“Generative Design of Sheet Metal Structures” by Barda, Tevet, Schulz and Bermano
Conference:
Type(s):
Title:
- Generative Design of Sheet Metal Structures
Session/Category Title: Fabrication-Oriented Design
Presenter(s)/Author(s):
Moderator(s):
Abstract:
Sheet Metal (SM) fabrication is perhaps one of the most common metalworking technique.Despite its prevalence, SM design is manual and costly, with rigorous practices that restrict the search space, yielding suboptimal results.In contrast, we present a framework for the first automatic design of SM parts. Focusing on load bearing applications, our novel system generates a high-performing manufacturable SM that adheres to the numerous constraints that SM design entails:The resulting part minimizes manufacturing costs while adhering to structural, spatial, and manufacturing constraints. In other words, the part should be strong enough, not disturb the environment, and adhere to the manufacturing process. These desiderata sum up to an elaborate, sparse, and expensive search space.Our generative approach is a carefully designed exploration process, comprising two steps. In Segment Discovery connections from the input load to attachable regions are accumulated, and during Segment Composition the most performing valid combination is searched for.For Discovery, we define a slim grammar, and sample it for parts using a Markov-Chain Monte Carlo (MCMC) approach, ran in intercommunicating instances (i.e, chains) for diversity. This, followed by a short continuous optimization, enables building a diverse and high-quality library of substructures. During Composition, a valid and minimal cost combination of the curated substructures is selected. To improve compliance significantly without additional manufacturing costs, we reinforce candidate parts onto themselves — a unique SM capability called self-riveting. we provide our code and data in https://github.com/amir90/AutoSheetMetal.We show our generative approach produces viable parts for numerous scenarios. We compare our system against a human expert and observe improvements in both part quality and design time. We further analyze our pipeline’s steps with respect to resulting quality, and have fabricated some results for validation.We hope our system will stretch the field of SM design, replacing costly expert hours with minutes of standard CPU, making this cheap and reliable manufacturing method accessible to anyone.
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