Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

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Audio-Based Tool Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4040874 ◽

2018 ◽

Vol 140 (11) ◽

Cited By ~ 8

Author(s):

Achyuth Kothuru ◽

Sai Prasad Nooka ◽

Rui Liu

Keyword(s):

Tool Wear ◽

Condition Monitoring ◽

Tool Condition Monitoring ◽

Classification Model ◽

Support Vector ◽

Workpiece Material ◽

Artificial Intelligent ◽

Tool Condition ◽

Hardness Variation ◽

Audible Sound

Machining industry has been evolving toward implementation of automation into the processes for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence of the nonuniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective tool condition monitoring (TCM) system to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process are analyzed by state-of-the-art artificial intelligent techniques, support vector machine (SVM) and convolutional neural network (CNN), to predict the tool wear and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and the hardness variation of the workpiece. This study also involves the comparative analysis between two employed artificial intelligent techniques to evaluate the performance of the model in prediction. The proposed TCM system has shown a high prediction accuracy in detecting the tool wear from the audible sound into the proposed multiclassification wear level in end milling of the nonuniform hardened workpiece.

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A comparison of artificial neural networks and support vector machines for short-term load forecasting using various load types

2017 IEEE Manchester PowerTech ◽

10.1109/ptc.2017.7980814 ◽

2017 ◽

Cited By ~ 6

Author(s):

Glen Mitchell ◽

Sanjay Bahadoorsingh ◽

Neil Ramsamooj ◽

Chandrabhan Sharma

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Machines ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Vector Machines ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Load Types

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Sensitivity analysis for time lag selection to forecast seasonal time series using Neural Networks and Support Vector Machines

The 2010 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2010.5596890 ◽

2010 ◽

Cited By ~ 14

Author(s):

Paulo Cortez

Keyword(s):

Neural Networks ◽

Time Series ◽

Sensitivity Analysis ◽

Support Vector Machines ◽

Time Lag ◽

Support Vector ◽

Seasonal Time Series ◽

Lag Selection ◽

Vector Machines

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Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽

10.1371/journal.pone.0257901 ◽

2021 ◽

Vol 16 (10) ◽

pp. e0257901

Author(s):

Yanjing Bi ◽

Chao Li ◽

Yannick Benezeth ◽

Fan Yang

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Support Vector Machines ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Computer Assisted ◽

Long Distance ◽

Relationship Analysis ◽

Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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