Generative Design of Sheet Metal Structures
Generative Design of Sheet Metal Structures
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DOI：
10.1145/3592444
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发表时间：
2023-07
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影响因子：
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通讯作者：
Amir Barda;Guy Tevet;Adriana Schulz;Amit H. Bermano
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文献类型：
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作者：
Amir Barda;Guy Tevet;Adriana Schulz;Amit H. Bermano
钣金加工可能是最常见的金属加工技术之一。尽管SM很流行，但它的设计是手工的，成本很高，而且严格的实践限制了搜索空间，产生了次优结果。在此基础上，提出了SM零件首次自动设计的框架。专注于承载应用，我们的新系统生成高性能可制造的SM，坚持SM设计所需要的众多约束：最终的零件在遵守结构，空间和制造约束的同时最小化制造成本。换句话说，部件要足够坚固，不干扰环境，并坚持制造工艺。这些需求总结起来就是一个复杂的、稀疏的、昂贵的搜索空间。我们的生成方法是一个精心设计的探索过程，包括两个步骤。在段发现过程中，从输入负载到可连接区域的连接被累积起来，在段组合过程中，搜索最有效的组合。对于Discovery，我们定义了一个精简语法，并使用Markov-Chain Monte Carlo （MCMC）方法对部分进行采样，在相互通信的实例（即链）中运行以实现多样性。这之后是一个短暂的连续优化，可以建立一个多样化和高质量的子结构库。在组合过程中，选择一个有效且成本最低的组合。为了在不增加制造成本的情况下显著提高合规性，我们将候选零件加固到自己身上——一种独特的SM能力，称为自铆接。我们在https://github.com/amir90/AutoSheetMetal中提供代码和数据。我们展示了我们的生成方法为许多场景生成可行的部件。我们将我们的系统与人类专家进行比较，并观察到零件质量和设计时间的改进。我们进一步分析了管道的步骤，并制作了一些结果进行验证。我们希望我们的系统将扩展SM设计领域，用几分钟的标准CPU取代昂贵的专家时间，使这种廉价可靠的制造方法对任何人来说都是可行的。
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.