TY - JOUR
T1 - Multistage Parameter Optimization for Rule Generation for Multistage Manufacturing Processes
AU - Wahyuni, Ida
AU - Chang, Chin Chun
AU - Yang, Hua Sheng
AU - Wang, Wei Jen
AU - Liang, Deron
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - 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%.
AB - 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%.
KW - Fabric manufacturing
KW - industrial data mining
KW - multiple type defects
KW - multistage manufacturing processes
KW - parameter optimization in value range
UR - http://www.scopus.com/inward/record.url?scp=85173400389&partnerID=8YFLogxK
U2 - 10.1109/TII.2023.3312408
DO - 10.1109/TII.2023.3312408
M3 - 期刊論文
AN - SCOPUS:85173400389
SN - 1551-3203
VL - 20
SP - 3857
EP - 3867
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 3
ER -