Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region
Blank holder force (BHF) is one of the important process parameters for successful sheet metal forming. Variable blank holder force (VBHF) that the BHF varies through the forming process is recognized as one of the advanced manufacturing technologies. Therefore, optimization of VBHF trajectory is a...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2019-12, Vol.105 (10), p.4265-4278 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Feng, Yixiong Hong, Zhaoxi Gao, Yicong Lu, Runjie Wang, Yushan Tan, Jianrong |
| description | Blank holder force (BHF) is one of the important process parameters for successful sheet metal forming. Variable blank holder force (VBHF) that the BHF varies through the forming process is recognized as one of the advanced manufacturing technologies. Therefore, optimization of VBHF trajectory is a crucial issue in industries. One of the effective approaches to determine the VBHF trajectory is to use the surrogate modeling techniques. However, it is very inaccurate and time-consuming to determine the VBHF trajectory for successful sheet forming through surrogate-based optimization methods. Therefore, this paper proposes an improved surrogate-based optimization method by integration of support vector regression (SVR) and trust region strategy to optimize VBHF in deep drawing. First, a random sampling test of VBHF in deep drawing is designed and a SVR approximate model of VBHF under random sampling is developed. Then, a trust region algorithm is adopted to predict and control the accuracy of the SVR approximate model of VBHF. Response surface is repeatedly constructed and optimized that is adopted to identify the Pareto-frontier of VBHF. The validity of the proposed approach is examined through the comparison of numerical and experimental results. The results of this research provide a reliable reference for future efforts to optimize VBHF in deep drawing. |
| doi_str_mv | 10.1007/s00170-019-04477-5 |
| format | Article |
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Variable blank holder force (VBHF) that the BHF varies through the forming process is recognized as one of the advanced manufacturing technologies. Therefore, optimization of VBHF trajectory is a crucial issue in industries. One of the effective approaches to determine the VBHF trajectory is to use the surrogate modeling techniques. However, it is very inaccurate and time-consuming to determine the VBHF trajectory for successful sheet forming through surrogate-based optimization methods. Therefore, this paper proposes an improved surrogate-based optimization method by integration of support vector regression (SVR) and trust region strategy to optimize VBHF in deep drawing. First, a random sampling test of VBHF in deep drawing is designed and a SVR approximate model of VBHF under random sampling is developed. Then, a trust region algorithm is adopted to predict and control the accuracy of the SVR approximate model of VBHF. Response surface is repeatedly constructed and optimized that is adopted to identify the Pareto-frontier of VBHF. The validity of the proposed approach is examined through the comparison of numerical and experimental results. The results of this research provide a reliable reference for future efforts to optimize VBHF in deep drawing.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-019-04477-5</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Advanced manufacturing technologies ; Algorithms ; Blankholders ; CAE) and Design ; Computer-Aided Engineering (CAD ; Deep drawing ; Engineering ; Forming ; Industrial and Production Engineering ; Mechanical Engineering ; Media Management ; Metal forming ; Metal sheets ; Model accuracy ; Optimization ; Original Article ; Pareto optimization ; Process parameters ; Random sampling ; Regression models ; Response surface methodology ; Support vector machines ; Trajectory optimization</subject><ispartof>International journal of advanced manufacturing technology, 2019-12, Vol.105 (10), p.4265-4278</ispartof><rights>Springer-Verlag London Ltd., part of Springer Nature 2019</rights><rights>The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2019). All Rights Reserved.</rights><rights>Springer-Verlag London Ltd., part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-7b930713c5213b72310721495fe50bb9ac2de83922f5399ff67c3ed6faceaca53</citedby><cites>FETCH-LOGICAL-c347t-7b930713c5213b72310721495fe50bb9ac2de83922f5399ff67c3ed6faceaca53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00170-019-04477-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00170-019-04477-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Feng, Yixiong</creatorcontrib><creatorcontrib>Hong, Zhaoxi</creatorcontrib><creatorcontrib>Gao, Yicong</creatorcontrib><creatorcontrib>Lu, Runjie</creatorcontrib><creatorcontrib>Wang, Yushan</creatorcontrib><creatorcontrib>Tan, Jianrong</creatorcontrib><title>Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>Blank holder force (BHF) is one of the important process parameters for successful sheet metal forming. Variable blank holder force (VBHF) that the BHF varies through the forming process is recognized as one of the advanced manufacturing technologies. Therefore, optimization of VBHF trajectory is a crucial issue in industries. One of the effective approaches to determine the VBHF trajectory is to use the surrogate modeling techniques. However, it is very inaccurate and time-consuming to determine the VBHF trajectory for successful sheet forming through surrogate-based optimization methods. Therefore, this paper proposes an improved surrogate-based optimization method by integration of support vector regression (SVR) and trust region strategy to optimize VBHF in deep drawing. First, a random sampling test of VBHF in deep drawing is designed and a SVR approximate model of VBHF under random sampling is developed. Then, a trust region algorithm is adopted to predict and control the accuracy of the SVR approximate model of VBHF. Response surface is repeatedly constructed and optimized that is adopted to identify the Pareto-frontier of VBHF. The validity of the proposed approach is examined through the comparison of numerical and experimental results. The results of this research provide a reliable reference for future efforts to optimize VBHF in deep drawing.</description><subject>Advanced manufacturing technologies</subject><subject>Algorithms</subject><subject>Blankholders</subject><subject>CAE) and Design</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Deep drawing</subject><subject>Engineering</subject><subject>Forming</subject><subject>Industrial and Production Engineering</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Metal forming</subject><subject>Metal sheets</subject><subject>Model accuracy</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Pareto optimization</subject><subject>Process parameters</subject><subject>Random sampling</subject><subject>Regression models</subject><subject>Response surface methodology</subject><subject>Support vector machines</subject><subject>Trajectory optimization</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kU1LxDAURYMoOI7-AVcB19V8NE2zlMEvEGaj65AmL2PHTlOTdkR_va0V3M0q8HLufQ8OQpeUXFNC5E0ihEqSEaoykudSZuIILWjOecYJFcdoQVhRZlwW5Sk6S2k74gUtygUa1l1f7-pv09ehxcHjvYm1qRrAVWPad_wWGgcR-xAt4LrFDqDDLprPut3gyiRweMyloetC7PEebB8ijrCJkNLUuAsOGmxah_s4pH76Gsfn6MSbJsHF37tEr_d3L6vH7Hn98LS6fc4sz2WfyUpxIim3glFeScYpkYzmSngQpKqUscxByRVjXnClvC-k5eAKbywYawRfoqu5t4vhY4DU620YYjuu1CxXpJQFz-lBijOiqBS0HCk2UzaGlCJ43cV6Z-KXpkRPEvQsQY8S9K8EPR3A51Aa4XYD8b_6QOoH8fCK0g</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Feng, Yixiong</creator><creator>Hong, Zhaoxi</creator><creator>Gao, Yicong</creator><creator>Lu, Runjie</creator><creator>Wang, Yushan</creator><creator>Tan, Jianrong</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20191201</creationdate><title>Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region</title><author>Feng, Yixiong ; Hong, Zhaoxi ; Gao, Yicong ; Lu, Runjie ; Wang, Yushan ; Tan, Jianrong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-7b930713c5213b72310721495fe50bb9ac2de83922f5399ff67c3ed6faceaca53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Advanced manufacturing technologies</topic><topic>Algorithms</topic><topic>Blankholders</topic><topic>CAE) and Design</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Deep drawing</topic><topic>Engineering</topic><topic>Forming</topic><topic>Industrial and Production Engineering</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Metal forming</topic><topic>Metal sheets</topic><topic>Model accuracy</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Pareto optimization</topic><topic>Process parameters</topic><topic>Random sampling</topic><topic>Regression models</topic><topic>Response surface methodology</topic><topic>Support vector machines</topic><topic>Trajectory optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Feng, Yixiong</creatorcontrib><creatorcontrib>Hong, Zhaoxi</creatorcontrib><creatorcontrib>Gao, Yicong</creatorcontrib><creatorcontrib>Lu, Runjie</creatorcontrib><creatorcontrib>Wang, Yushan</creatorcontrib><creatorcontrib>Tan, Jianrong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Feng, Yixiong</au><au>Hong, Zhaoxi</au><au>Gao, Yicong</au><au>Lu, Runjie</au><au>Wang, Yushan</au><au>Tan, Jianrong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2019-12-01</date><risdate>2019</risdate><volume>105</volume><issue>10</issue><spage>4265</spage><epage>4278</epage><pages>4265-4278</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>Blank holder force (BHF) is one of the important process parameters for successful sheet metal forming. Variable blank holder force (VBHF) that the BHF varies through the forming process is recognized as one of the advanced manufacturing technologies. Therefore, optimization of VBHF trajectory is a crucial issue in industries. One of the effective approaches to determine the VBHF trajectory is to use the surrogate modeling techniques. However, it is very inaccurate and time-consuming to determine the VBHF trajectory for successful sheet forming through surrogate-based optimization methods. Therefore, this paper proposes an improved surrogate-based optimization method by integration of support vector regression (SVR) and trust region strategy to optimize VBHF in deep drawing. First, a random sampling test of VBHF in deep drawing is designed and a SVR approximate model of VBHF under random sampling is developed. Then, a trust region algorithm is adopted to predict and control the accuracy of the SVR approximate model of VBHF. Response surface is repeatedly constructed and optimized that is adopted to identify the Pareto-frontier of VBHF. The validity of the proposed approach is examined through the comparison of numerical and experimental results. The results of this research provide a reliable reference for future efforts to optimize VBHF in deep drawing.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-019-04477-5</doi><tpages>14</tpages></addata></record> |
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| subjects | Advanced manufacturing technologies Algorithms Blankholders CAE) and Design Computer-Aided Engineering (CAD Deep drawing Engineering Forming Industrial and Production Engineering Mechanical Engineering Media Management Metal forming Metal sheets Model accuracy Optimization Original Article Pareto optimization Process parameters Random sampling Regression models Response surface methodology Support vector machines Trajectory optimization |
| title | Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region |
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