Intelligent in-process enhancement technique for machining efficiency in CNC machine tools based on spindle power

Abstract

In the realm of mechanical machining, adaptive machining techniques offer an efficient method. However, existing adaptive machining technologies cannot automatically identify the type of workpiece to be machined, nor can they set different control targets based on tool load-bearing capacity. This often requires manual and cumbersome operations, making the application of adaptive machining technology difficult to popularize. To address these challenges, this paper proposes an intelligent and efficient adaptive machining method. Specifically, a workpiece automatic classification algorithm based on machine tool spindle power is introduced. This algorithm classifies the machined workpieces according to the collected spindle power data, enhancing the convenience of adaptive machining technology. Also based on spindle power data, a time-series segmentation method is proposed, dividing spindle power into different subsequences according to the tools used in machining, which enhances the accuracy of adaptive machining technology. Furthermore, an adaptive feed rate control algorithm is designed based on fuzzy control theory, realizing intelligent and efficient adaptive machining of workpieces to improve machining efficiency. Finally, experiments are conducted to validate the effectiveness of the intelligent and efficient adaptive machining method. The proposed method effectively addresses the drawbacks of existing adaptive machining method, which require complex parameter settings. It simplifies the operational process, enhances the intelligence, and improves the practical applicability of adaptive machining techniques. This represents an innovative contribution to the field of adaptive machining techniques.