Energy efficient machining through evolutionary real-time optimization of cutting conditions on CNC-milling controllers

Optimizing the use of manufacturing resources is vital for any engineering enterprise. Modern responsible industry is also taking increasingly the environmental impact into account. In milling the correct selection of cutting conditions can help minimizing the energy consumption, thus achieving a more sustainable operation. This paper presents a novel approach of applying on-line on-board multi-objective optimization techniques for adaptive improvement of CNC milling processes through IEC 61499 standardized Function Blocks running on an industrial CNC machine controller. The results show that it is possible to run even complex advanced evolutionary optimization algorithms on modern CNC machines in real-time. The case study also demonstrates that this approach can reduce up to 25% of the peak energy demand and 12% of cutting time when compared to conventional non optimized solutions.