Jihoon Kim

F431, Faculty Building,

193, Munji-ro, Yuseong-gu,

Daejeon, Republic of Korea

As a Ph.D. student in Mechanical Engineering at KAIST, my driving passion is to transform Generative Design from a mere conceptual tool into a practical, efficient, and innovative solution for the manufacturing challenges of the future.

Guided by the expertise of Prof. Namwoo Kang at Smart Design Lab, I am pioneering the application of deep learning techniques to revolutionize the optimization of mechanical components, focusing on manufacturability, mass production, and crucially, mechanical performance.

At SLB (previously Schlumberger), my work involved developing machine learning models that enhanced the efficiency and accuracy of oil and gas production, tackling well construction challenges such as predictive steering, drill bit optimization, and predictive maintenance.

Before joining KAIST, I received my MSc degree in Data Science from University of Bath, UK, advised by Dr. Wenbin Li, and BEng degree in Mechanical Engineering from University of Bristol, UK.

selected publications

Deep Generative Design for Mass Production

Jihoon Kim*, Yongmin Kwon*, and Namwoo Kang

2024

Abs arXiv PDF

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 inspira- tional 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.