Abstract
Complex product design often relies on design experience and numerous repeated equipment operations. In order to speed up the design process on a complicated product, a methodology of knowledge extraction (KE) is proposed. Moreover, a case study in designing a shaped boss using the KE technique in conjunction with a Back-Propagation Network (BPN) method as well as a genetic algorithm (GA) will be introduced. The results indicate that the prediction error between learned and examined data is found to be within 8%. Moreover, the error between the GA’s solution and the specific target is also found to be within 5%. Therefore, the bi-directional prediction scheme constructed in this project is deemed to be reliable. Consequently, knowledge extraction can provide a rapid and economical way to design and shape a complicated product.
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Chiu, MC., Lan, TS., Cheng, HC. (2020). Plastic Boss Design Using Knowledge Extraction Method. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_64
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DOI: https://doi.org/10.1007/978-3-030-32591-6_64
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