Additive Manufacturing Process Selection and Decision Support
Summary
Additive manufacturing (AM) encompasses a suite of technologies that build parts layer by layer from digital models, offering unparalleled design freedom, material efficiency and customisation. The diversity of AM processes—ranging from powder‐bed fusion and material extrusion to vat photopolymerisation—necessitates systematic decision‐support to navigate trade-offs in resolution, build speed, mechanical performance, cost and sustainability. Process selection is inherently a multi-criteria challenge, requiring integration of quantitative data on material properties, machine capabilities and environmental impact with qualitative expert judgements. Contemporary frameworks fuse multi-criteria decision-making (MCDM) methods, fuzzy logic and data-driven tools to accommodate uncertainty and stakeholder preferences. Life-cycle analysis, digital twins and real-time monitoring are increasingly embedded to refine selection under dynamic production conditions. Effective decision support accelerates adoption of AM in sectors such as aerospace, automotive and biomedical engineering by ensuring optimal alignment between part requirements, material systems and manufacturing constraints, while advancing global aims of resource efficiency and supply-chain resilience.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Research from all publishers
Recent studies have advanced hybrid decision-support techniques that explicitly handle uncertainty in AM process selection. A 2024 methodology employs Pythagorean fuzzy multiple‐criteria group decision-making to aggregate expert inputs into fuzzy preference numbers, using TOPSIS and sensitivity analysis to guide material and process choices in sustainable AM designs. In 2023, a Pythagorean fuzzy CRITIC–EDAS framework was applied to the automotive industry: criterion weights were determined objectively via inter-criteria correlation, and AM alternatives were ranked by their distance from an average solution; sensitivity and comparative analyses confirmed robustness under varying expert weightings. A 2022 decision-support tool tailored a database of AM parameters for rapid investment casting, introducing a novel weighting algorithm to convert subjective priorities into comparative values; an industrial case study demonstrated its capability to streamline technology evaluation across design and manufacturing stages.
Additive Manufacturing Process Selection and Decision Support publication trend
The graph below shows the total number of articles in additive manufacturing process selection and decision support across all publications each year (not limited to Nature Index journals).
Technical terms
Additive Manufacturing (AM): A layer-by-layer fabrication process enabling complex geometries and customised parts directly from digital models.
Multi-Criteria Decision-Making (MCDM): A set of techniques for evaluating and prioritising alternatives against multiple, often conflicting criteria.
Pythagorean Fuzzy Sets: An extension of fuzzy set theory allowing enhanced expression of uncertainty by permitting squared membership and non-membership degrees to sum to at most one.
CRITIC (Criteria Importance Through Inter-criteria Correlation): An objective weighting method that derives criterion weights from contrast intensity and correlation among criteria.
EDAS (Evaluation based on Distance from Average Solution): A ranking method that assesses alternatives by their distance from an ideal average solution across criteria, facilitating compromise decision-making.
References
- Solution strategy for sustainable additive manufacturing design problem using Pythagorean fuzzy MCGDM methodology. Complex & Intelligent Systems (2024).
- Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS. PLOS ONE (2023).
- A multicriteria decision-making method for additive manufacturing process selection. Rapid Prototyping Journal (2022).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.