A systematic review of conceptual design and multi-criteria decision-making integration in early-phase design for additive manufacturing

Additive manufacturing (AM) offers significant design flexibility, material efficiency, and product customization capabilities. However, fully exploiting these advantages requires the effective application of design for additive manufacturing (DfAM) methodologies, particularly during the conceptual design stage, where critical engineering decisions are made. Despite the increasing adoption of AM technologies, the integration of design methodologies and multi-criteria decision-making (MCDM) techniques within early-phase DfAM remains limited and fragmented. This review investigates prevailing design frameworks, decision-support methods, integration strategies, and existing methodological gaps. Literature published between 2015 and 2025 was retrieved from Scopus, Web of Science Core Collection, ScienceDirect, and IEEE Xplore. The review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Seventy-eight studies met the inclusion criteria and were analyzed through thematic synthesis. The findings reveal that conceptual design methodologies such as the Theory of Inventive Problem Solving (TRIZ) and Axiomatic Design (AD) are commonly used to support concept generation, while MCDM techniques, including Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are widely applied for evaluating manufacturing alternatives and engineering trade-offs. Although some studies demonstrate partial methodological linkage, current integrations remain highly fragmented, application-specific, and predominantly decoupled or sequential. Systematic interaction between concept development and quantitative decision evaluation remains limited across the reviewed DfAM workflows, indicating that existing approaches have not yet matured into broadly applicable and iterative forms of integration. This review highlights the need for unified and fully integrated DfAM frameworks capable of simultaneously supporting conceptual creativity, quantitative decision-making, and manufacturing considerations. The findings provide direction for future research on the development of systematic and iterative DfAM methodologies. The review is limited by its 2015–2025 publication window, selected literature-source coverage, reliance on accessible peer-reviewed literature, and the lack of artificial intelligence (AI)-focused studies from the systematic review dataset.