A data-driven framework for learning the capability of manufacturing process sequences. (July 2022)

• Record Type: Journal Article
• Authors: Zhao, Changxuan; Dinar, Mahmoud; Melkote, Shreyes N.
• Is Part Of: Journal of manufacturing systems. Volume 64(2022)
• Page Start: 68
• Page End: 80
• Publication Date: 2022-07
• Subjects: Manufacturing process capability -- Data-driven -- Sequence mining; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.05.009
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Automatically acquiring knowledge of manufacturing process capabilities described in terms of the part shapes they can produce, materials they can process, and part qualities they can generate is necessary to enable on-demand cyber manufacturing. This paper aims to present a novel data-driven framework to (i) identify the sequences of processes used to manufacture a discrete part, and (ii) describe their capabilities via quantifiable descriptors of shape, material properties, and part quality. Specifically, given existing manufacturing data consisting of different parts and their corresponding manufacturing methods, the proposed framework utilizes a sequence mining algorithm to identify frequently occurring sequence patterns of different manufacturing processes. In addition, the manufacturing capability of each sequence pattern is described quantitatively in terms of the achievable shapes, material properties, and part quality metrics. Such manufacturing process capability descriptions can be queried to obtain suggestions of feasible process sequences with the capability to manufacture a new part design. An exemplar implementation of the proposed framework with a curated manufacturing dataset is given to illustrate how the framework can enable manufacturing process selection and planning. The high Confidence Rates achieved by the proposed framework show its predictive strength for use in Computer Aided Process Planning (CAPP). Highlights: A data-driven framework identifies manufacturing sequence capabilities. A sequence mining algorithm identifies frequently occurring manufacturing process sequences. Manufacturing capabilities are described in terms of shape, material properties, and part quality. A data-driven framework suggests manufacturing sequences to achieve new part designs.