Deep Generative Design for Mass Production
Jihoon Kim Yongmin Kwon Namwoo Kang
Generative Design (GD) has evolved as a transformative design approach,
employing advanced algorithms and AI to create diverse and innovative solutions
beyond traditional constraints. Despite its success, GD faces significant
challenges regarding the manufacturability of complex designs, often
necessitating extensive manual modifications due to limitations in standard
manufacturing processes and the reliance on additive manufacturing, which is
not ideal for mass production. Our research introduces an innovative framework
addressing these manufacturability concerns by integrating constraints
pertinent to die casting and injection molding into GD, through the utilization
of 2D depth images. This method simplifies intricate 3D geometries into
manufacturable profiles, removing unfeasible features such as
non-manufacturable overhangs and allowing for the direct consideration of
essential manufacturing aspects like thickness and rib design. Consequently,
designs previously unsuitable for mass production are transformed into viable
solutions. We further enhance this approach by adopting an advanced 2D
generative model, which offer a more efficient alternative to traditional 3D
shape generation methods. Our results substantiate the efficacy of this
framework, demonstrating the production of innovative, and, importantly,
manufacturable designs. This shift towards integrating practical manufacturing
considerations into GD represents a pivotal advancement, transitioning from
purely inspirational concepts to actionable, production-ready solutions. Our
findings underscore usefulness and potential of GD for broader industry
adoption, marking a significant step forward in aligning GD with the demands of
manufacturing challenges.