Innovations in Manufacturing Processes and Systems for Sustainable Practices

In recent years, manufacturing has undergone significant transformation triggered by pressing societal demand for productivity and sustainability, made possible by accelerating technological innovation [1]. This evolution has redefined the design of industrial systems and has introduced a new vision of production that is smarter, more efficient, and environmentally responsible [2]. A relevant aspect of modern manufacturing is its emphasis on performance and sustainability. The use of the traditional metrics of productivity and quality is now coupled with lifecycle thinking, eco-efficiency, and the implementation of circular economy (CE) principles [3]. Thus, it is necessary to develop innovative approaches that embrace the duality of technologies and methods that not only enhance process capabilities but also reduce environmental impact and support regulatory compliance and societal expectations [4]. The integration of digital tools, particularly those enabling real-time data exchange, intelligent decision-making, and predictive maintenance, has become central to achieving these goals [5]. Currently, digital twins, smart sensors, and cyber–physical systems are enabling more flexible and transparent manufacturing environments [6]. Novel strategies are also being developed for the design and control of sustainable supply chains, exploring how sustainability indicators can be embedded into decision-making from the earliest stages of process development [7]. Another prominent subject in this field is the exploration of additive and micro–nano-manufacturing tools, which are revolutionizing how products are designed and fabricated [8]. These technologies offer unprecedented customization, geometrical complexity, material efficiency, and potential for decentralized and on-demand production, which are vital for a more sustainable industrial model [9]. This Special Issue of Processes, titled “Innovations in Manufacturing Processes and Systems Driving Sustainable Practices,” presents a collection of 22 original research and review papers that investigate manufacturing innovation. These contributions are grouped into five key subtopics, each representing a fundamental pillar in the transition toward sustainable and intelligent manufacturing: 1—Strategic Approaches to Sustainable Manufacturing and Digitalization; 2—Additive Manufacturing and 3D Printing for Sustainability; 3—Advanced Materials and Green Material Development; 4—Process Innovation and Sustainable Machining; 5—Digital Tools, Simulation, and Production System Optimization. These five subtopics are addressed separately below, and the published papers focusing on each area are briefly described.

Strategic Approaches to Sustainable Manufacturing and Digitalization: Sustainable manufacturing begins with system-level transformation, in which strategy, policy, and digital integration are required [10]. The adoption of CE philosophies, lifecycle thinking, and green design principles is triggering manufacturers to rethink traditional labor-intensive production models. Digitalization and Industry 4.0 technologies, including digital twins, IoT-enabled monitoring, and cyber–physical systems, are redefining how processes are controlled, optimized, and aligned with environmental objectives [11]. In their review, Tiuncika and Bormane [12] identified 19 key criteria for evaluating expertise in sustainable entrepreneurship across environmental, social, economic, and managerial dimensions. Based on a structured literature review, they emphasized the central role of sustainable management within the triple-bottom-line (TBL) framework and proposed a conceptual tool to assess sustainability practices in small- and medium-sized enterprises (SMEs) in manufacturing. Rahmani et al. [13] analyzed the transition to smart, sustainable factories in the automotive sector, emphasizing the integration of additive manufacturing (AM), AI, and human–robot collaboration. Focusing on companies in northern Portugal, they highlighted the move toward Industry 5.0 through lightweight materials, modular robotics, and digitalization strategies aimed at improving efficiency and driving the energy transition. Wiegand and Wynn [14] explored how digital technologies (DTs) support CE practices in Germany’s textile and clothing industry. Using qualitative methods and interview-based insights, they identified key drivers of and challenges in the adoption of CE principles. In their research, they proposed an operational framework to help companies align with EU sustainability goals through digital transformation. Cai et al. [15] introduced a fuzzy decision-making method to enhance green design and remanufacturability, integrating environmental, cost, and reliability factors. By applying entropy weighting and hesitation fuzzy sets, the approach supported the selection of optimal design solutions. Validated through an injection mold case study, the method proved effective in guiding sustainable design decisions via a functional prototype system. Alotaibi [16] introduced a structured framework to assess the readiness of micro-, small-, and medium-sized enterprises (MSMEs) to adopt remanufacturing practices. Through identifying and modeling nine key attributes, such as core acquisition, reverse logistics, and design for remanufacturing, and through using graph theory and matrices (GTMs), a Remanufacturing Readiness Index (RRI) was developed. The index quantified the preparedness of enterprises and helped pinpoint intervention priorities. A case study validated the model, confirming its value as a decision-support tool in promoting sustainable practices in MSMEs.

Additive Manufacturing and 3D Printing for Sustainability: AM is rapidly becoming the basis of sustainable production [17]. Its ability to minimize material waste, enable decentralized fabrication, and support lightweight, complex designs makes it a green alternative to traditional subtractive methods [18]. Human-centered design and ergonomic assessments are being integrated into AM workflows, reinforcing its role in future-ready, inclusive manufacturing ecosystems [19,20]. Kwon and Hwang [21] conducted a literature review of recurring challenges in AM, focusing on print failures across the AM workflow. Using the PRISMA method, 126 peer-reviewed articles were analyzed and classified into three main categories: design/pre-processing errors, geometric deviations, and failures in in-process monitoring. The review highlighted key mitigation strategies such as STL optimization, thermal control, and real-time sensing, and the findings offered a structured overview of failure types and solutions to support improved AM reliability. In their review, Fianko et al. [22] analyzed 61 studies to explore mass customization strategies in AM, highlighting key technologies, challenges, and applications across various industries. Four main implementation strategies were identified, supported by digital workflows and adaptive production systems, while determined obstacles included material limitations and quality control issues. A multi-tiered framework was proposed to align personalization with operational efficiency, offering clear guidance for AM adoption and future research directions. Mendoza-Muñoz et al. [23] developed a framework combining axiomatic design and the analytic hierarchy process to evaluate AM processes based on sustainability and human factors. Data from 31 users showed that fused deposition modeling (FDM) was the preferred option, particularly for expert users, due to lower material cost and information demand. The method offered a structured approach to AM process selection, with potential for the future integration of additional sustainability indicators. Hasan et al. [24] investigated the reuse of post-consumer polylactic acid (PLA) blended with virgin PLA to produce sustainable 3D-printed parts. Using a Taguchi design, they evaluated the effects of printing parameters on mechanical properties, revealing that greater layer height and nozzle temperature improved strength. The optimized blend achieved notable gains in tensile and flexural performance, confirming its suitability for low-strength applications and reinforcing the potential of recycled PLA in circular manufacturing. Remache et al. [25] analyzed advanced AM techniques for producing turbomachinery components, focusing on impellers and high-performance materials. Powder bed fusion and FDM were the most utilized methods, offering advantages in complexity, material strength, and production speed. Notably, superalloys such as Inconel 718 demonstrated excellent wear and corrosion resistance, with minimal material loss in harsh environments, highlighting AM’s potential to outperform traditional manufacturing methods in demanding applications. Garcia-Llamas et al. [26] investigated the dry milling of Ti6Al4V components produced via selective laser melting (SLM), aiming to enhance machining sustainability. They examined the effects of tool geometry, cutting paths, and feed rates on surface finish, tool wear, and temperature at the tool–workpiece interface using infrared thermography. The results identified optimal conditions that balanced machining performance with reduced energy use and environmental impact, supporting greener manufacturing strategies for titanium parts.

Advanced Materials and Green Material Development: The search for materials that meet performance requirements while reducing ecological impact is the focus of green manufacturing [27]. This includes the development of bioinspired composites, low-impact reinforcements, and recyclable or biodegradable materials tailored for mechanical strength, thermal stability, or multifunctionality [28]. Advances in materials engineering are enabling the production of structures that are both durable and eco-efficient, from construction-grade composites to high-performance coatings and tool materials [29]. In their review, Liu et al. [30] described recent advances in self-renewing polycrystalline diamond compact (PDC) drill bit technologies, highlighting rack-, worm-, and ratchet-driven designs. They detailed their mechanical systems and compared their advantages in deep drilling applications, emphasizing the need for smarter wear detection and multi-technology integration to enhance performance and sustainability. These innovations promise longer bit life and more efficient single-trip drilling in challenging conditions. In another review, Adami et al. [31] assessed hydrogen plasma smelting reduction (HPSR) as a low-carbon pathway for steel production, using hydrogen in various states to reduce iron ore. To improve efficiency, they proposed integrating pre-heating and pre-reduction stages into a planned scale-up of an HPSR demo plant. Through a comparative analysis of reactor types, including fluidized beds, rotary kilns, and cyclone cascades, the authors identified a three-stage cyclone system as the most suitable option for optimizing thermal and hydrogen efficiency in future industrial applications. Estevez and Abdallah [32] optimized a hydrogel composed of sodium alginate, gelatin, and 6% calcium phosphate dibasic (CPDB) through thermally induced cross-linking, aiming to enhance rheological and mineralization properties. The hydrogel demonstrated superior elasticity, hardness, and antimicrobial activity, particularly when incubated in air without cells, where hydroxyapatite crystal formation was most prominent. When combined with osteosarcoma SaOs-2 cells, the bioink promoted dense mineralization. In contrast, fetal bovine serum (FBS)-incubated samples demonstrated less mineralization and greater degradation, indicating the hydrogel’s suitability for bone tissue engineering and sustainable material profile. Zhang et al. [33] analyzed the motion of 3D five-directional circular braided fibers and derived angle and stress transformation formulas using fiber node coordinates. A volume average method was applied to calculate the stiffness matrix and fiber volume content, considering matrix effects. The model parameters included a 2 mm knuckle length, a 7 mm inner diameter, and a 0.61 filling coefficient. Comparison with experimental data showed minimal error, confirming the model’s accuracy in predicting composite mechanical properties. Fazlali et al. [34] introduced a sustainable method to manufacture rigid printed circuit boards (PCBs) using biodegradable PLA and polyhydroxybutyrate (PHB) biopolymers. A novel overmolding process was used to bond these materials to flexible PI-based circuits after standard PCB fabrication, avoiding high-temperature limitations. Functional test circuits confirmed feasibility, while adhesion and end-of-life options such as debonding and composting were evaluated. The approach resulted in significant reductions in CO₂ emissions, energy use, and installation costs.

Process Innovation and Sustainable Machining: Sustainability in manufacturing is increasingly being driven at the process level. Techniques such as dry or near-dry machining, laser-assisted methods, and robotic finishing reduce the need for harmful lubricants, extend tool life, and lower energy consumption [35]. In parallel, surface engineering techniques are evolving to enhance durability, aesthetics, and functionality without compromising ecological standards [36]. These innovations illustrate the potential of process-level refinements to create high-value products with significantly reduced environmental foot-prints. In their review, Pawanr and Gupta [37] presented recent advances in dry machining, highlighting its benefits regarding sustainability and implementation-related challenges. Key findings included the use of textured tools and coatings to enhance performance and reduce costs and the effectiveness of alumina-based ceramic tools with SiC whiskers. Techniques such as microwave sintering and energy-assisted machining were also noted for improving tool properties and cutting efficiency. Shi et al. [38] examined the robotic polishing of NAK80 mold steel using 3M abrasive disks (P180–P800) on an adaptive hydraulic polishing (AHP)-equipped platform. The effects of polishing force, rotational speed, and feed rate on surface roughness were analyzed using single-factor tests and a response surface methodology. Optimal parameters improved surface finish from 0.4 µm to 0.017 µm (95.75%), and a predictive model for P800 abrasives showed ≤7.14% error. Evin et al. [39] applied Six Sigma to optimize the electro-discharge texturing of cold mill work rolls, which directly affects sheet metal surface quality in automotive applications. A predictive model for average roughness was developed using current, voltage, and time as input parameters, and control charts, capability indices, and a modified weighted product method validated the model. Additionally, an algorithm was proposed for process control, aligning with continuous improvement goals.

Digital Tools, Simulation, and Production System Optimization: The digitalization of manufacturing systems has enabled new levels of efficiency, flexibility, and technical insight [40,41]. Simulation-based design, process modeling, and real-time data analytics are driving smarter decision-making and predictive maintenance, thereby minimizing downtime and resource use [42]. Additionally, approaches such as topology optimization, lean mapping, and AI-driven diagnostics allow for process reengineering that aligns productivity goals with sustainability [43]. These advancements are not only improving operational efficiency but also supporting long-term environmental stewardship in manufacturing systems. Zhang et al. [44] presented TopADDPi, a cost-effective Raspberry Pi cluster designed for learning and research in parallel-computing topology optimization (PCTO). They detailed its assembly and configuration, evaluating various setups for computing efficiency, and benchmark tests highlighted its strengths in parallel processing, debugging, energy efficiency, and reduced environmental impact. The results emphasize its value as a sustainable and practical tool for structural engineering education and research. Costa et al. [45] applied lean tools, particularly value stream mapping (VSM), to optimize metal treatment processes. In a case study, value-added and non-value-added activities were identified, revealing a low initial process efficiency (6.29%). After improvements, lead time dropped from 336 to 318 h, and efficiency rose to 7.15%. Despite limitations in discontinuous flows, VSM was proven effective for enhancing performance in complex industrial settings. Wójkowski et al. [46] explored the use of servomotors in industrial equipment control systems, highlighting their advantages over classical mechanical mechanisms. A methodology was proposed for dynamic parameter determination using electronic motion profiles, resulting in an accurate mathematical model. Results showed enhanced system flexibility and adaptability and suggested an optimized angular range (205–270°) to improve performance. The authors emphasized that precise modeling is vital for ensuring system stability and durability.

Collectively, the contributions in this Special Issue offer a comprehensive view of advances redefining the manufacturing sector. These span a wide range of scales and disciplines yet share a common objective: to promote innovation that is technically sound and aligned with environmental standards and social responsibility. As Guest Editors, we are pleased to present this collection of high-quality research and review papers. We thank all the authors for their excellent contributions, the reviewers for their rigorous evaluations, and the editorial team of Processes for their continuous support. It is our hope that this Special Issue will serve as a valuable reference for academics, engineers, and policy-makers alike, inspiring further research and collaboration and driving a more sustainable future in manufacturing.