{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:08:29Z","timestamp":1767337709473,"version":"3.41.2"},"reference-count":40,"publisher":"ASME International","issue":"2","license":[{"start":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T00:00:00Z","timestamp":1578009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61702517","61772525","61873236","61802211","61902345"],"award-info":[{"award-number":["61702517","61772525","61873236","61802211","61902345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Traditional design optimization is an iterative process of design, simulation, and redesign, which requires extensive calculations and analysis. The designer needs to adjust and evaluate the design parameters manually and continually based on the simulation results until a satisfactory design is obtained. However, the expensive computational costs and large resource consumption of complex products hinder the wide application of simulation in industry. It is not an easy task to search the optimal design solution intelligently and efficiently. Therefore, a simulation data-driven design approach which combines dynamic simulation data mining and design optimization is proposed to achieve this purpose in this study. The dynamic simulation data mining algorithm\u2014on-line sequential extreme learning machine with adaptive weights (WadaptiveOS-ELM)\u2014is adopted to train the dynamic prediction model to effectively evaluate the merits of new design solutions in the optimization process. Meanwhile, the prediction model is updated incrementally by combining new \u201cgood\u201d data set to reduce the modeling cost and improve the prediction accuracy. Furthermore, the improved heuristic optimization algorithm\u2014adaptive and weighted center particle swarm optimization (AWCPSO)\u2014is introduced to guide the design change direction intelligently to improve the search efficiency. In this way, the optimal design solution can be searched automatically with less actual simulation iterations and higher optimization efficiency, and thus supporting the rapid product optimization effectively. The experimental results demonstrate the feasibility and effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1115\/1.4045527","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T16:29:50Z","timestamp":1574699390000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":8,"title":["A Simulation Data-Driven Design Approach for Rapid Product Optimization"],"prefix":"10.1115","volume":"20","author":[{"given":"Yanli","family":"Shao","sequence":"first","affiliation":[{"name":"Key Laboratory of Complex, Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Huawei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou 310015, China"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Part Rolling Key Laboratory of Zhejiang Province, Ningbo University, Ningbo, Zhejiang 315211, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Mechanical and Manufacturing Engineering, Cardiff University, Cardiff CF24 3AA, UK"}]},{"given":"Yusheng","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310027, China"}]}],"member":"33","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"issue":"2","key":"2021022706141618800_CIT0001","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.cirp.2008.09.007","article-title":"Recent Advances in Engineering Design Optimisation: Challenges and Future Trends","volume":"57","author":"Roy","year":"2008","journal-title":"CIRP Ann. 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