Version 1
: Received: 17 July 2021 / Approved: 19 July 2021 / Online: 19 July 2021 (10:35:22 CEST)
How to cite:
Chen, S.; Gao, M. A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction. Preprints2021, 2021070401. https://doi.org/10.20944/preprints202107.0401.v1
Chen, S.; Gao, M. A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction. Preprints 2021, 2021070401. https://doi.org/10.20944/preprints202107.0401.v1
Chen, S.; Gao, M. A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction. Preprints2021, 2021070401. https://doi.org/10.20944/preprints202107.0401.v1
APA Style
Chen, S., & Gao, M. (2021). A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction. Preprints. https://doi.org/10.20944/preprints202107.0401.v1
Chicago/Turabian Style
Chen, S. and Min-Sheng Gao. 2021 "A Study of the Effect of Combining Multi-Sensor Signals and Cutting Chip Color on Tool Life Prediction" Preprints. https://doi.org/10.20944/preprints202107.0401.v1
Abstract
Often, engineers with machining experience often judge machining state and tool life according to chips’ features. Engineers' experience is digitized in this study. During the cutting process, the cutting tool coming in contact with the workpiece produces a shear zone, which causes plastic deformation and shear slip. The chips closest to the shear zone can directly show the state of the tool and workpiece when the material is SKD61. This study used chip color, vibration, and current signal integration for prediction of machining state and cutting tool life. When the cutting tool wears increased, the chip surface color changed in the following way: purpleè purple blueè blue ècyan, or even green and yellow. When the cutting tool was in the accelerating wear phase, the color change was particularly obvious. The Back-Propagation Levenberg–Marquardt (BP-LM) predictive methodology was used to compare the predictive ability of voltage, vibration signal, and chip color. The Mean Absolute Percentage Error (MAPE) for the voltage signal was 12.28%, for the vibration signal it was 11.38%, and for the chip color combined with multi-sensor characteristics it was 7.85%. The MAPE of the chip color was the smallest. Using the General Regression Neural Network (GRNN) methodology, the MAPE for the voltage signal was 10.74%, for the vibration signal 7.96%, and for the chip color combined with multi-sensor characteristics was 6.59%. The MAPE of the chip color was the smallest. Obviously, the chip color combined with multi-sensor signals provided better predictive results than the vibration signal or voltage signal alone. There is currently no research on the usefulness of monitoring chip color for tool life prediction.
Keywords
Cutting Tool Wear; Chip Color; Color Correction; Multi-sensor
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
