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

Remanufacturing is one of the most effective ways to deal with the current global waste crisis; moreover, it can improve the production and resource efficiency of remanufacturing, which is an urgent need for global sustainable development. Therefore, in this study, a data-driven intelligent control system is proposed to improve the production and resource efficiency of remanufacturing assembly systems. An optimization model of the reassembly scheme is constructed to minimize quality loss and comprehensive cost. Then, based on the data acquisition and processing technology, the remanufacturing parts are measured, grouped, and coded, and the dimensional chain calculated. Then, a control method, which is a real-time monitoring and dynamic compensation response to abnormal quality, is proposed to achieve intelligent control of the remanufacturing assembly process. Moreover, some data-driven technologies of intelligent control systems that include information perception and fusion technology and real-time monitoring and dynamic compensation architecture are researched and implemented. Lastly, the intelligent control prototype system is used in a remanufacturing engine assembly workshop. Both theoretical and experimental results demonstrate that the data-driven intelligent control system in remanufacturing assembly is effective, thus providing a method and technical support for production and resource efficiency in reassembly systems.