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Robust Design Based on Cost-Quality Model in Micro-Manufacturing
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Business School, Yangzhou University, China
School of Business, Anhui University, China
Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, China
School of Information Management, Jiangxi University of Finance and Economics, China
Submission date: 2024-02-18
Final revision date: 2024-04-13
Acceptance date: 2024-06-23
Online publication date: 2024-07-07
Publication date: 2024-07-07
Corresponding author
Jiawei Wu   

School of Information Management, Jiangxi University of Finance and Economics, 330013, Yangzhou, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(3):190380
  • We propose a novel economically quality design model under model parameter uncertainty.
  • The model covers both pre-sale manufacturing and post-sale warranty costs.
  • Warranty cost models considering model parameters uncertainty have been constructed.
  • In modeling process trade-offs between cost and quality are considered.
  • A micro-drilling manufacturing process validates the effectiveness of the method.
This paper proposes a novel total cost model for the micro‐products' entire life cycle that takes into account the uncertainty of the model parameters. The total cost includes pre-sale manufacturing and post-sale warranty costs. Additionally, different marketing strategies are also given based on the weight of internal and external costs. Furthermore, limited data and unknown effects in experiments may cause large errors in parameter estimates. This could prevent the achievement of reliable designs. To address this, robust optimization and interval estimation are used. This approach reduces the impact of uncertainty on parameter estimates. It ensures optimality and robustness in micro-manufacturing parameters. Example analysis and numerical simulation results show that the proposed method assists companies in selecting the optimal manufacturing parameter level that aligns with their marketing strategies. Besides, considering uncertainty factors can ensure that the optimization results remain guaranteed, even under the worst-case scenarios.
The research work was supported by grants from the National Natural Science Foundation of China (NSFC72302208, 72301002), Eksploatacja i Niezawodność – Maintenance and Reliability Vol. 26, No. 3, 2024 and the project of Philosophy, Social Science Research in Colleges and Universities in Jiangsu Province (2021SJA1974), the Ministry of Education "Chunhui Program" Cooperative Research Project (HZKY20220116), the science and technology project of Jiangxi Provincial Education Department (GJJ210528).
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