Machine Learning to Empower a Cyber-Physical Machine Tool
| Autor: | Yuqian Lu, Xun Xu, Max Rogers, Flynn Letford |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Polynomial regression
0209 industrial biotechnology business.product_category Artificial neural network business.industry Computer science Cyber-physical system 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre Random forest Machine tool Data set Support vector machine 020901 industrial engineering & automation Artificial intelligence Tool wear 0210 nano-technology business computer |
| Zdroj: | CASE |
| DOI: | 10.1109/case48305.2020.9216842 |
| Popis: | Machine learning is used to empower a machine tool, which gives rise to a new generation machine tool, i.e. cyber-physical machine tool. The use of four sensors to measure the cutting force, vibration, acoustic emission, and spindle motor current of an end milling machine is proposed. Sixty-five cutting tests using an end milling machine were conducted, during which sensor data was recorded. The flank wear exhibited on the tool following each cut was then measured using a microscope. This provided a labelled data set on which to train four machine learning algorithms: Support Vector Regression, Random Forests, Feed-Forward Back-Propagation Artificial Neural Networks, and Polynomial Regression. These were then compared and it was found that an artificial neural network provides the most accurate predictions of tool flank wear, with a mean absolute percentage accuracy of 90.11%. Using this trained neural network model, a real-time tool wear prediction system was implemented in LabVIEW. This tool condition monitoring system can be used to increase efficiency of manufacturing processes |
| Databáze: | OpenAIRE |
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