Approach to derive golden paths based on machine sequence patterns in multistage manufacturing process

Abstract

A multistage manufacturing process (MMP) consists of several consecutive process stages, each of which has multiple machines performing the same functions in parallel. A manufacturing path (simply referred to as path) is defined as an ordered set indicating a record of machines assigned to a product at each process stage of an MMP. An MMP usually produces products through various paths. In practice, multiple machines in a process stage have different operational performances, which accumulate during production and affect the quality of products. This study proposes a heuristic approach to derive the golden paths that produce products whose quality exceeds the desired level. The proposed approach consists of the searching phase and the merging phase. The searching phase extracts two types of machine sequence patterns (MSPs) from a path dataset in an MMP. An MSP is a subset of the path that is defined as an ordered set of assigned machines from several process stages. The two extracted types of MSPs are: (1) superior MSP, which affects the production of superior-quality products, and (2) inferior MSP, which affects the production of inferior-quality products, called inferior MSP. The merging phase derives the golden paths by combining superior MSPs and excluding inferior MSPs. The proposed approach is verified by applying it to a hypothetical path dataset and the semiconductor tool fault isolation (SETFI) dataset. This verification shows that the proposed approach derives the golden paths that exceed the predefined product quality level. This outcome demonstrates the practical viability of the proposed approach in an MMP.