PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 26, 2025Virtual and Physical Prototyping25 citationsOpen Access

Multi-material additive manufacturing: a computational design perspective

View Full Paper
XYXiaochen YuJGJacklyn GriffisGMGuha Manogharan

Key Points

  • Multi-material additive manufacturing relies on advanced computational design tools for effective product fabrication.
  • Key challenges include material response modelling and manufacturability, impacting multi-material design outcomes.
  • Observational analysis of computational algorithms including topology optimisation and data-driven strategies was conducted.
  • Recommendations support integrating design and manufacturing processes to enhance multi-material applications.

Abstract

Multi-material additive manufacturing (MMAM) unlocks unprecedented opportunities in engineering, afforded by material and geometric complexity. However, leveraging its full potential necessitates advanced computational design tools tailored to specific application needs and fabrication techniques capable of translating digital designs into physical products with minimal discrepancy. This review provides a comprehensive evaluation of computational algorithms available for multi-material design, encompassing numerical methods like topology optimisation as well as data-driven inverse design strategies. MMAM techniques are evaluated for their multi-material design capability and manufacturing constraints. Key challenges in MMAM design are identified, including multi-material interface, advanced material response modelling, manufacturability, failure constraints, robustness, multifunctionality, and integrated design-manufacturing-validation workflow. Recommendations are made for incorporating these considerations into the optimisation framework. Future research directions are also suggested to pave the way for innovative multi-material applications.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Yu et al. (2025) studied this question.

synapsesocial.com/papers/68af620aad7bf08b1eae318fhttps://doi.org/10.1080/17452759.2025.2546671
Ask AI
Helpful
Bookmark
Share
View Full Paper