Development of smart machining system for optimizing feedrates to minimize machining time

Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. (C) 2017 Society for Computational Design and Engineering. Publishing Services by Elsevier.