A cost-driven process planning method for hybrid additive–subtractive remanufacturing. (April 2020)

• Record Type: Journal Article
• Authors: Zheng, Yufan; Liu, Jikai; Ahmad, Rafiq
• Is Part Of: Journal of manufacturing systems. Volume 55(2020)
• Page Start: 248
• Page End: 263
• Publication Date: 2020-04
• Subjects: Remanufacturing -- Hybrid manufacturing -- Process planning -- Cost-minimization optimization -- Feature extraction; 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.2020.03.006
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A novel cost minimization process planning is developed for remanufacturing. An automated hybrid manufacturing feature extraction-based algorithm is proposed. Precedence constraints and cost models of hybrid remanufacturing are formulated. Abstract: Hybrid manufacturing combines additive manufacturing's advantages of building complex geometries and subtractive manufacturing's benefits of dimensional precision and surface quality. This technology shows great potential to support repairing and remanufacturing processes. Hybrid manufacturing is used to repair end-of-life parts or remanufacture them to new features and functionalities. However, process planning for hybrid remanufacturing is still a challenging research topic. This is because current methods require extensive human intervention for feature recognition and knowledge interpretation, and the quality of the derived process plans are hard to quantify. To fill this gap, a cost-driven process planning method for hybrid additive–subtractive remanufacturing is proposed in this paper. An automated additive–subtractive feature extraction method is developed and the process planning task is formulated into a cost-minimization optimization problem to guarantee a high-quality solution. Specifically, an implicit level-set function-based feature extraction method is proposed. Precedence constraints and cost models are also formulated to construct the hybrid process planning task as a mixed-integer programming model. Numerical examples demonstrate the efficacy of the proposed method.