Tool Life Prediction of Ti [C,N] Mixed Alumina Ceramic Cutting Tool Using Gradient Descent Algorithm on Machining Martensitic Stainless Steel

Senthil Kumar A, Joseph Daniel S

Abstract


In automated manufacturing systems, most of the manufacturing processes, including machining, are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Tool life model was developed using Gradient Descent Algorithm. The accuracy of the machine learning model was tested using the test data, and 99.83% accuracy was obtained.


Keywords


Gradient descent algorithm, machine learning, machining, prediction, tool life model.

Full Text:

PDF

References


Rahul, R., Alok Kumar, D, “A review on cutting of industrial ceramic materials”, Precision Engineering, vol. 59, pp. 90-109, 2019.

Fei, Y.H., Huang, C.Z., Liu, H.L., Zou, B., “Mechanical properties of Al2O3–TiC–TiN ceramic tool materials”, Ceramic International, vol. 40 (7), pp. 10205-10209, 2014.

Ronald L. K., and Donald R. H., High chromium ferritic and martensitic steels for nuclear applications, ASTM International, West Conshohocken, Pennsylvania, USA, pp. 5-27, 2001.

Mikołajczyka, T., Nowickib, K., Bustilloc, A., Yu Pimenovd, D., “Predicting tool life in turning operations using neural networksand image processing”, Mechanical Systems and Signal Processing, vol. 104, pp. 503 – 513, 2018.

Gouarir, A., Martínez-Arellano, G., Terrazas, G., Benardosand, P., Ratchev, S., “In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis”, Procedia CIRP, vol. 77, pp. 501 – 504, 2018.

Wu, X., Liu, Y., Zhou, X., and Mou, A.,“Automatic Identification of Tool Wear Based on Convolutional Neural Network in FaceMilling Process”, Sensors, vol. 19, pp. 3817, 2019.

Karandikar, J., “Machine learning classification for tool life modeling using production shop-floor tool wear data”, Procedia Manufacturing, vol. 34, pp. 446 – 454, 2019.

Schwenzer, M., Miura, K., and Bergs, T., “Machine Learning for Tool Wear Classification in Milling Based on Force and Current Sensors”, IOP Conference Series Materials Science and Engineering Vol. 520, 2019.

Hui, Y., Mei, X., Jiang, G., Tao, T., Pei, C., and Ma, Z., ”Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model”, Shock and Vibration, vol. 2019, pp. 7386523, 2019.

Neef, B., Bartels, J., and Thiede, S., “Tool Wear and Surface Quality “Monitoring Using High Frequency CNC Machine Tool Current Signature”, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 2018.

Wu,D., Jennings, C., Terpenny, J., Kumara, S., and Gao, R.X., “Cloud-Based Parallel Machine Learning for Tool Wear Prediction”, Journal of Manufacturing Science and Engineering, vol. 140(4), pp. 041005, 2018.

Mart ́ınez-Arellano, G., Terrazas, G., and Ratchev, S., “Tool wear classification using time series imaging and deep learning”, The International Journal of Advanced Manufacturing Technology, vol. 104, pp. 3647 – 3662, 2019.

Kilundua, B., Dehombreuxa, P., and Chiementin, X., “Tool wear monitoring by machine learning techniques and singular spectrum analysis”, Mechanical Systems and Signal Processing, vol. 25, pp. 400 – 415, 2019.




DOI: http://dx.doi.org/10.17977/um016v4i22020p144

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Journal of Mechanical Engineering Science and Technology (JMEST)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


View My Stats