Multistage Parameter Optimization for Rule Generation for Multistage Manufacturing Processes

Ida Wahyuni, Chin Chun Chang, Hua Sheng Yang, Wei Jen Wang, Deron Liang

Research output: Contribution to journalArticlepeer-review


Defects in multistage manufacturing processes (MMPs) decrease profitability and product quality. Therefore, MMP parameter optimization within a range is essential to prevent defects, achieve dynamic accuracy, and accommodate manufacturing tolerances. However, existing studies only focused on optimization in a single manufacturing stage of MMP, such as the weaving stage in fabric manufacturing. Furthermore, existing methods optimize for a single value rather than a range. Thus, we propose a novel approach called multistage parameter optimization for rule generation (MPORG) to prevent the occurrence of defects in MMPs. In the proposed approach, key parameters are identified and optimized to a range for each defect type. Subsequently, the optimized parameters for each defect type are merged. Our approach is novel because it optimizes parameters to a range rather than a single value, allowing engineers to select a value in this range according to their experience. It also provides results that are specific to a product type. Our approach outperformed the classification and regression tree (CART) algorithm and multiresponse CART method in experiments on an empirical fabric manufacturing dataset that we gathered. The experimental results demonstrated that the MPORG approach can prevent the occurrence of single-type or multiple-type defects by approximately 89%.

Original languageEnglish
Pages (from-to)3857-3867
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number3
StatePublished - 1 Mar 2024


  • Fabric manufacturing
  • industrial data mining
  • multiple type defects
  • multistage manufacturing processes
  • parameter optimization in value range


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