A cost-driven process planning method for hybrid additive–subtractive remanufacturing

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.