Detecting Low-Yield Machines in Batch Production Systems Based on Observed Defective Pieces

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 54; no. 7; pp. 3972 - 3983
• Main Authors: Adipraja, Philip F. E., Chang, Chin-Chun, Yang, Hua-Sheng, Wang, Wei-Jen, Liang, Deron
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batch production; Batch production systems; expectation–maximization (EM) algorithm; machine maintenance suggestion; Maintenance engineering; Maximum likelihood estimation; per-machine yield estimation; Production; Prognostics and health management; Reliability; Yield estimation
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2024.3374393

In batch production systems, detecting low-yield machines is essential for minimizing the production of defective pieces, which is a complex problem that currently requires multiple experts, considerable capital, or a combination of both to overcome. To solve this problem, we proposed a cost-efficient and straightforward method that involves using maximum likelihood estimation and bootstrap confidence intervals to estimate per-machine yield; this method enables identification of low-yield machines and generation of a list of these machines. Manufacturing engineers can use the list to perform necessary verification and maintenance processes. Before implementing this method, a manufacturer with 50-500 machines should build a dataset containing approximately 6-20 times as many batches as there are production machines. When this condition is met, the proposed method can be used effectively to detect up to five low-yield machines.