A STUDY OF MACHINING PROCESS POWER MONITORING AND PRODUCT QUALITY PREDICTION

摘要

The adoption of power sensor and power data analysis techniques has been expanding in the area of machine condition monitoring. Besides typical power usage analytics, machine health status and component degradation are the emerging merits of power data to provide more insights in the machine and process performance. This paper presents a methodology to monitor power consumption of a milling process and predict part quality based on a correlation model developed. A power sensor is instrumented at the main power supply of a three-axis horizontal milling center to manufacture a batch of typical aerospace components having a circular boss and bore features. A batch of 48 components is produced, where tool wear, product quality, power consumption and realtime machining parameters are measured and monitored. The tool change is performed based on quality requirements and tolerance information. The boss and bore diameter is measured for each part using on-machine probing and compared with its nominal value, wherein the difference is used as the part quality metric. Effective power data in kilowatt from all cycles is analyzed and meaningful features are extracted from the power signal. The feature deviations from the baseline are used to interpret the performance degradation of each tool over cycles. The deviation trend is considered as correlated with the change in the part quality, verifying that power data and its features can be used to infer the part quality using correlation model. In the future, the presented work can be validated with further testing and improved to be adaptive with multiple manufacturing process regimes. To conclude, the framework of using power data to predict machine performance in terms of health condition and part quality is highly beneficial to manage maintenance, scheduling and product quality.