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In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.

Machine-learning-based one-to-many inverse design of multi-material lattices

Panesar, Ajit; Yu, Xiaochen

Type:
Papers
DS 145: Proceedings of the DESIGN2026 19th International Design Conference
Year:
2026
Editor:
Štorga, M.; Škec, S.; Martinec, T.; Marjanović, D.; Pavković, N.
Author:
Panesar, Ajit; Yu, Xiaochen
Series:
DESIGN
Institution:
Imperial College London, United Kingdom
Page(s):
2063-2070
DOI number:
10.1017/pds.2026.10564
ISSN:
2732-527X (Online)
Abstract:
This work presents an ML-based inverse design framework for multi-material lattices with curved struts, targeting mechanical and thermal performance. Using cubic-spline parameterization and discrete material assignment, the design space expands beyond conventional lattices. A workflow combining a material classifier, property predictor, and inverse generators addresses one-to-many mapping, enabling probabilistic sampling and diverse designs. The approach supports multi-objective trade-offs and lays the foundation for multi-scale optimization of functionally graded metamaterials.
Keywords:
additive manufacturing, artificial intelligence (AI), data-driven design
DESIGN2026 - The 19th International Design Conference
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