Tool condition monitoring by SVM classification of machined surface images in turning

Bhat, Nagaraj; Dutta, Samik; Vashisth, Tarun; Pal, Srikanta; Pal, Surjya K.; Sen, Ranjan

doi:10.1007/s00170-015-7441-3

Cited by 61 publications

(23 citation statements)

References 45 publications

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“…However, the self-prediction of the 1-D CNN model is not found over the range of Ra values available here. Figure 14a,c correspond to datasets of numbers (4,14,24,34,44) and (6,16,26,36,46), respectively. RMSE ∑ ,…”

Section: Performance Of the Three Applied Modelsmentioning

confidence: 99%

“…The five testing datasets are regularly chosen in each of the intervals, and the remaining 45 datasets are used for training. For example, datasets numbered (4,14,24,34,44) and (6,16,26,36,46) are selected as the testing datasets. The training results corresponding to FFT-DNN, FFT-LSTM, and 1-D CNN using the remaining datasets are displayed in Figure 14, Figure 15, and Figure 16, respectively.…”

Section: Performance Of the Three Applied Modelsmentioning

confidence: 99%

“…Finally, cross-validation for testing is performed to evaluate the predictive performance of the surface roughness via vibration signal datasets after training datasets. Using the afore-described methods for choosing the test dataset numbers, dataset numbers (3,13,23,33,43), (4,14,24,34,44), (5,15,25,35,45), (6,16,26,36,46), and (7,17,27,37,47) are chosen for testing the datasets to account for cross-validation in the testing process. The mean error results of the dataset cross-validation is provided to evaluate the predictive accuracy of the trained model.…”

Section: -D Cnnmentioning

confidence: 99%

“…The methods most commonly used for feature extraction methods are time-domain, frequency domain, and time-frequency domain methods. The extracted features are fed into the classifier, such as a support vector machine (SVM) [26][27][28] or a neural network (NN) [29,30]. N. N. Bhat [26] proposed the use of the SVM technique for classifying tool wear states of surface images.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

et al. 2019

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The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.

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Section: Performance Of the Three Applied Modelsmentioning

confidence: 99%

Section: Performance Of the Three Applied Modelsmentioning

confidence: 99%

Section: -D Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

et al. 2019

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“…Prediction of the surface roughness of a workpiece by using adaptive neuro-fuzzy inference system (ANFIS) modeling for the monitoring of unmanned production systems with tool-life management is presented in [3]. In other studies, machined surface images are analyzed on the basis of a support vector machine using as input the features extracted from the gray-level co-occurrence matrix [4], and in-process surface-roughness monitoring system for an end-milling operation is analyzed using neural-fuzzy methods in [5]. Another parameter monitored in order to identify tool wear is the cutting force.…”

Section: Introductionmentioning

confidence: 99%

Tool-Wear Analysis Using Image Processing of the Tool Flank

et al. 2017

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Flexibility of manufacturing systems is an essential factor in maintaining the competitiveness of industrial production. Flexibility can be defined in several ways and according to several factors, but in order to obtain adequate results in implementing a flexible manufacturing system able to compete on the market, a high level of autonomy (free of human intervention) of the manufacturing system must be achieved. There are many factors that can disturb the production process and reduce the autonomy of the system, because of the need of human intervention to overcome these disturbances. One of these factors is tool wear. The aim of this paper is to present an experimental study on the possibility to determine the state of tool wear in a flexible manufacturing cell environment, using image acquisition and processing methods.

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