Abstract
To realize the increasing automation and flexibilization of production, it is necessary to monitor component-specific characteristics under fluctuating production conditions. Signals with a high correlation to the process quality have to be evaluated. In machining, the process force is an important measurand, which is sensitive to changes in the process. Feeling machines with force-sensitive machine tool components are therefore a promising signal source to monitor the machining. However, the force is also sensitive to non-critical process fluctuations such as stock allowance. Consequently, it is necessary to perform signal pre-processing and generate features that increase the robustness of the monitoring. In this paper, the material-specific cutting force was investigated for the first time concerning its suitability for process monitoring of parts with a stock allowance. The sensitivity of confidence limits was evaluated based on the normed bandgap. For the investigation, face turning processes of 20MnCr5 were carried out. The results show that the use of material-specific cutting force improves the sensitivity of the confidence limits to process errors. In this context, the feeling machine can be used to substitute the dynamometer for process monitoring.
Highlights
In production, the aim is to increase product quality and reduce costs in manufacturing by full automation of manufacturing and autonomous machining processes
Rehorn et al estimated that for machine tools with computer numerical control (CNC), downtime can be reduced by 20% and productivity can be increased by up to 50% by integrating a process monitoring system
For the first time, the feeling machine and the dynamometer are compared with regard to their suitability for monitoring components with dimensional deviations
Summary
The aim is to increase product quality and reduce costs in manufacturing by full automation of manufacturing and autonomous machining processes. In this context, process monitoring systems are an important part of modern production plants. Process monitoring systems are an important part of modern production plants They protect machines and machine operators from damage, reduce downtime and improve workpiece quality by eliminating, e.g., chatter [1,2]. Rehorn et al estimated that for machine tools with computer numerical control (CNC), downtime can be reduced by 20% and productivity can be increased by up to 50% by integrating a process monitoring system. The integration of monitoring systems will reduce personnel requirements and related costs
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