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Use of sensors to monitor dynamic performance of machine tools at Ford’s powertrain machining plants has proven to be effective. The traditional approach to convert sensor data to actionable intelligence consists of identifying single features from cycle based signatures and setting thresholds above acceptable performance limits based on trials. The thresholds are used to discriminate between acceptable and unacceptable performance during each cycle and raise alarms if necessary. This approach requires a significant amount of resource & time intensive set up work up-front and considerable trial and error adjustments. The current state does not leverage patterns that might be discernible using multiple features simultaneously. This paper describes enhanced methods for processing the data using supervised and unsupervised machine learning methods. The objective of using these methods is to improve the prediction accuracy and reduce up-front set up.

Thermal growth of spindle and other components is common in CNC machines, especially with MQL machining, and directly impacts positioning accuracy and thereby quality. A common method to address this is by measuring the thermal growth using gage bore probing and then compensating for it. Application of this method in 5-axis CNC machines is relatively new and effectiveness is often not tested. Error sources due to various orientations of the part could arise. A new artifact based method is presented for assessment of thermal compensation used by the CNC OEM. The method involves an artifact, test cycle and automated data logging. A precision granite artifact with gage bores on different faces is fabricated and can be presented for probing in different orientations. The machine is warmed up using repeated dry-cycling, thereby creating thermal growth. Compensation error is evaluated as the difference of actual thermal growth and compensation offset.