Data-driven production control for complex and dynamic manufacturing systems

作者：Frazzon Enzo M^*; Kueck Mirko; Freitag Michael

来源：CIRP Annals - Manufacturing Technology, 2018, 67(1): 515-518.

DOI：10.1016/j.cirp.2018.04.033

• Manufacturing systems
• Optimization
• Adaptive control

Digitalization allows for production control based on the current state of the manufacturing system. Thereof, this paper proposes and applies a data-driven adaptive planning and control approach that uses simulation-based optimization to determine most suitable dispatching rules in real-time under varying conditions. The data integration between the real manufacturing system and the simulation model is implemented through a data-exchange framework. The approach is evaluated in a scenario of a Brazilian manufacturer of mechanical components for the automotive industry, achieving better operational performance compared to the procedure previously applied by the company as well as in comparison to static dispatching rules.

• 出版日期2018