IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p12683-d1222289.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tzung-Ming Chen & Jia-Qi Wu & Jian-Ting Lin, 2023. "Classification of 3D Casting Models for Product Lifecycle Management and Corporate Sustainability," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12683-:d:1222289
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/12683/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/12683/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xu, Qiong & Zhong, Meirui & Li, Xin, 2022. "How does digitalization affect energy? International evidence," Energy Economics, Elsevier, vol. 107(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shunbin Zhong & Huafu Shen & Ziheng Niu & Yang Yu & Lin Pan & Yaojun Fan & Atif Jahanger, 2022. "Moving towards Environmental Sustainability: Can Digital Economy Reduce Environmental Degradation in China?," IJERPH, MDPI, vol. 19(23), pages 1-23, November.
    2. Rongwu Zhang & Wenqiang Fu & Yingxu Kuang, 2022. "Can Digital Economy Promote Energy Conservation and Emission Reduction in Heavily Polluting Enterprises? Empirical Evidence from China," IJERPH, MDPI, vol. 19(16), pages 1-21, August.
    3. Ding, Qian & Huang, Jianbai & Chen, Jinyu & Tao, Dali, 2023. "Internet development and renewable energy technological innovation: Does institutional quality matter?," Renewable Energy, Elsevier, vol. 218(C).
    4. Essossinam Ali & Hodabalo Bataka & Kwami Ossadzifo Wonyra & Nadège Essossolim Awade & Nèmè Nalèwazou Braly, 2024. "Global value chains participation and environmental pollution in developing countries: Does digitalization matter?," Journal of International Development, John Wiley & Sons, Ltd., vol. 36(1), pages 451-478, January.
    5. Pingkuo Liu & Jiahao Wu, 2023. "Game Analysis on Energy Enterprises’ Digital Transformation—Strategic Simulation for Guiding Role, Leading Role and Following Role," Sustainability, MDPI, vol. 15(13), pages 1-33, June.
    6. Wang, Bo & Wang, Jianda & Dong, Kangyin & Nepal, Rabindra, 2024. "How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society," Energy Policy, Elsevier, vol. 186(C).
    7. Lee, Chien-Chiang & He, Zhi-Wen & Xiao, Fu, 2022. "How does information and communication technology affect renewable energy technology innovation? International evidence," Renewable Energy, Elsevier, vol. 200(C), pages 546-557.
    8. Rabindra Nepal & Yang Liu & Kangyin Dong & Tooraj Jamasb, 2024. "Green Financing, Energy Transformation, and the Moderating Effect of Digital Economy in Developing Countries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(12), pages 3357-3386, December.
    9. Zhang, Yijun & Meng, Zhenzhen & Song, Yi, 2023. "Digital transformation and metal enterprise value: Evidence from China," Resources Policy, Elsevier, vol. 87(PB).
    10. Henryk Dzwigol & Aleksy Kwilinski & Oleksii Lyulyov & Tetyana Pimonenko, 2024. "Digitalization and Energy in Attaining Sustainable Development: Impact on Energy Consumption, Energy Structure, and Energy Intensity," Energies, MDPI, vol. 17(5), pages 1-17, March.
    11. Zhao, Shuang & Zhang, Liqun & Peng, Lin & Zhou, Haiyan & Hu, Feng, 2024. "Enterprise pollution reduction through digital transformation? Evidence from Chinese manufacturing enterprises," Technology in Society, Elsevier, vol. 77(C).
    12. Shuangcheng Luo & Yangli Yuan, 2023. "The Path to Low Carbon: The Impact of Network Infrastructure Construction on Energy Conservation and Emission Reduction," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    13. Peng, Hui & Lu, Yaobin & Wang, Qunwei, 2023. "How does heterogeneous industrial agglomeration affect the total factor energy efficiency of China's digital economy," Energy, Elsevier, vol. 268(C).
    14. Dong, Kangyin & Liu, Yang & Wang, Jianda & Dong, Xiucheng, 2024. "Is the digital economy an effective tool for decreasing energy vulnerability? A global case," Ecological Economics, Elsevier, vol. 216(C).
    15. Huang, Chenchen & Lin, Boqiang, 2023. "Promoting decarbonization in the power sector: How important is digital transformation?," Energy Policy, Elsevier, vol. 182(C).
    16. Zhao, Congyu & Jia, Rongwen & Dong, Kangyin, 2023. "Does financial inclusion achieve the dual dividends of narrowing carbon inequality within cities and between cities? Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    17. Kaffo Fotio, Hervé & Timbi, Sezard & Mbiankeu Nguéa, Stéphane, 2024. "Breaking barriers, building resilience: Leveraging knowledge to mitigate energy vulnerability in Africa," Energy Policy, Elsevier, vol. 194(C).
    18. Benedetti, Ilaria & Guarini, Giulio & Laureti, Tiziana, 2023. "Digitalization in Europe: A potential driver of energy efficiency for the twin transition policy strategy," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    19. Reni Pantcheva, 2024. "Economic and Social Drivers of Renewable Energy Consumption in the European Union: An Econometric Analysis," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 62-84.
    20. Wu, Haitao & Wang, Bingjie & Lu, Mingyue & Irfan, Muhammad & Miao, Xin & Luo, Shiyue & Hao, Yu, 2023. "The strategy to achieve zero‑carbon in agricultural sector: Does digitalization matter under the background of COP26 targets?," Energy Economics, Elsevier, vol. 126(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12683-:d:1222289. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.