A Methodology to Predict and Optimize Ease of Assembly for Injected Parts in a Family-Mold System
In this study, the assembly behavior for two injected components made by a family mold system were investigated. Specifically, a feasible method was proposed to evaluate the characteristic length of two components within a family mold system using numerical simulation and experimental validation. Re...
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| Veröffentlicht in: | Polymers 2021-09, Vol.13 (18), p.3065 |
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| creator | Huang, Chao-Tsai Lin, Tsai-Wen Jong, Wen-Ren Chen, Shia-Chung |
| description | In this study, the assembly behavior for two injected components made by a family mold system were investigated. Specifically, a feasible method was proposed to evaluate the characteristic length of two components within a family mold system using numerical simulation and experimental validation. Results show that as the packing pressure increases, the product index (characteristic length) becomes worse. This tendency was consistent for both the simulation prediction and experimental observation. However, for the same operation condition setting through a basic test, there were some differences in the product index between the simulation prediction and experimental observation. Specifically, the product index difference of the experimental observation was 1.65 times over that of the simulation prediction. To realize that difference between simulation and experiment, a driving force index (DFI) based on the injection pressure history curve was proposed. Through the DFI investigation, the internal driving force of the experimental system was shown to be 1.59 times over that of the simulation. The DFI was further used as the basis for machine calibration. Furthermore, after finishing machine calibration, the integrated CAE and DOE (called CAE-DOE) strategy can optimize the ease of assembly up to 20%. The result was validated by experimental observation. |
| doi_str_mv | 10.3390/polym13183065 |
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Specifically, a feasible method was proposed to evaluate the characteristic length of two components within a family mold system using numerical simulation and experimental validation. Results show that as the packing pressure increases, the product index (characteristic length) becomes worse. This tendency was consistent for both the simulation prediction and experimental observation. However, for the same operation condition setting through a basic test, there were some differences in the product index between the simulation prediction and experimental observation. Specifically, the product index difference of the experimental observation was 1.65 times over that of the simulation prediction. To realize that difference between simulation and experiment, a driving force index (DFI) based on the injection pressure history curve was proposed. Through the DFI investigation, the internal driving force of the experimental system was shown to be 1.59 times over that of the simulation. The DFI was further used as the basis for machine calibration. Furthermore, after finishing machine calibration, the integrated CAE and DOE (called CAE-DOE) strategy can optimize the ease of assembly up to 20%. The result was validated by experimental observation.</description><identifier>ISSN: 2073-4360</identifier><identifier>EISSN: 2073-4360</identifier><identifier>DOI: 10.3390/polym13183065</identifier><identifier>PMID: 34577966</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Assembly ; CAD ; CAE ; Calibration ; Computer aided design ; Computer aided engineering ; Design optimization ; Genetic algorithms ; Injection molding ; Linear programming ; Molds ; Optimization techniques ; Polymer melts ; Product development ; Product quality ; Research methodology ; Simulation ; Taguchi methods ; Viscosity</subject><ispartof>Polymers, 2021-09, Vol.13 (18), p.3065</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-89ae8b8d1dd7f7e064019c2bf3c5be599005ae3a49f2736fe5168270ec2494a93</citedby><cites>FETCH-LOGICAL-c392t-89ae8b8d1dd7f7e064019c2bf3c5be599005ae3a49f2736fe5168270ec2494a93</cites><orcidid>0000-0001-9367-6031 ; 0000-0002-5823-178X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468598/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468598/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53769,53771</link.rule.ids></links><search><creatorcontrib>Huang, Chao-Tsai</creatorcontrib><creatorcontrib>Lin, Tsai-Wen</creatorcontrib><creatorcontrib>Jong, Wen-Ren</creatorcontrib><creatorcontrib>Chen, Shia-Chung</creatorcontrib><title>A Methodology to Predict and Optimize Ease of Assembly for Injected Parts in a Family-Mold System</title><title>Polymers</title><description>In this study, the assembly behavior for two injected components made by a family mold system were investigated. Specifically, a feasible method was proposed to evaluate the characteristic length of two components within a family mold system using numerical simulation and experimental validation. Results show that as the packing pressure increases, the product index (characteristic length) becomes worse. This tendency was consistent for both the simulation prediction and experimental observation. However, for the same operation condition setting through a basic test, there were some differences in the product index between the simulation prediction and experimental observation. Specifically, the product index difference of the experimental observation was 1.65 times over that of the simulation prediction. To realize that difference between simulation and experiment, a driving force index (DFI) based on the injection pressure history curve was proposed. Through the DFI investigation, the internal driving force of the experimental system was shown to be 1.59 times over that of the simulation. The DFI was further used as the basis for machine calibration. Furthermore, after finishing machine calibration, the integrated CAE and DOE (called CAE-DOE) strategy can optimize the ease of assembly up to 20%. The result was validated by experimental observation.</description><subject>Accuracy</subject><subject>Assembly</subject><subject>CAD</subject><subject>CAE</subject><subject>Calibration</subject><subject>Computer aided design</subject><subject>Computer aided engineering</subject><subject>Design optimization</subject><subject>Genetic algorithms</subject><subject>Injection molding</subject><subject>Linear programming</subject><subject>Molds</subject><subject>Optimization techniques</subject><subject>Polymer melts</subject><subject>Product development</subject><subject>Product quality</subject><subject>Research methodology</subject><subject>Simulation</subject><subject>Taguchi methods</subject><subject>Viscosity</subject><issn>2073-4360</issn><issn>2073-4360</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkc1rGzEQxUVoaYKbY--CXHrZVrv6WOkSMCFuDTYJJD0LrTSbyGhXjiQHtn99N9iUpnOZgfnxmDcPoS81-UapIt_3MUxDTWtJieBn6KIhLa0YFeTDP_M5usx5R-ZiXIi6_YTOKeNtq4S4QGaJt1Ceo4shPk24RHyfwHlbsBkdvtsXP_jfgG9NBhx7vMwZhi5MuI8Jr8cd2AIO35tUMvYjNnhlBh-mahuDww9TLjB8Rh97EzJcnvoC_VrdPt78rDZ3P9Y3y01lqWpKJZUB2UlXO9f2LRDBSK1s0_XU8g64UoRwA9Qw1TctFT3wWsimJWAbpphRdIGuj7r7QzeAszCWZILeJz-YNOlovH6_Gf2zfoqvWjIhuZKzwNeTQIovB8hFDz5bCMGMEA9ZN_PPGFeEiRm9-g_dxUMaZ3tvlGBSKElmqjpSNsWcE_R_j6mJfstPv8uP_gGp-Yy7</recordid><startdate>20210910</startdate><enddate>20210910</enddate><creator>Huang, Chao-Tsai</creator><creator>Lin, Tsai-Wen</creator><creator>Jong, Wen-Ren</creator><creator>Chen, Shia-Chung</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9367-6031</orcidid><orcidid>https://orcid.org/0000-0002-5823-178X</orcidid></search><sort><creationdate>20210910</creationdate><title>A Methodology to Predict and Optimize Ease of Assembly for Injected Parts in a Family-Mold System</title><author>Huang, Chao-Tsai ; 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Specifically, a feasible method was proposed to evaluate the characteristic length of two components within a family mold system using numerical simulation and experimental validation. Results show that as the packing pressure increases, the product index (characteristic length) becomes worse. This tendency was consistent for both the simulation prediction and experimental observation. However, for the same operation condition setting through a basic test, there were some differences in the product index between the simulation prediction and experimental observation. Specifically, the product index difference of the experimental observation was 1.65 times over that of the simulation prediction. To realize that difference between simulation and experiment, a driving force index (DFI) based on the injection pressure history curve was proposed. Through the DFI investigation, the internal driving force of the experimental system was shown to be 1.59 times over that of the simulation. 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| subjects | Accuracy Assembly CAD CAE Calibration Computer aided design Computer aided engineering Design optimization Genetic algorithms Injection molding Linear programming Molds Optimization techniques Polymer melts Product development Product quality Research methodology Simulation Taguchi methods Viscosity |
| title | A Methodology to Predict and Optimize Ease of Assembly for Injected Parts in a Family-Mold System |
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