Data-driven remanufacturing assembly intelligent control system for improving resource and production efficiency

Abstract

Remanufacturing has been considered to be one of the most effective ways to deal with sustainable manufacturing. This paper proposes a data-driven intelligent control system for improving the production and resource efficiency of the remanufacturing assembly systems. First, an optimization model of the reassembly scheme is established for minimizing the quality loss and comprehensive cost. Remanufactured parts are measured, grouped, coded, and dimensional chain calculated based on the data acquisition and processing technology. Then, an intelligent control method for remanufacturing assembly process is proposed, which is a real-time monitoring and dynamic compensation response to abnormal quality to achieve intelligent control of reassembly process. The intelligent control information system that include information perception and fusion technology, real-time monitoring and dynamic compensation architecture are studied and implemented through data-driven technologies. Finally, a case study illustrates its practicability offering a technical support for sustainability of remanufacturing.