{"acknowledgement":"The authors would like to thank anonymous reviewers for their helpful feedback; Haomiao Wu for her contribution to the algorithm development in the early stage of the project; Elias Baldwin, David Tsay, Alexander Lefort, and Qiyang Tan for helping the experiments.","citation":{"chicago":"Zhao, Haisen, Max Willsey, Amy Zhu, Chandrakana Nandi, Zachary Tatlock, Justin Solomon, and Adriana Schulz. “Co-Optimization of Design and Fabrication Plans for Carpentry.” <i>ACM Transactions on Graphics</i>. Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3508499\">https://doi.org/10.1145/3508499</a>.","short":"H. Zhao, M. Willsey, A. Zhu, C. Nandi, Z. Tatlock, J. Solomon, A. Schulz, ACM Transactions on Graphics 41 (2022).","ista":"Zhao H, Willsey M, Zhu A, Nandi C, Tatlock Z, Solomon J, Schulz A. 2022. Co-optimization of design and fabrication plans for carpentry. ACM Transactions on Graphics. 41(3), 32.","mla":"Zhao, Haisen, et al. “Co-Optimization of Design and Fabrication Plans for Carpentry.” <i>ACM Transactions on Graphics</i>, vol. 41, no. 3, 32, Association for Computing Machinery, 2022, doi:<a href=\"https://doi.org/10.1145/3508499\">10.1145/3508499</a>.","ieee":"H. Zhao <i>et al.</i>, “Co-optimization of design and fabrication plans for carpentry,” <i>ACM Transactions on Graphics</i>, vol. 41, no. 3. Association for Computing Machinery, 2022.","ama":"Zhao H, Willsey M, Zhu A, et al. Co-optimization of design and fabrication plans for carpentry. <i>ACM Transactions on Graphics</i>. 2022;41(3). doi:<a href=\"https://doi.org/10.1145/3508499\">10.1145/3508499</a>","apa":"Zhao, H., Willsey, M., Zhu, A., Nandi, C., Tatlock, Z., Solomon, J., &#38; Schulz, A. (2022). Co-optimization of design and fabrication plans for carpentry. <i>ACM Transactions on Graphics</i>. Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3508499\">https://doi.org/10.1145/3508499</a>"},"doi":"10.1145/3508499","type":"journal_article","title":"Co-optimization of design and fabrication plans for carpentry","intvolume":"        41","article_type":"original","date_updated":"2024-08-06T07:03:14Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2107.12265","open_access":"1"}],"volume":41,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"issue":"3","quality_controlled":"1","abstract":[{"text":"Past work on optimizing fabrication plans given a carpentry design can provide Pareto-optimal plans trading off between material waste, fabrication time, precision, and other considerations. However, when developing fabrication plans, experts rarely restrict to a single design, instead considering families of design variations, sometimes adjusting designs to simplify fabrication. Jointly exploring the design and fabrication plan spaces for each design is intractable using current techniques. We present a new approach to jointly optimize design and fabrication plans for carpentered objects. To make this bi-level optimization tractable, we adapt recent work from program synthesis based on equality graphs (e-graphs), which encode sets of equivalent programs. Our insight is that subproblems within our bi-level problem share significant substructures. By representing both designs and fabrication plans in a new bag of parts (BOP) e-graph, we amortize the cost of optimizing design components shared among multiple candidates. Even using BOP e-graphs, the optimization space grows quickly in practice. Hence, we also show how a feedback-guided search strategy dubbed Iterative Contraction and Expansion on E-graphs (ICEE) can keep the size of the e-graph manageable and direct the search towards promising candidates. We illustrate the advantages of our pipeline through examples from the carpentry domain.","lang":"eng"}],"department":[{"_id":"BeBi"}],"year":"2022","language":[{"iso":"eng"}],"external_id":{"arxiv":["2107.12265"]},"status":"public","day":"09","publication":"ACM Transactions on Graphics","_id":"17065","scopus_import":"1","date_created":"2024-05-29T06:09:23Z","month":"03","article_number":"32","publication_identifier":{"issn":["0730-0301"],"eissn":["1557-7368"]},"publication_status":"published","article_processing_charge":"No","date_published":"2022-03-09T00:00:00Z","publisher":"Association for Computing Machinery","author":[{"first_name":"Haisen","full_name":"Zhao, Haisen","orcid":"0000-0002-6389-1045","id":"fb7f793a-80d1-11eb-8869-d56e5b2a8ff4","last_name":"Zhao"},{"last_name":"Willsey","full_name":"Willsey, Max","first_name":"Max"},{"last_name":"Zhu","first_name":"Amy","full_name":"Zhu, Amy"},{"full_name":"Nandi, Chandrakana","first_name":"Chandrakana","last_name":"Nandi"},{"last_name":"Tatlock","first_name":"Zachary","full_name":"Tatlock, Zachary"},{"full_name":"Solomon, Justin","first_name":"Justin","last_name":"Solomon"},{"last_name":"Schulz","full_name":"Schulz, Adriana","first_name":"Adriana"}],"oa_version":"Preprint"}