A Graph Theory-Based Optimization Design for Complex Manufacturing Processes

• Published in: IEEE access Vol. 8; pp. 95547 - 95558
• Main Authors: Han, Zhong, Cheng, Lin, Xing, Liudong, Tian, Liting
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Design optimization; Graph model; Graph theory; Machining; Manufacturing; manufacturing process; model weight; Optimization; optimization design
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2991218

The manufacturing process of modern equipment becomes very complex due to features such as mass units, multiple machining, and complicated coupling-relationships, posing a big challenge for determining the manufacturing scheme. This paper addresses the challenge by proposing a graph theory-based optimization design for the complex manufacturing process. A detailed analysis of a serial of graph models built according to the manufacturing process features reveals that the Hamilton graph is suitable for modeling the manufacturing process system. Some model weight assignment functions are extracted for the quantitative study. Further the optimal scheme for an optimization design of the complex manufacturing process is solved using the full link graph feature algorithm - a search optimization algorithm. A manufacturing model matrix is constructed, and penalty number and divisor are formulated to simplify the matrix and improve the algorithm efficiency in the process of algorithm design. An example is provided to demonstrate feasibility and effectiveness of the proposed method.