99%), and VOC emissions (−99%), these gains were counterbalanced by increased CO₂e emissions (+24%) and energy demand (+18%). The findings highlighted that improvements in material efficiency do not automatically translate into overall environmental benefits when the chosen manufacturing process, in this case FDM, imposes long fabrication times and high energy consumption.

This outcome revealed a key limitation of TO: although it identifies structurally efficient solutions, it converges on a single geometry without considering variability in fabrication effort. The LCA results therefore indicated that a more holistic approach is required, one capable of balancing material savings with energy efficiency.

These results confirmed the need to investigate alternative approaches; consequently, the study proceeded with GD to examine whether different geometries could maintain material efficiency while reducing energy demand.

4.3. Comparative Environmental Impact Analysis by Category

Energy demand exhibited substantial variation across the examined manufacturing scenarios. The baseline configuration consumed 8.54 kWh, mostly from multiple short woodworking operations. TO showed the highest demand (10.07 kWh) due to extended print duration and heating requirements in FDM. GDs were more efficient, with Gen 1 at 7.93 kWh and Gen 2 at 6.60 kWh, demonstrating the importance of geometry-driven printability in reducing energy burdens. The numerical results are summarized in

Table 8. Comparative summary of total manufacturing energy consumption across all design scenarios.

Scenario	Energy Consumption (kWh)
Baseline (Solid Red Oak/CNC)	8.54
Topology Optimized (TO)	10.07
Generative Design (GD)—GD Outcome 1	7.93
Generative Design (GD)—GD Outcome 2	6.60

, while the comparative distribution of energy use across all scenarios is presented in Figure 21.

CO₂e emissions followed a similar trend across the examined scenarios. The topology-TO scenario exhibited the highest footprint at 2.8 kg CO₂e, whereas the second GD outcome achieved the lowest value at 1.88 kg CO₂e, reflecting both reduced energy consumption and lower material mass. The baseline configuration, with emissions of 2.26 kg CO₂e, exceeded both generative outcomes, primarily due to emissions associated with the lacquering process. The numerical results are summarized in

Table 9. Comparative summary of Global Warming Potential across all design scenarios.

Scenario	CO2e Emissions (kg CO2e)
Baseline (Solid Red Oak/CNC)	2.26
Topology Optimized (TO)	2.8
Generative Design (GD)—GD Outcome 1	2.23
Generative Design (GD)—GD Outcome 2	1.88

, while the comparative distribution of CO₂e use across all scenarios is presented in Figure 22.

Material use (Figure 23) exhibited substantial differences across the examined scenarios. The baseline configuration required 20.18 kg of solid red oak, whereas all AM–based cases consumed less than 2 kg of material, including the applied veneer. Among these, Generative Outcome 2 achieved the greatest material savings, requiring only 0.93 kg of PETG-CF10 in combination with approximately 0.246 kg of oak veneer, corresponding to a reduction exceeding 94% relative to the baseline. When compared with the topology-optimized case (1.51 kg), Generative Outcome 2 demonstrated an additional material reduction of approximately 38%, confirming the effectiveness of geometry-driven mass minimization strategies. The numerical results are summarized in

Table 10. Comparative summary of total material usage across all design scenarios.

Scenario	Material Use (Kg)
Baseline	20.18 (Red Oak)
Topology Optimized (TO)	1.76 (PETG-CF10 + Oak Veneer)
Generative Design (GD)—GD Outcome 1	1.60 (PETG-CF10 + Oak Veneer)
Generative Design (GD)—GD Outcome 2	1.18 (PETG-CF10 + Oak Veneer)

, while the comparative distribution of material use across all scenarios is illustrated in Figure 23.

Material waste (Figure 24) differed markedly across the examined manufacturing approaches. Subtractive processes in the baseline scenario generated approximately 14.65 kg of waste, corresponding to nearly 72% of the initial raw material input. In contrast, all AM–based scenarios produced less than 0.15 kg of waste, primarily originating from brims and support structures. Among these, Generative Outcome 2 exhibited the lowest material waste, at approximately 75 g, confirming that optimized geometry and build orientation play a critical role in minimizing surplus material in FDM. The numerical results are summarized in

Table 11. Comparative summary of material waste generated per design scenario.

Scenario	Material Waste (g)
Baseline	14,650
Topology Optimized (TO)	~121
Generative Design (GD)—GD Outcome 1	~108
Generative Design (GD)—GD Outcome 2	~75

, while the comparative distribution of material waste across all scenarios is presented in Figure 23.

VOC emissions (Figure 25) exhibited pronounced differences between the baseline and additively manufactured scenarios. As summarized in

Table 12. Comparative summary of VOC emissions per scenario.

Scenario	VOC Emissions (g)
Baseline	~60
Topology Optimized (TO)	<0.15
Generative Design (GD)—GD Outcome 1	<0.15
Generative Design (GD)—GD Outcome 2	<0.15

, the baseline scenario presented substantial VOC emissions of approximately 60 g per functional unit, originating exclusively from the solvent-based lacquering stage employed in conventional woodworking. In contrast, all additively manufactured configurations, including the topology-optimized design and both GD outcomes, exhibit negligible VOC emissions, remaining below 0.15 g. This reduction is primarily attributed to the elimination of workshop-applied lacquers and the use of a zero-VOC polyurethane adhesive during the veneering process. The results indicated that surface finishing operations, rather than material shaping or structural optimization strategies, constituted the dominant source of VOC emissions in furniture manufacturing. Consequently, the adoption of digitally fabricated hybrid structures with pre-finished veneers represents an effective pathway for significantly reducing air-quality-related environmental impacts within the defined gate-to-gate system boundary.

To consolidate the findings across all scenarios, Baseline, Topology Optimized, and GD Outcomes 1 and 2, a comparative overview was developed.

Table 13. Summary comparison of environmental performance across all scenarios.

Impact Category	Minimum Impact Scenario	Maximum Impact Scenario
Energy use	Gen 2 (6.6 kWh)	TO (10.07 kWh)
CO2 Emissions	Gen 2 (1.88 kg CO2e)	TO (2.8 kg CO2e)
Material use	Gen 2 (0.933 kg + ~0.246 kg veneer)	Baseline (20.18 kg red oak)
Waste	Gen 2 (~75 g)	Baseline (~14.65 kg)
VOC emissions	Gen 1 Gen 2 (~0.15 g from adhesive)	Baseline (~60 g from lacquer and glue)

highlights the best and worst performers in each impact category (energy use, CO₂e emissions, material use, waste, and VOCs). This summary facilitates a direct comparison, clarifying the trade-offs between subtractive and additive approaches as well as between optimization and generative methods.

4.4. Sensitivity Analysis and Uncertainty Assessment

To address the inherent limitations of relying on slicing software for LCI data and to comply with the data quality requirements of ISO 14044, a formal sensitivity analysis was performed. Given the identified ±10–20% uncertainty range in slicer-derived estimates for energy and material use [30], a worst-case scenario adjustment of +20% was applied to the AM results.

Table 14. Sensitivity analysis of CO₂e emissions under ±20% uncertainty.

Scenario	Measured CO2e (kg)	+20% Error (Worst Case)	Ranking Robustness
Baseline (Wood)	2.26	2.26 (Measured)	Still Higher than Gen 2
TO scenario	2.80	3.36	Remains Worst Performer
Gen GD Outcome 1	2.23	2.68	Sensitive to Ranking Shift
Gen GD Outcome 2	1.88	2.25	Remains Best Performer

presents the impact of this uncertainty on the CO₂e emission rankings. The analysis confirmed that even under a maximum +20% error margin in energy demand, Generative GD Outcome 2 (2.25 kg CO₂e) remains environmentally more efficient than the Baseline wooden stool (2.26 kg CO₂e). This demonstrated that the comparative rankings and the identified environmental benefits of GD are robust and remain valid despite potential machine-specific process variability.

The adjustment level of ±20% was established based on empirical studies identifying this as the upper bound of deviation between slicer-based energy estimates and real-world FDM power consumption [36].

4.5. Post-Reconstruction Structural Validation

To ensure that the internalized structural interventions in the GD scenarios maintain functional integrity, a post-reconstruction Finite Element Analysis (FEA) was performed in Fusion 360. The structural validation was conducted on the unified solid bodies generated by the software’s export process. Although the generative algorithm merges the internal ‘unassigned’ geometry with the preserved regions into a single monolithic entity, assigning the properties of PETG-CF to the entire volume represents a conservative, worst-case scenario analysis. Given that the final physical components are reinforced externally with oak veneer and solid wood elements, materials significantly stiffer than the thermoplastic core, proving structural integrity for a purely PETG-CF model ensures an even higher safety margin for the actual hybrid assembly.

To account for the anisotropic behavior inherent in FDM manufacturing, where inter-layer adhesion typically exhibits lower tensile strength than the bulk material, the peak simulation stresses were compared against the minimum reported Z-axis tensile strength for PETG-CF (approx. 15–20 MPa) [36]. The results for both GD outcomes, summarized in

Table 15. Finite Element Analysis (FEA) results summary for GD Outcome 1 and 2.

Parameter	GD Outcome 1	GD Outcome	Safety Comparison
Max Von Mises Stress	0.861	0.539	>17× lower than min. Z-strength
Max Displacement	0.012	0.005	Negligible structural deflection
Min. Safety Factor	58.05	92.76	Exceeds furniture safety standards
Max Equivalent Strain	1.801 × 10−4	1.150 × 103	Within elastic material limits

and illustrated in Figure 26 and Figure 27, confirmed exceptional structural robustness. The stress distribution and displacement plots are illustrated in Figure 28, Figure 29 and Figure 30. The stress distribution and displacement plots are illustrated in Figure 27, Figure 28, Figure 29 and Figure 30. For GD Outcome 2, the peak Von Mises stress was recorded at 0.539 MPa (Figure 28), with a negligible displacement of 0.006 mm (Figure 29). Similarly, GD Outcome 1 exhibited a peak stress of 0.861 MPa (Figure 30) and a maximum displacement of 0.012 mm (Figure 31).

As shown in Figure 25 and Figure 30, the maximum stress recorded across both outcomes remains significantly lower than the critical failure threshold of 15 MPa. Specifically, GD Outcome 2 exhibited even higher rigidity, with peak displacement restricted to 0.005 mm. This quantitative validation proved that the generative geometries are not only material-efficient but also structurally over-engineered for safety, maintaining high rigidity even under conservative anisotropic assumptions.

5. Discussion

This study examined whether sustainability in furniture design could be enhanced through internalized structural interventions, specifically by applying TO to a conventional stool without altering its external form. The results partially confirmed the initial hypothesis: while TO achieved a 57.8% mass reduction, this did not directly translate to a lower environmental footprint. Instead, the LCA revealed a critical energy–material trade-off, where the carbon savings from reduced timber were offset by the energy intensity of the FDM process. Integrating LCA into this early-stage design process allowed for the quantitative validation of these optimized structural interventions, proving that mass reduction must be balanced with manufacturing efficiency [16].

5.1. Energy Intensity and the Manufacturing Paradox

The TO scenario exhibited the highest energy demand (10.07 kWh) and CO₂e emissions (2.8 kg CO₂e) of all cases. This paradox indicates that material efficiency alone is an insufficient proxy for sustainability in AM. As the results suggest, the environmental performance of AM-based furniture is governed more by print duration and toolpath complexity than by raw material volume. Our results align with current literature which indicate that while CNC milling uses roughly 8–12 MJ/kg, AM energy demand can be significantly higher due to long thermal cycles [45].

5.2. Generative Design as a Process-Aware Strategy

The shift to GD addressed the limitations of pure structural optimization. GD Outcome 2 emerged as the most sustainable configuration, reducing energy consumption by 34% compared to the TO scenario (6.60 kWh vs. 10.07 kWh). This improvement stems from a higher correspondence between geometric form and manufacturing constraints. Unlike TO, which produced complex internal load paths that prolonged build time, GD algorithms prioritized toolpath continuity and minimized support structures. The data suggest that geometric restraint and continuity are more valuable for sustainability in AM than maximal material removal.

Beyond environmental gains, the structural validation of the generative outcomes (Section 4.5) confirms that geometry selection functions as a reliable lever for performance. The peak stress of 0.539 MPa in GD Outcome 2, being over 27 times lower than the material’s conservative Z-axis strength threshold, demonstrates that generative algorithms can produce high-performance furniture components that far exceed standard safety margins. This proves that the identified material and energy savings do not come at the expense of structural durability, supporting the viability of this computational workflow for functional, load-bearing furniture design.

5.3. Methodological Limitations and Data Asymmetry

It is important to acknowledge several methodological limitations related to data resolution and consistency within the LCI. Data for the baseline scenario were collected during an active production run in a small-scale workshop serving a commercial client. While this setting enabled realistic observation of manufacturing practices, it also introduced practical constraints on measurement accuracy. In particular, although primary material waste was quantified by weighing components after each machining step, secondary waste streams, such as fine sawdust captured by extraction systems, could not be measured directly and were instead estimated using mass-balance assumptions.

Similarly, energy consumption for the baseline scenario was calculated based on recorded machine operating times measured with a stopwatch. This approach may slightly underestimate actual electricity demand, as it does not fully capture energy use during machine idling, startup phases, or short-term power fluctuations.

By contrast, the AM scenarios relied on simulation-based estimates generated by slicing software. While these tools provide consistent and repeatable inventory data, they are subject to a documented uncertainty range of approximately ±10–20%, primarily due to auxiliary thermal loads and machine-specific behaviors that are not fully represented in digital models. Nevertheless, the sensitivity analysis indicates that the main comparative findings remain stable even under a conservative worst-case adjustment. The topology-optimized scenario consistently exhibits the highest energy demand, while the second GD outcome maintained the lowest environmental footprint across all evaluated indicators.

5.4. AM as an Integrated Design Decision Variable

The findings necessitate a reframing of AM from a neutral fabrication tool to an active design decision variable. In extrusion-based processes, environmental performance is coupled with toolpath characteristics such as overhang distribution and curvature smoothness. This study demonstrates that incorporating manufacturing constraints early in the computational workflow allows sustainability to function as a generative driver rather than a downstream validation step. Specifically, the transition to pre-finished veneers effectively eliminated manual finishing, thereby bypassing a phase known for high VOC emissions and health hazards in furniture production [46,47]. The results indicate that designs facilitating uninterrupted extrusion paths and minimal supports are found to have shorter build times and less cumulative energy demand, regardless of material usage.

5.5. Future Work

Given the simulation-based nature of this proof-of-concept study, future research should focus on empirical validation of the proposed findings through physical fabrication and testing. In particular, experimental measurements of energy consumption and material waste during FDM production are necessary to replace slicer-based estimates. The use of direct sub-metering on printing equipment would enable the accurate capture of machine-specific power demand, heating, and cooling cycles, thereby reducing the uncertainty associated with digital proxies.

Further work is also required to verify the mechanical performance of the optimized components. Physical load testing in accordance with EN 1728 and ANSI/BIFMA standards would confirm the structural adequacy of the redesigned legs under real service conditions. In addition, the durability of the hybrid construction should be examined by experimentally quantifying the interfacial bond strength between the 3D-printed PETG-CF core and the 2 mm oak veneer, for example through pull-off or shear tests.

Beyond technical performance, future studies should address user-related aspects of product evaluation. Although the optimized designs achieve significant reductions in mass, this change may influence users’ perceptions of stability, quality, and value. Investigating the relationship between weight reduction, tactile experience, and perceived robustness is therefore essential for assessing the commercial viability of such hybrid furniture solutions.

The present case study focused on a massive, cylindrical leg geometry that provided ample internal design space for optimization. To assess the broader applicability of the proposed framework, future research should examine alternative geometries, including more slender, tapered, or asymmetrical forms. Expanding the exploration of the GD solution space would help clarify how different structural archetypes respond to the integrated design–process approach, particularly in cases where the available design space is more constrained.

In addition, the adaptability of the methodology should be evaluated across different manufacturing routes. Applying the framework to large-scale AM systems or hybrid workflows that combine additive and subtractive processes would clarify whether the approach remains effective beyond desktop FDM, and whether it offers sufficient flexibility to support sustainability-driven design decisions across diverse industrial contexts.

The scalability of the approach also warrants further investigation. Applying the methodology to a wider range of furniture typologies, such as dining tables or lounge chairs, would help determine whether the observed trade-offs between energy consumption and material savings remain consistent across different sizes, load requirements, and functional constraints.

Finally, future LCAs should be extended beyond a gate-to-gate perspective. Incorporating cradle-to-grave LCI data for PETG-CF10 filament, in line with ISO 14044 guidelines, would enable a more comprehensive comparison of environmental impacts. This extension is particularly important for accounting for the high embodied energy of synthetic polymers and for providing a complete assessment of the environmental implications of hybrid, additively manufactured furniture.

6. Conclusions

This study demonstrates that the environmental performance of additively manufactured furniture is governed by an integrated design–material–process relationship rather than isolated geometric changes. The results confirm that while TO (TO) can achieve a 57.8% mass reduction, the resulting geometric complexity leads to increased print duration and energy demand, offsetting the benefits of material savings. This finding proves that material efficiency alone was an insufficient indicator of sustainability in the transition from subtractive to AM.

In contrast, GD proved to be a more effective sustainability driver by incorporating manufacturing constraints directly into the form-generation process. The most efficient generative outcome reduced total energy consumption by 34% compared to the TO scenario, highlighting that geometry-driven printability, characterized by toolpath continuity and minimized support structures, is the primary lever for reducing the carbon footprint of FDM-produced components. Furthermore, post-reconstruction structural validation confirms that these generative geometries maintain a high safety margin, with peak stresses remaining significantly below conservative inter-layer adhesion thresholds. This ensures that the achieved environmental gains do not compromise the product’s functional durability or user safety, reinforcing the feasibility of the proposed framework for real-world furniture applications.

The proposed methodology contributes a structured pathway for designers to move beyond intuitive sustainability claims toward quantified performance metrics. By integrating LCA at the earliest stages of the computational workflow, the study demonstrates that sustainability can function as an active generative variable. While further experimental validation through physical prototyping and mechanical testing is necessary, these findings establish a robust framework for aligning designer intent with measurable environmental performance in the era of digital fabrication.

Finally, it should be noted that this study adopts a gate-to-gate system boundary and therefore focuses exclusively on the manufacturing phase. Although the hybrid design demonstrates improved material efficiency and reduced VOC emissions, these benefits should be interpreted in light of the comparatively high embodied energy of CF-PETG relative to natural red oak. Future work should extend the assessment to a cradle-to-grave boundary in order to capture upstream and downstream environmental impacts and to provide a more comprehensive evaluation of the overall life-cycle sustainability of additively manufactured furniture.