Classification of 3D Casting Models for Product Lifecycle Management and Corporate Sustainability

Tzung-Ming Chen (), Jia-Qi Wu and Jian-Ting Lin
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Tzung-Ming Chen: Department of Industrial Education and Technology, Bao-Shan Campus, National Changhua University of Education, No. 2, Shi-Da Road, Changhua 500, Taiwan
Jia-Qi Wu: Department of Industrial Education and Technology, Bao-Shan Campus, National Changhua University of Education, No. 2, Shi-Da Road, Changhua 500, Taiwan
Jian-Ting Lin: Department of Industrial Education and Technology, Bao-Shan Campus, National Changhua University of Education, No. 2, Shi-Da Road, Changhua 500, Taiwan

Sustainability, 2023, vol. 15, issue 17, 1-15

Abstract: The purpose of this study was to combine simulations and experiments in order to present the first stage of construction in product lifecycle management. Based on the simplification of casting models, the relationship between the filling and solidification characteristics, casting methods, and geometrical classifications of aluminum alloy precision casting products was investigated. By rearranging and summarizing the data, the casting models could be digitally managed; moreover, the digitized data could be used as the basis for intelligent processes in further developments. The simulations calculated and analyzed the casting speeds, defect locations, material densities, and critical fraction of a solid A356 aluminum–silicon alloy; the actual casting was carried out and samples were taken for metallographic observation to confirm the simulation results. The part model was simplified with four basic geometric shapes: solid cylinder, tubular, block rectangle, and thin-shell rectangle. The 150 casting models were summarized using 37 combinations, which were further classified into five main categories to match the casting method: solid cylindrical, tubular, and thin-shell rectangular for side casting, and discoidal and plate rectangular for bottom casting. File-compression rates of up to 75% were achieved after classification and archiving, and data integrity was maintained. Finally, model training using random forest classification resulted in an 88.8% accuracy when predicting the casting method. This research is based on the practical issues raised by business owners and R&D engineers, and a solution was obtained. From the perspective of product lifecycle management, the results of this study show the consistency and uniformity of product design rules, as well as the reusability of product process planning, which can be integrated with carbon emissions trading and carbon taxes to save energy and achieve corporate sustainability.

Keywords: energy saving; precision casting; geometric simplification; PLM; mold flow analysis; process automation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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