A Smart Tool Holder Calibrated by Machine Learning for Measuring Cutting Force in Fine Turning and Its Application to the Specific Cutting Force of Low Carbon Steel S15C
| Autor: | Teng-Shan Hu, Yuh-Chung Hu, Liang-Wei Tseng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Accuracy and precision
piezoresistive force sensor Control and Optimization business.product_category Computer science neural network intelligent machine Mechanical engineering warm-up shift calibration turning tool holder Industrial and Manufacturing Engineering law.invention Bluetooth Software Machining law Computer Science (miscellaneous) TJ1-1570 Mechanical engineering and machinery Electrical and Electronic Engineering Dynamometer Artificial neural network business.industry Mechanical Engineering dynamometer Piezoresistive effect Machine tool machine learning Control and Systems Engineering cutting force machine tool business CNC lathe |
| Zdroj: | Machines, Vol 9, Iss 190, p 190 (2021) Machines Volume 9 Issue 9 |
| ISSN: | 2075-1702 |
| Popis: | Real-time monitoring of the cutting force in the machining process is critical for improving machining accuracy, optimizing the machining process, and optimizing tool lifetime however, the dynamometers are too expensive to be widely used by machine tool users. Therefore, this paper presents a simple and cheap apparatus—a smart tool holder—to measure the cutting force of turning tools in the finishing turning. The apparatus does not change the structure of the turning tool. It consists of a tool holder and a piezoresistive force sensor foil, and transmits the signal through Bluetooth wireless communication. Instead of dealing with the circuit hardware, this paper uses the Artificial Neural Network (ANN) model to successfully calibrate the warm-up shift problem of the piezoresistive force sensor. Such a software method is simple, and considerably cheaper than the hardware method. For the force measurement capability of the smart tool holder, the cross-interference between orthogonal forces are very small and thus can be ignored. The force reading of the smart tool holder possesses high repeatability for the same turning parameters and high accuracy within the experiment groups. The authors apply the smart tool holder to cut the low carbon steel S15C, and to determine its specific cutting force in fine turning. The resulting fine turning force model agrees very well with the measurement. Its mean absolute deviation is 3.87% and its standard deviation is 1.55%, which reveals that the accuracy and precision of the smart tool holder and the fine turning force model are both good. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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