Condition Monitoring of Single Point Cutting Tools Based on Machine Learning Approach

Gangadhar, N.; Kumar, Hemantha; Narendranath, S.; Sugumaran, V.

doi:10.20855/ijav.2018.23.21130

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Specimen preparation is done prior to the experimentation by turning the workpiece to remove the oxide layer and achieve roundness. The machine tool is allowed to stabilize after the start of the machine tool for any random vibration if present [16,17]. The different tool conditions considered for the experiment were: The images of various tool conditions are shown in Fig.…”

Section: Classification Of Features: Rotation Forest Algorithmmentioning

confidence: 99%

Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm

2019

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In various machining operations, the tool condition monitoring (TCM) is highly necessary to avoid uncertain downtime in production. TCM provides continuously the condition of cutting tool by noticing various parameters such as temperature, acoustic emission and vibration. One of the best ways to monitor the condition of cutting tools for unmanned machining is by observing tool vibration signature. In the present work, vibration signals are acquired from the cutting tool. One healthy state and three faulty conditions of tools are considered for the study. The faulty tools considered in the current study are worn flank, broken tool and extended overhang. The vibration signals of these faulty tool conditions are used to train the machine learning algorithm. Statistical features are extracted from the vibration signal to feed as input to the J48 decision tree. The classifier algorithm used in the current study is rotation forest algorithm. The algorithm uses only significant features which are selected from a decision tree. The algorithm is validated with test dataset to recognize the faulty or healthy state of the tool. It was found that the algorithm could classify the tool condition with 95.00% classification accuracy.

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Section: Classification Of Features: Rotation Forest Algorithmmentioning

confidence: 99%

Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm

2019

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“…With this motivation, the concept of developing a Tool Condition Monitoring (TCM) system has evolved. A significant goal of TCM is to recognize the deterioration of the cutting edge, which subsequently improves the value of the job [ 14 , 15 ]. TCM generally aims at monitoring the behavior of the cutting tool in terms of machining datasets collected through various sensors such as microphones [ 16 ], dynamometers [ 17 ], accelerometers [ 18 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Belief Network for Tool Faults Recognition

Kale

Wahul

Patange³

et al. 2023

Sensors

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The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.

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“…Unidirectional Multilayer Perceptron (MLP) based on artificial neural network (ANN) is used to classify the faults in Single point cutting tools based on images generated using the histograms of the signal as proposed by Gangadhar et al. 18…”

Section: Introductionmentioning

confidence: 99%

“…These features are then fed to ML algorithms like K-nearest neighbor for the classification of faults in a wind turbine blade. Unidirectional Multilayer Perceptron (MLP) based on artificial neural network (ANN) is used to classify the faults in Single point cutting tools based on images generated using the histograms of the signal as proposed by Gangadhar et al 18 To diagnose various bearing faults using deep learning models, the availability of considerable amount of images is a big challenge. To address this issue, in the current study, SinGAN is used which is capable to generate diverse images from a single original images of various fault conditions.…”

Section: Introductionmentioning

confidence: 99%

Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

Akhenia

Bhavsar

Panchal

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Condition monitoring and diagnosis of a bearing are very important for any rotating machine as it governs the safety while the machine is in operating condition. To construct a feature vector selection of suitable signal processing techniques is a challenge for vibration-based condition monitoring techniques. In the methodology proposed, Short Time Fourier Transform (STFT), Walsh Hadamard Transform (WHT) and Variable Mode Decomposition (VMD) are used to generate 2-D time-frequency spectrograms from the various fault conditions of bearing. When Deep learning techniques apply for fault diagnosis, a large amount of dataset is required for training of machine learning model. To overcome this issue single image Generative Adversarial Network (SinGAN) as a data augmentation technique, utilized for generating additional 2-D time-frequency spectrograms from various fault conditions of ball bearing. To detect fault severity, four deep learning algorithms, ResNet 34, ResNet50, VGG16, and MobileNetV2 are used as a classifier. Experiments are conducted on a rolling bearing dataset provided by the bearing data center of Case Western Reserve University (CWRU) for validating the utility of methodology proposed. Results show that the proposed methodology enables to detect fault severity level with high classification accuracy.

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Condition Monitoring of Single Point Cutting Tools Based on Machine Learning Approach

Cited by 7 publications

References 7 publications

Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm

Fault diagnosis of single-point cutting tool using vibration signal by rotation forest algorithm

Development of Deep Belief Network for Tool Faults Recognition

Fault severity classification of ball bearing using SinGAN and deep convolutional neural network

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