2020
DOI: 10.1109/access.2020.2982800
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An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

Abstract: Immediate monitoring of the conditions of the grinding wheel during the grinding process is important because it directly affects the surface accuracy of the workpiece. Because the variation in machining sound during the grinding process is very important for the field operator to judge whether the grinding wheel is worn or not, this study applies artificial intelligence technology to attempt to learn the experiences of auditory recognition of experienced operators. Therefore, we propose an intelligent system … Show more

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Cited by 61 publications
(29 citation statements)
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“…Due to the highly coupled and complex non-linear relationship between the discharge parameters (included electronic and non-electric parameters) and the machining efficiency in the EDM process, it is difficult to establish a general numerical simulation model for selecting the best machining parameters [26]. Recently, AI technologies have demonstrated their outstanding performance in various fields and are continuously emerging [27]- [28]. In EDM field domain, AI technologies can effectively integrate and analyze the tacit knowledge in the EDM process to have experts' analysis ability [7].…”
Section: Process Monitoring and Controlmentioning
confidence: 99%
See 1 more Smart Citation
“…Due to the highly coupled and complex non-linear relationship between the discharge parameters (included electronic and non-electric parameters) and the machining efficiency in the EDM process, it is difficult to establish a general numerical simulation model for selecting the best machining parameters [26]. Recently, AI technologies have demonstrated their outstanding performance in various fields and are continuously emerging [27]- [28]. In EDM field domain, AI technologies can effectively integrate and analyze the tacit knowledge in the EDM process to have experts' analysis ability [7].…”
Section: Process Monitoring and Controlmentioning
confidence: 99%
“…Deep learning has recently become a trendy research topic in the AI field [27]- [28], [34]- [35]; it solves the core problems in representation learning by expressing simpler representations, thereby enabling computers to construct complex concepts from simpler concepts [36]. Generally speaking, a deep neural network is one kind of ANN network architecture; it refers to a feedforward neural network with more than one hidden layer.…”
Section: Classificationmentioning
confidence: 99%
“…Table VII shows the prediction accuracy of each classifier using the F1 score (8), a way to calculate accuracy using the confusion matrix. The F1 score accounts for both precision (9) and recall (10) to reflect accuracy of this model in a balanced way [46]. Figure 11 shows the confusion matrix of the testing data for each classifier.…”
Section: A Identification Of Grinding Wheels With Different Gradesmentioning
confidence: 99%
“…The activation layer performs nonlinear conversion through the activation function to obtain the features. Commonly seen activation functions include the Sigmoid (24) and ReLU (25). However, the Sigmoid function has a gradient loss problem.…”
Section: B Support Vector Machinementioning
confidence: 99%
“…The first involves a Finite Impulse Response Filter (FIR) [13], [14] for signal decomposition after which feature extraction is done using Approximate Entropy (ApEn) [15]- [17].The second approach uses a fractional-order Chen-Lee Chaotic system [18], [19] to conduct nonlinear feature mapping and the Chaotic Dynamic Error Centroid and Chaotic Dynamic Error Maps are selected as features of status identification. Finally, the feature extraction data obtained by these two different methods are used for identification by (1) a Back Propagation Neural Network (BPNN) [20], [21], (2) a Support Vector Machine (SVM) [22], [23] and (3) a Convolutional Neural Network (CNN) [24], [25] respectively, to find the most suitable classification model and feature extraction method for signal testing.…”
Section: Introductionmentioning
confidence: 99%