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A selective assembly strategy to improve mechanical product quality based on Bayesian network and genetic algorithm

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Abstract

Selective assembly is a quality improvement strategy that can be used to obtain higher-quality product assemblies by using relatively low-quality components. Past selective assembly research has often focused on quality characteristics (QCs) that have linear relations. However, there are many nonlinear QCs relations in mechanical products. For this situation, a QCs relations model based on the Bayesian network is introduced. The product quality can be predicted at an early stage based on the Bayesian network. To analyze the relations between the QCs, the QCs were categorized into three types: initial quality characteristic (IQC), target quality characteristic (TQC), and middle quality characteristic. The different IQCs can lead to different product qualities. When many products are manufactured in a batch, the product quality can be improved by the selective assembly. It means to optimize the IQC combinations. A quality improvement model based on genetic algorithm is proposed to optimize the IQC combinations. A translation mechanism is used to demonstrate the effectiveness of the proposed method. Results show that the proposed model is well suited for the quality improvement of mechanical products.

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All the data and materials in this study are available upon reasonable request.

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Funding

This work was supported by the (i) project of Intelligent Manufacturing New Schema, (ii) National Project of High-end CNC machine, (iii) National High Technology Research and Development Program of China (863 Program) (No 2015AA042101), and (iv) Joint project for graduate students of Beijing higher education institutions (No. BJ2017-BH003).

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Authors and Affiliations

  1. School of Mechanical Engineering & Automation, Beihang University, Beijing, 100191, People’s Republic of China

    Taotao Liu & Guijiang Duan

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  1. Taotao Liu
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  2. Guijiang Duan
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Contributions

Taotao Liu: conceptualization, methodology, original draft preparation, review, and editing. Guijiang Duan: methodology, project administration, review, and editing.

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Correspondence to Guijiang Duan.

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Liu, T., Duan, G. A selective assembly strategy to improve mechanical product quality based on Bayesian network and genetic algorithm. Int J Adv Manuf Technol 116, 3619–3634 (2021). https://doi.org/10.1007/s00170-021-07720-0

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