“Generative Design of Sheet Metal Structures” by Barda, Tevet, Schulz and Bermano

  • ©Amir Barda, Guy Tevet, Adriana Schulz, and Amit Haim Bermano




    Generative Design of Sheet Metal Structures

Session/Category Title: Fabrication-Oriented Design




    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.


    1. Martin Philip Bendsoe and Ole Sigmund. 2013. Topology optimization: theory, methods, and applications. Springer Science & Business Media.
    2. Desai Chen, Pitchaya Sitthi-amorn, Justin T. Lan, and W. Matusik. 2013. Computing and Fabricating Multiplanar Models. Computer Graphics Forum 32 (2013).
    3. Wei-Hsi Chen, Shivangi Mishra, Yuchong Gao, Young-Joo Lee, Daniel Koditschek, Shu Yang, and Cynthia Sung. 2020. A Programmably Compliant Origami Mechanism for Dynamically Dexterous Robots. IEEE Robotics and Automation Letters PP (01 2020).
    4. Erik Demaine. 2006. Origami, Linkages, and Polyhedra: Folding with Algorithms. 1.
    5. Erik Demaine and Tomohiro Tachi. 2017. Origamizer: A Practical Algorithm for Folding Any Polyhedron.
    6. Tao Du, Kui Wu, Andrew Spielberg, Wojciech Matusik, Bo Zhu, and Eftychios Sifakis. 2020. Functional optimization of fluidic devices with differentiable stokes flow. ACM Transactions on Graphics (TOG) 39, 6 (2020), 1–15.
    7. Christoph Ertelt and Kristina Shea. 2008. Generative Design and CNC Fabrication Using Shape Grammars. Proceedings of the ASME Design Engineering Technical Conference 289 (01 2008).
    8. Samuel Felton, Michael Tolley, C.D. Onal, Daniela Rus, and Robert Wood. 2013. Robot self-assembly by folding: A printed inchworm robot. Proceedings – IEEE International Conference on Robotics and Automation, 277–282.
    9. Peter J Green. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 4 (1995), 711–732.
    10. Ruslan Guseinov, Connor McMahan, Jesús Pérez, Chiara Daraio, and Bernd Bickel. 2020. Programming temporal morphing of self-actuated shells. Nature Communications 11 (01 2020), 237.
    11. Jeffrey Herrmann and D.R. Delalio. 2001. Algorithms for sheet metal nesting. Robotics and Automation, IEEE Transactions on 17 (05 2001), 183 — 190.
    12. Kristian Hildebrand, Bernd Bickel, and Marc Alexa. 2012. crdbrd: Shape Fabrication by Sliding Planar Slices. Computer Graphics Forum 31 (05 2012), 583–592.
    13. Carl-Johan Jonsson, Roland Stolt, and Fredrik Elgh. 2020. Stamping Tools for Sheet Metal Forming: Current State and Future Research Directions.
    14. Jikai Liu, Andrew T Gaynor, Shikui Chen, Zhan Kang, Krishnan Suresh, Akihiro Takezawa, Lei Li, Junji Kato, Jinyuan Tang, Charlie CL Wang, et al. 2018. Current and future trends in topology optimization for additive manufacturing. Structural and Multidisciplinary Optimization 57, 6 (2018), 2457–2483.
    15. N. Morris, A. Butscher, and Francesco iorio. 2020. A subtractive manufacturing constraint for level set topology optimization. Structural and Multidisciplinary Optimization 61 (2020), 1573–1588.
    16. Vishal Naranje. 2010. AI applications to metal stamping die design- A Review.
    17. Jay Patel and Matthew Campbell. 2008. An Approach to Automate Concept Generation of Sheet Metal Parts Based on Manufacturing Operations. Proceedings of the ASME Design Engineering Technical Conference 1 (01 2008).
    18. Jay Patel and Matthew I. Campbell. 2010. An Approach to Automate and Optimize Concept Generation of Sheet Metal Parts by Topological and Parametric Decoupling. Journal of Mechanical Design 132, 5 (2010), 051001.
    19. Grand View Research. 2019. Sheet Metal Market Size, Share & Trends Analysis Report By Material (Steel, Aluminum), By End-Use (Automotive & Transportation, Building & Construction), By Region, And Segment Forecasts, 2019 – 2025. Report ID (2019), 110–1. https://www.grandviewresearch.com/industry-analysis/sheet-metal-market/segmentation
    20. Nora Ripperda and Claus Brenner. 2009. Application of a Formal Grammar to Facade Reconstruction in Semiautomatic and Automatic Environments.
    21. Daniela Rus and Michael Tolley. 2018. Design, fabrication and control of origami robots. Nature Reviews Materials 3 (05 2018), 1.
    22. Sachin Salunkhe, Soham Teraiya, Hussein Mohamed, and Shailendra Kumar. 2019. Smart System for Feature Recognition of Sheet Metal Parts: A Review: Volume 2. 535–549.
    23. Larry Sass. 2006. A Wood Frame Grammar: A Generative System for Digital Fabrication. International Journal of Architectural Computing 4, 1 (2006), 51–67. arXiv:https://doi.org/10.1260/147807706777008920
    24. H.-J. Schek. 1974. The force density method for form finding and computation of general networks. Computer Methods in Applied Mechanics and Engineering 3, 1 (1974), 115–134.
    25. Joseph Schlecht, Kobus Barnard, Ekaterina Spriggs, and Barry Pryor. 2007. Inferring Grammar-based Structure Models from 3D Microscopy Data. In 2007 IEEE Conference on Computer Vision and Pattern Recognition. 1–8.
    26. Adriana Schulz, Cynthia Sung, Andrew Spielberg, Wei Zhao, Robin Cheng, Eitan Grinspun, Daniela Rus, and Wojciech Matusik. 2017. Interactive robogami: An end-to-end system for design of robots with ground locomotion. The International Journal of Robotics Research 36, 10 (2017), 1131–1147. arXiv:https://doi.org/10.1177/0278364917723465
    27. Aditya Soman and Matthew Campbell. 2002. A Grammar-Based Approach to Sheet Metal Design. Proceedings of the ASME Design Engineering Technical Conference 2 (01 2002).
    28. Jerry O. Talton, Yu Lou, Steve Lesser, Jared Duke, Radomír Měch, and Vladlen Koltun. 2011. Metropolis Procedural Modeling. ACM Trans. Graph. 30, 2, Article 11 (April 2011), 14 pages.
    29. Chenming Wu, Haisen Zhao, Chandrakana Nandi, Jeffrey I. Lipton, Zachary Tatlock, and Adriana Schulz. 2019. Carpentry Compiler. ACM Trans. Graph. 38, 6, Article 195 (Nov. 2019), 14 pages.
    30. Yuan Yuan, Hua Xu, and Bo Wang. 2014. An Improved NSGA-III Procedure for Evolutionary Many-Objective Optimization. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (Vancouver, BC, Canada) (GECCO ’14). Association for Computing Machinery, New York, NY, USA, 661–668.
    31. Bo Zhu, Mélina Skouras, Desai Chen, and Wojciech Matusik. 2017. Two-Scale Topology Optimization with Microstructures. ACM Trans. Graph. 36, 4, Article 120b (July 2017), 16 pages.

ACM Digital Library Publication:

Overview Page: