Feeling Machine for Process Monitoring of Components with Stock Allowance

Author:

Denkena Berend,Bergmann Benjamin,Witt Matthias

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.

Funder

Deutsche Forschungsgemeinschaft

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Digital twin assisted intelligent machining process monitoring and control;CIRP Journal of Manufacturing Science and Technology;2024-04

2. Sensitivity of process signals to deviations in material distribution and material properties of hybrid workpieces;The International Journal of Advanced Manufacturing Technology;2023-12-19

3. Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation;Robotics and Computer-Integrated Manufacturing;2023-08

4. AI-Driven Digital Process Twin via Networked Digital Process Chain;2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech);2022-09-12

5. Process monitoring of machining;CIRP Annals;2022

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