Optional classification for reassembly methods with different precision remanufactured parts

Abstract

Purpose – The purpose of this study was to research how the reassembly (remanufacturing assembly) achieves a quality that is not lower than original production with different precision remanufactured parts based on the integration of mechanics, mathematics (measurement uncertainty) and management (optional classification). Remanufactured product quality is the soul of the remanufacturing project. Design/methodology/approach – First, this paper studies the recycled parts features and reassembly features. Then, we build the mathematical sub-model with remanufactured parts and dimensional precision, which is proven that optional classification can effectively improve the reassembly accuracy mathematically. The optimization model of optional classification for reassembly is proposed under the constraint of a dimensional chain, and the solutions are studied based on particle swarm optimization. Finally, this method is applied in a remanufacturing enterprise and achieves good results. Findings – The method can reduce the cost of quality loss and improve the quality of remanufactured products. Originality/value – It provides a new solution and idea for reassembly with different precision remanufactured parts and promotes the healthy development of reverse logistics with a high level of customer satisfaction. This method can maximize the use of different levels of quality remanufactured parts and improve reassembly accuracy by mathematical proofs and examples.