{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T13:26:25Z","timestamp":1786973185239,"version":"build-2736575974"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":56,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Injection molding of complex three-dimensional curved components is highly sensitive to processing conditions, particularly when dimensional accuracy and packing quality must be balanced simultaneously. This study proposes an integrated optimization and decision-making framework to improve the molding quality of a commercial curved shin guard plate (SGP) by combining Response Surface Methodology (RSM), adaptive non-dominated sorting genetic algorithm II (NSGA-II), and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A Box\u2013Behnken design was employed to investigate the effects of packing pressure (\n                    <jats:italic>PP<\/jats:italic>\n                    ), melt temperature (\n                    <jats:italic>MT<\/jats:italic>\n                    ), and cooling time (\n                    <jats:italic>CT<\/jats:italic>\n                    ) on absolute dimensional deviation (\n                    <jats:italic>\u0394D<\/jats:italic>\n                    ) and molded part weight (\n                    <jats:italic>PW<\/jats:italic>\n                    ). The results showed that the developed quadratic response surface models exhibited good predictive capability, and analysis of variance identified\n                    <jats:italic>PP<\/jats:italic>\n                    as the most influential parameter. The surrogate models were integrated with standard and adaptive NSGA-II algorithms to perform multi-objective optimization and generate well-distributed Pareto-optimal solutions, with the adaptive NSGA-II showing improved Pareto-front exploration capability based on crowding-distance and hypervolume analyses. Entropy-weighted TOPSIS was subsequently applied to identify the most suitable compromise solution. Experimental validation was conducted at the TOPSIS-selected optimum together with two additional Pareto-optimal conditions. The validation results showed satisfactory agreement between predicted and experimental responses, with deviations remaining below 7.41% for\n                    <jats:italic>\u0394D<\/jats:italic>\n                    and below 1.70% for\n                    <jats:italic>PW<\/jats:italic>\n                    . These results demonstrate the practical applicability of the proposed optimization framework for complex injection-molded components under industrial manufacturing conditions.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-58677-7","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T19:44:32Z","timestamp":1782157472000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-objective optimization of dimensional accuracy and part weight in injection molding of a 3D curved shin guard plate"],"prefix":"10.1038","volume":"16","author":[{"given":"Son","family":"Hoang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinh Quang","family":"Pham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong Chi","family":"Tran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"58677_CR1","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1016\/j.matpr.2017.01.164","volume":"4","author":"G Singh","year":"2017","unstructured":"Singh, G. & Verma, A. 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