1. Introduction and research background
Additive Manufacturing (AM) is a relatively new technology which is rapidly changing the manufacturing industry. Even though it can still be considered at a developing stage, it is foreseen that AM will dominate the industry in a few decades (Reference PontesPontes, 2021). AM is composed of several different technologies (e.g. Fused Deposition Modelling – FDM, Selective Laser Melting -SLM etc.), all of which share the same production concept of manufacturing layer by layer. This new way of manufacturing parts allowed designers to get rid of most of the constraints that Conventional Manufacturing (CM) technologies, such as casting or milling, imposed. Therefore, an evolution from the traditional Design for Manufacturing (DfM) approaches to a Design for AM (DfAM) one is needed (Reference RosenRosen, 2014), to fully exploit the benefits that AM can bring. Given the recent advancements and the fast-changing capabilities of AM, the current common limitation of the adoption of DfAM guidelines resides in its knowledge and application, together with a lack of standardization (Reference Carfagni, Fiorineschi, Furferi, Governi and RotiniCarfagni et al., 2018). Indeed, investigating the state of the art, some researchers highlight that AM guidelines mainly rely on empirical evidence and that the majority of choices are made based on the designer’s experience (Reference Uz Zaman, Rivette, Siadat and BaqaiUz Zaman et al., 2018), which might not be sufficient considering its rapid evolution. To reduce this necessity, the design process often relies on computational tools (Reference Nyamekye, Lakshmanan, Tepponen and WestmanNyamekye et al., 2024) which facilitate Topology Optimization (TO) or lattice lightweight structures (LS) design, at the expense of significant time and computational resources (Reference Priarone, Catalano and SettineriPriarone et al., 2023). Authors proposed methodologies and frameworks to generalize and simplify the adoption of DfAM guidelines in the product development process, like Reference Salonitis and ZarbanSalonitis & Zarban (2015) who proposed a multicriteria tool to define the final design based on DfAM optimized design. Reference Favi, Mandolini, Campi, Cicconi and GermaniFavi et al. (2021) proposed a framework to couple the geometrical features and manufacturing constraints, structuring a knowledge-based system grounded on AM guidelines.
From the environmental point of view, AM technologies have a high energy demand, and sometimes, even higher than the CM processes (Reference Ingarao, Priarone, Deng and ParaskevasIngarao et al., 2018). Existing lifecycle studies typically focus on specific processes, limiting the evaluation to the production phase. Their typical outcomes evidence that environmental and economic indicators can vary across AM technologies, depending on technological and product characteristics (e.g. shape or materials) (Reference Landi, Zefinetti, Spreafico and RegazzoniLandi et al., 2022), process parameters (e.g. machine settings) or the phases of the associated lifecycle (e.g. material and manufacturing, end of life, etc.) (Reference Ma, Harstvedt, Dunaway, Bian and JaradatMa et al., 2018, Reference Naser, Defersha, Xu and YangNaser et al., 2023). Reference Santiago-Herrera, Igos, Alegre, Martel-Martín and BarrosSantiago-Herrera et al. (2024) performed a Life Cycle Assessment (LCA) comparing Directed Energy Deposition (DED) with casting and highlighted that, despite the weight reduction, AM still shows higher environmental impacts than the CM. Reference Ingarao, Priarone, Deng and ParaskevasIngarao et al. (2018) conducted a comparative LCA between Selective Laser Melting and CM, highlighting that AM would result in a more environmentally sustainable outcome only if the use phase is considered. To improve AM overall sustainability, some researchers proposed an integrated design framework to suggest the optimal compromise of materials, processes, and machinery for AM, addressing the environmental issue by evaluating the recyclability and consumption of materials (Reference Uz Zaman, Rivette, Siadat and BaqaiUz Zaman et al., 2018). Reference Liu, Deng, Wei, Zhao, Li and ZhangLiu et al. (2021) proposed a framework to assess and compare the energy consumption of hybrid AM processes with CM. What also emerges from analyzing the literature is that although AM is often associated with reduced waste compared to CM, the actual waste reduction obtained varies significantly among AM technologies, with some achieving near-zero waste while others, depending on specific process and material parameters, generate amounts comparable to or even greater than CM (Reference Faludi, Bayley, Bhogal and IribarneFaludi et al., 2015; Reference London, Lewis and KeoleianLondon et al., 2020). Additionally, many DfAM tools and frameworks do not consider the environmental implications and possible benefits of AM across the entire lifecycle of the product (Reference Favi, Murgese, Gallozzi, Chiacchietta, Marconi and MandoliniFavi et al., 2025).
The review of the current literature highlights that within the design domain, the designer’s inherent knowledge and expertise remain crucial in the effective implementation of DfAM guidelines. Furthermore, there is a discernible lack of awareness regarding the environmental impacts associated with additive processes; this frequently results in erroneous assessments when selecting the most efficient production strategy.
In this context, this paper aims at proposing the concept of a tool that allows designers to perform lifecycle-based comparisons between AM and CM alternatives, supporting informed decision-making throughout the entire design workflow, such as technology and material selection, towards a novel ecodesign-oriented workflow that integrates environmental, economic, and technological assessments directly into the AM design process.. This approach aims at reducing the need for multiple software tools and designers’ experience, enabling early detection of potential sustainability and manufacturing issues already at the early development stages by unifying DfAM guidelines, LCA insights, and process evaluation.
The paper is structured as follows: in Section 2, the present design workflow is presented. Section 3 introduces the tool’s composition and its integration within the design workflow, with possible use scenarios. Section 4 discusses the possible benefits that the approach and tool may introduce, while Section 5 concludes the paper with future outlooks.
2. Current design workflow
After a review of the state of the art in DfAM, combined with an analysis of current design practices and interviews with AM industry professionals, carried out by understanding the production process of four different companies involving their technical offices, research and development departments, a conventional workflow for the design and manufacture components by AM has been outlined (Reference Yang, Hsu, Baughman, Godfrey, Medina, Menon and WienerYang et al., 2017; Reference Wiberg, Persson and ÖlvanderWiberg et al., 2019; Reference Dash, Nordin and JohanssonDash et al., 2025, ISO/ASTM 52910:2018). This workflow is typically structured into three main phases: i) Computer-Aided Design (CAD) model design, ii) model optimization according to DfAM guidelines, and iii) print process setup. In addition to these established stages, two further stages can be integrated to address environmental and economic requirements, which have become significant drivers in the design process (Reference Tao, Chen, Yu and LiuTao et al., 2017). Figure 1 provides an overview of this workflow.
The design requirement phase represents the starting point of the development process. At this stage, production targets, environmental and economic drivers, and key technical and product specifications are defined. It is also in this phase that it is decided whether to employ AM or CM.
The CAD modelling phase step, during which the initial geometry is defined, could be considered as a starting point for optimization. During the optimization, DfAM guidelines are applied to determine the adequate optimization strategies (e.g TO, LS, etc). Within this phase, the optimization objective (i.e. mass to reduce) together with the load cases are established. The optimization process largely relies on engineers’ experience and knowledge of AM and the component’s functional requirements. This stage is commonly performed with dedicated optimization tools, which may differ from the modelling software.
Once the optimization is completed, structural analysis through Computer-Aided Engineering tools is required to ensure that the component can withstand the applied loads. If such requirements are not satisfied, the optimization strategy must be revised iteratively adjusting the objectives or modifying the geometry.
Following the validation of structural integrity, the print process analysis is carried out. This phase is typically performed with specific software such as Ansys, Siemens NX or Netfabb (Reference Zanini, Marconi, Mandolini, Carfagni, Furferi, Di Stefano, Governi and GherardiniZanini et al., 2024), ensuring the manufacturability of the part with the chosen AM technology. This phase can be considered optional, as print simulation is not yet a widespread practice due to the availability and computational requirements of simulation tools. Indeed, printability is more commonly verified through physical test prints. If the print quality fails to meet the initial requirements, further adjustments to the geometry or the optimization (since they do not consider the manufacturability) are necessary. In some cases, adopting a different AM technology may be considered as an alternative; however, this leads to high time and computational cost, as DfAM guidelines are process specific.
Eventually, environmental and economic assessments are conducted with dedicated tools (i.e. SimaPro for environmental evaluations and LeanCost for economic ones).
Additive manufacturing design workflow
Figure 1 Long description
The flowchart illustrates the additive manufacturing design workflow, detailing the steps from design requirement to manufacturing. The process starts with the design requirement phase, which involves AM technology and DfAM application. This leads to the CAD modelling phase, where dedicated CAD software is used. The next step is optimization, which involves choosing optimization strategies and objectives. Following this is structural analysis, where dedicated softwares are used to check if the design is structurally compliant. If not, the process loops back to the optimization phase. If structurally compliant, the process moves to print process analysis, where dedicated softwares and AM technology compliance are considered. This phase checks if the part is printable. If not, the process loops back to the optimization phase. If printable, the process moves to economical and environmental assessment, where dedicated softwares are used to check if the part complies with environmental and economical requirements. If not, the process loops back to the optimization phase. If compliant, the process proceeds to manufacturing.
3. Concept of the tool
In this section the general concept of the tool and its architecture is briefly described to highlight its integration within the AM design process. Eventually, the possible use scenarios are presented.
3.1. Tool’s architecture
The proposed tool is structured in four core modules: i) a knowledge-based system, ii) a CAD evaluation module, iii) an optimization module, and iv) a product lifecycle evaluation module, as illustrated in Figure 2.
The CAD evaluation module operates in two main stages: feature extraction and feature evaluation. Starting with the first stage, feature recognition algorithms extract critical information from the CAD, enabling the verification of compliance with DfAM guidelines (Reference Favi, Mandolini, Campi, Cicconi and GermaniFavi et al., 2021). The latter stage, through the use of Machine Learning (ML), acts as a recommendation system to suggest a set of design alternatives that meet and satisfy the initial requirements. This feedback mechanism enhances design efficiency by reducing iterative cycles and anticipating downstream aspects of the design process, such as printability and sustainability indicators.
The optimization module acts as an intermediary between the user and the Knowledge-based system’s database. By leveraging data from the CAD evaluation module, it enables the comparison of different optimization strategies and objectives (i.e. minimum mass to reduce) with the aim of providing suitable strategies that balance environmental, economic, and structural performance metrics. It also allows the exploration of prior optimization results for similar product applications. This process would significantly reduce computational effort during geometric validation and accelerate the overall optimization workflow.
The proposed ecodesign tool’s architecture
The product lifecycle evaluation module is composed of four interoperable submodules dedicated to printing, environmental, economic, and productivity assessment across the lifecycle of the product. The print assessment submodule analyses the CAD geometry and, according to the initial design requirement, evaluates the printability of the part, addressing the support volume and build orientation, evaluating the printing time and printing quality of the selected AM process. The environmental assessment submodule quantifies not only the impacts due to the manufacturing phase but also addresses the environmental burdens across the lifecycle of the product (Reference Chiacchietta, Marconi, Favi, Gallozzi and MandoliniChiacchietta et al., 2025). This can be achieved using lifecycle models specifically designed and tuned for AM technologies built to incorporate data originating from the product development process and elaborate them with dedicated environmental databases (Reference Favi, Murgese, Gallozzi, Chiacchietta, Marconi and MandoliniFavi et al., 2025). Although AM might result in an energy-intensive technology, this approach might bring to substantial benefits evaluable during other phases of the lifecycle, such as less fuel consumption if enough lightweighting is achieved and low material consumption due to optimized material utilization. The economic assessment submodule extends beyond production costs, addressing lifecycle economic indicators of the product, providing valuable insights not only on general costs but also on economic variables such as the Break Even Point and the Return on Investments. Such assessments can be carried out on the basis of AM-specific cost assessment models which also use data coming from a specifically developed Economic database (Reference Favi, Murgese, Gallozzi, Chiacchietta, Marconi and MandoliniFavi et al., 2025). The productivity assessment submodule focuses on managerial performance metrics such as Time to Market and production scalability, providing decision makers with insights into overall manufacturing efficiency. Exception made for the printing submodule, which is responsible for the evaluation of the manufacturing phase, the remaining three submodules perform comparative analysis between AM and CM technologies (e.g. Casting, Injection molding etc.). These comparisons employ the use of multi-criteria decision-making algorithms that, based on multiple technical (e.g., quality) and sustainability-oriented criteria (e.g. Global Warming Potential environmental impact), provide quantitative ranking between design alternatives. The multi-criteria decision-making algorithm serves as the conjunction point for performing comparisons using different criteria and metrics obtained from the previously explained modules, carefully weighed directly by the final user according to the specific requirements and application context.
The knowledge-based system (KBS) module represents the backbone of the tool, providing the foundational information required by all other modules. Structured as an ontology-like database, it allows the storage, retrieval, and continuous enrichment of DfAM (Reference Favi, Mandolini, Campi, Cicconi and GermaniFavi et al., 2021; Reference Haruna, Yang and JiangHaruna et al., 2023) and ecodesign guidelines. The KBS performs two critical functions. Firstly, it supplements the required knowledge, in the form of qualitative data to ensure DfAM compliance (e.g., DfAM geometry compliance), and quantitative data to support evaluation and comparison tasks for the optimization module. Secondly, it records the outcomes of evaluation, comparison, and optimization processes, thereby supporting self-learning and continuous improvement of the database. Beyond serving as a repository of DfAM guidelines, the KBS also incorporates practical examples and case-based evidence drawn from literature and industry. This functionality supports the identification of emerging trends and best practices towards a sustainability-oriented AM.
3.2. Ecodesign workflow for AM
The proposed tool aims at supporting the designer in making informed, sustainability-oriented decisions from the early phases of the design process. As summarized in Figure 3, the updated workflow involves the use of the tool, allowing environmental, economic, and production evaluations across the entire development process, starting from the design requirement phase.
At this stage, the proposed tool would provide preliminary assessments on environmental and economic sustainability, thereby guiding the selection of the most suitable AM technology (metal or polymer-based) according to the intended application field. Based on the defined requirements, the tool would determine whether AM constitutes an appropriate alternative to CM, ensuring that AM is selected only when it provides tangible environmental, economic, and productive advantages thanks to multi-criteria algorithms.
During CAD modelling phase, the tool evaluates the component geometry, suggesting design modifications that enhance DfAM. These modifications are then related to a multiple design alternative assessment, where environmental and economic metrics are evaluated to demonstrate the possible benefits of adapting the geometry to the DfAM guidelines.
In the subsequent optimization phase, the tool would recommend mainly the most appropriate optimization strategy (e.g. TO or LS), objectives, and constraints, comparing structural requirements with previous successful optimizations that led to environmental and economic benefits. The sustainability indicators are evaluated with the dedicated lifecycle models by comparing the pre-optimization geometry to the post-optimization geometry to quantitatively assess the economic and environmental impact differences, highlighting eventual gains and improvements from previous design versions. This approach is intended to reduce the number of design iterations typically required for structural validation.
For the printing process analysis, the proposed tool aims at assessing the printability of the component by evaluating its print time and the support volumes, while considering environmental, economic, and production drivers. During this phase, the tool would suggest the most suitable technology, by comparing, for instance, SLM, DED and casting for metal applications or FDM, Stereolithography and injection molding for polymeric ones, and print settings alternatives, such as different build orientations or varying job productivity by providing rankings through multi-criteria algorithms. Within the same phase, the tool would determine the expected product quality to assure full compliance with initial specifications.
Ecodesign for additive manufacturing tool’s integration within the AM workflow
Eventually, during the environmental and economic assessment the tool would provide, within the same interface, full sustainability analysis across the product’s lifecycle. At this stage, the final geometry is evaluated and compared with CM to eventually ensure that DfAM is fully exploited, and the product is ready to be manufactured.
3.3. Use scenarios
To clarify the applicability and integration of the proposed tool and approach within the design process, two representative use scenarios are presented: i) the novel development of an AM product and ii) the redesign of an existing product for AM.
The first use scenario illustrates the design of a new product developed specifically for AM technology. Beginning with the design requirement (see Section 2), the user (e.g., a manager or an engineer) would input key product specifications (e.g., product application field, target productivity, structural requirements etc.). Based on this input, the tool would perform a preliminary assessment using the KBS module. This module would provide recommendations regarding the most adopted AM technology for the specified product application, the most suitable materials, and guidelines to inform the initial part design with an emphasis on enhancing sustainability. At this stage, a preliminary comparison with CM is carried out to ensure whether AM offers potential advantages in terms of cost, performance, or sustainability. Following with the CAD modelling, the user could further refine the analysis by including geometrical data. In this case, the tool would employ both the CAD and product lifecycle modules, suggesting design improvements, and generating multiple design alternatives with preliminary lifecycle evaluations, after a careful selection of the comparison criteria weights. In this phase CM technologies are still considered as possible alternatives. The tool subsequently would narrow the selection to a feasible set of materials and a reduced set of AM technologies thanks to the multi-criteria algorithms. As the design advances with the optimization phase, the tool would activate the optimization module together with the KBS to suggest valid optimization strategies and objectives. Concluding the workflow with the product evaluation, the final user enters once again the optimized geometry, activating the product lifecycle evaluation and KBS modules. At this stage, detailed environmental, economic, and productivity assessments are carried out and compared with previous geometries and different technological variables (e.g., different build orientations, different support volumes, etc.) to identify the optimal configuration to optimize all criteria.
The second use scenario highlights a redesign task where an existing product, originally manufactured with CM, is adapted to be manufactured with AM. It is important to emphasize that redesigned products typically do not fully comply with DfAM guidelines, as these aim to develop new geometries unconstrained from CM limitations. Nevertheless, such redesign tasks are still highly relevant in both industry and literature and therefore merit consideration. At the beginning of the redesign process, the original CAD and design specifications are inserted into the tool. In this scenario, all four modules are activated. In detail, the CAD and optimization modules collaborate to provide and identify the necessary information to reshape the initial geometry, ensuring that the redesigned part leverages AM capabilities effectively. Concurrently, the KBS and the product lifecycle modules work together to generate and assess design alternatives, justifying the redesign based on performance improvements, material efficiency, and sustainability outcomes.
4. Discussion
The use of the proposed tool aims at reducing the number of iterations typically required during the product development process. The tool would enable informed decision-making well before detailed modeling and analysis begin by providing key design and assessment information at an early design stage. In the context of AM, where design knowledge is limited to the designer’s experience, this early-stage approach accelerates the application of DfAM guidelines. As a result, designers could develop functional parts more efficiently by integrating environmental and economic sustainability considerations in the evaluation process.
Another possible advantage of the approach proposed in this paper lies in its capacity to enhance flexibility throughout the AM design process. What emerges from the use scenarios is that multiple professionals, such as engineers and managers, could employ the tool. The primary input data required are the product specifications and the CAD geometry of the part, both accessible to various stakeholders, promoting a concurrent engineering approach and fostering cross-disciplinary collaboration. This is further reinforced by the tool’s integrated architecture, which, conceived as a unified software program, would minimize the need to transfer information between different software programs. This feature is particularly beneficial during the product evaluation phase, where conventional workflows typically require different software tools (typically one per analysis), each with distinct input data required. For instance, performing a Life Cycle Assessment in SimaPro needs an explicit identification of material and energy flows. In addition, these software programs often require long and specific training, while the proposed tool automates many of these data transformations through its modular structure, thus reducing potential sources of error. It is important to highlight that the modules could still be used independently to conduct more targeted and immediate evaluations during specific design phases.
Additionally, the integration of a KBS within the tool would introduce several key benefits. Leading among these is the formalization of ecodesign guidelines for AM, ensuring a more appropriate and consistent application of AM. This contributes to making AM truly competitive with conventional technologies, especially when the KBS is used in combination with the environmental assessment module operating with a lifecycle perspective. Together, these modules enable a more comprehensive comparison between AM and CM, extending the evaluation beyond the production phase, including upstream and downstream lifecycle phases. Moreover, the expandable nature of the database ensures continuous updating as new materials, optimization techniques, and AM technology improve. The KBS can be readily adapted to incorporate these advancements, ensuring that the guidelines remain current and robust.
Finally, it is important to emphasize that the tool proposed in this article is not intended to replace specialized DfAM tools such as topological optimization software, CAD systems, or printing simulation tools, nor Life Cycle Assessment or Life Cycle Costing tools. Instead, its primary purpose is to support AM design by optimizing the use of specific DfAM tools only when necessary.
On the other hand, the tool’s implementation may present several challenges and limitations that need to be addressed. Data integration between the modules can be problematic due to variations in data formats and types. Additionally, the lack of available technological and printer-related data may limit the modelling of the product lifecycle module, considering that most of the industrial-grade AM printers are of a closed nature. Similarly, the material modelling of AM feedstock requires specific attention, mainly for environmental evaluations, considering that their processing (e.g., Gas Atomization for powder-based AM) is not always comprehensively represented in existing environmental databases. Moreover, the tool’s application may be limited to the specific application field on which both lifecycle models and KBS are built (e.g., automotive rather than construction). Current ecodesign guidelines for AM may not yet provide a sufficient level of accuracy by the machine learning algorithms that would be implemented in the CAD evaluation module, thus continuous refinement and integration of emerging guidelines are essential to fully exploit the tool’s potential.
5. Concluding remarks
This paper introduces the concept of a dedicated AM tool that would enable a novel ecodesign-oriented approach within the AM design workflowl. The tool would help anticipate potential design and sustainability challenges that would emerge in later design stages, where modifications tend to be both economically and temporally costly by providing early insights with the adoption of DfAM and ecodesign approaches. In addition, its expected input parameters aim at fostering concurrent approaches, allowing different stakeholders to easily participate in the AM design process.
Addressing the current limitations will be essential to ensure the robustness and applicability of the tool across diverse industrial contexts.
Future steps focus on the initial development and integration of the individual modules, ensuring seamless data flow and interaction within the tool’s architecture. Subsequent validation will involve industrial case studies aimed at assessing the tool’s efficacy, usability, and scalability in real-world product development scenarios.
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