A Multistage Cutting Tool Fault Diagnosis Algorithm for the Involute form Cutter Using Cutting Force and Vibration Signals Spectrum Imaging and Convolutional Neural Networks

In a machining system, tool condition monitoring systems are required to get a high-quality product and to prevent the downtime of machine tools due to tool failures. For this purpose, tool condition monitoring systems have become very important during the years since the mechanical faults can cause high cost. This study introduces a multistage cutting tool fault diagnosis method to detect the presence and level of the involute form cutter faults on the by the cutting force and vibration signal analysis. Therefore, different fault levels (low, medium and high) were generated on the involute form cutter as a tool breakage. During the experiments, the cutting force, vibration and acoustic signals were gathered with three different feed rates for each fault level. The gathered signals were processed by a multistage signal processing algorithm developed in the MATLAB environment. As an initial step, the continuous wavelet transform of the obtained signals was taken and saved as an image by the developed algorithm. After that, a convolutional neural network model is trained and tested by using the obtained images. The developed algorithm firstly checks the presence of the cutting tool fault. Once the algorithm labels the cutting tool is damaged, it then checks the damage level of the cutting tool fault. It is observed from the results, cutting force analysis is sufficient for the detection of cutting tool fault. On the other hand, the cutting force signal analysis is insufficient to detect the damage level of the cutting tool. Therefore, the vibration signal analysis is required to detect the damage level of the cutting tool. Results prove that, by the vibration analysis, the developed algorithm could detect not only the presence of the damage on the cutting tool but also the damage level. The results of the algorithm for each stage and signal are given in the results section.

[1]  Ning Li,et al.  Force-based tool condition monitoring for turning process using v-support vector regression , 2017 .

[2]  Isa Yesilyurt,et al.  End mill breakage detection using mean frequency analysis of scalogram , 2006 .

[3]  Walter Bartelmus,et al.  Advances in Condition Monitoring of Machinery in Non-stationary Operations , 2014 .

[4]  Chao Wang,et al.  Adaptive smart machining based on using constant cutting force and a smart cutting tool , 2013 .

[5]  S. Narendranath,et al.  Face milling tool condition monitoring using sound signal , 2017, Int. J. Syst. Assur. Eng. Manag..

[6]  Bo Zhou,et al.  Sequential spindle current-based tool condition monitoring with support vector classifier for milling process , 2017 .

[7]  Bülent Kaya,et al.  Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks , 2011, Adv. Eng. Softw..

[8]  Woon Kiow Lee,et al.  Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision , 2019, The International Journal of Advanced Manufacturing Technology.

[9]  Bradley Howell Jared,et al.  Investigation of the direction of chip motion in diamond turning , 2001 .

[10]  Chan-Yun Yang,et al.  Prediction of tool breakage in face milling using support vector machine , 2008 .

[11]  Marc Thomas,et al.  Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process , 2018, The International Journal of Advanced Manufacturing Technology.

[12]  Sung-Hoon Ahn,et al.  Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry , 2018, International Journal of Precision Engineering and Manufacturing-Green Technology.

[13]  Chia-Hao Kuo,et al.  A PNN self-learning tool breakage detection system in end milling operations , 2015, Appl. Soft Comput..

[14]  Rui Liu,et al.  Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling , 2017, The International Journal of Advanced Manufacturing Technology.

[15]  Mohd. Zaki Nuawi,et al.  A Review of Sensor System and Application in Milling Process for Tool Condition Monitoring , 2014 .

[16]  A. Al–Habaibeh,et al.  Self-Learning Algorithm for Automated Design of Condition Monitoring Systems for Milling Operations , 2001 .

[17]  Isa Yesilyurt,et al.  Modeling and experimental verification of cutting forces in gear tooth cutting , 2018 .

[18]  Samy E. Oraby,et al.  Tool life determination based on the measurement of wear and tool force ratio variation , 2004 .

[19]  M. Gadala,et al.  Simulation of the orthogonal metal cutting process using an arbitrary Lagrangian–Eulerian finite-element method , 2000 .

[20]  Jian Guan,et al.  Motion classification for radar moving target via STFT and convolution neural network , 2019 .

[21]  Xin Li,et al.  An STFT-LSTM System for P-Wave Identification , 2020, IEEE Geoscience and Remote Sensing Letters.

[22]  Bin Yao,et al.  ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network , 2019, IEEE Access.

[23]  V. Sugumaran,et al.  Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool , 2011, Expert Syst. Appl..

[24]  Yusuf Altintas,et al.  Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design , 2000 .

[25]  Sohyung Cho,et al.  Tool breakage detection using support vector machine learning in a milling process , 2005 .

[26]  Kai Cheng,et al.  Machining dynamics: Fundamentals, applications and practices , 2008 .