• Tsz Ho Kwok, Concordia University
• Jida Huang, University of Illinois Chicago

With the rapid advancement of advanced manufacturing (AM) technologies, it is possible to mass produce personalized goods for the nowadays customization-oriented market. Since the AM technologies can rapidly fabricate complex physical objects, the personalization brings challenges for the design generation in mass customization. Due to the high complexity and design variations among distinct customization requirements, it is desirable to procure an efficient generative design methodology that can match the customization prerequisites and provide sufficient fabrication integrity for AM processes. Within the context of mass customization, the size of design data is explosively increased, the geometry and structure complexity included in the customized products make the problem even more challenging. In the recent decade, machine learning (ML) has been proved a suitable tool for analyzing large and complex datasets. Therefore, it is unsurprisingly to introduce ML methods for the customized design generation problem.

In this minisymposium, we invite papers that discuss research in the development and utilization of ML principles and tools to gain a fundamental understanding of generative design for mass customization with AM. Topics in this minisymposium include but not limited to the learning-based method for design specification, customized design formalization, computational design modeling, ML-based topology optimization, geometric deep learning methods for design and fabrication in AM, and efficient pre-fabrication computation for 3D printing.