A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning

Wang, Dashuang; Hong, Rongjing; Lin, Xiaochuan

doi:10.1016/j.precisioneng.2021.08.010

Cited by 23 publications

(6 citation statements)

References 40 publications

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“…Zhang et al (2014) proposed a tool wear model using least squares support vector machines and Kalman filter, contributing significantly to predictive models for tool wear (Zhang et al, 2014). Wang et al (2021) developed a technique to forecast hobbing tool wear by leveraging CNC real-time monitoring data and applying deep learning, demonstrating deep learning methods for predicting tool conditions (Wang et al, 2021). Liu et al (2019) focused on predicting the remaining useful life of cutting tools through support vector regression, introducing a modelling approach for estimating tool lifespan (Liu et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Acoustic emission (AE) sensorsprovide valuableinsights intothe microfracture process within the tool, further enhancing wear estimation accuracy. Additional data sources like cutting force, spindle current, and temperature are also being integrated better to understand the tool's health (Zhang et al, 2021). Feature extraction is a critical step in the successful application of machine learning (ML).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning for monitoring hobbing tool health in CNC hobbing machine

Tambake,

Deshmukh,

Pardeshi

et al. 2024

Front. Mater.

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Utilizing Machine Learning (ML) to oversee the status of hobbing cutters aims to enhance the gear manufacturing process’s effectiveness, output, and quality. Manufacturers can proactively enact measures to optimize tool performance and minimize downtime by conducting precise real-time assessments of hobbing cutter conditions. This proactive approach contributes to heightened product quality and decreased production costs. This study introduces an innovative condition monitoring system utilizing a Machine Learning approach. A Failure Mode and Effect Analysis (FMEA) were executed to gauge the severity of failures in hobbing cutters of Computer Numerical Control (CNC) Hobbing Machine, and the Risk Probability Number (RPN) was computed. This numerical value aids in prioritizing preventive measures by concentrating on failures with the most substantial potential impact. Failures with high RPN numbers were considered to implement the Machine Learning approach and artificial faults were induced in the hobbing cutter. Vibration signals (displacement, velocity, and acceleration) were then measured using a commercial high-capacity and high-frequency range Data Acquisition System (DAQ). The analysis covered operating parameters such as speed (ranging from 35 to 45 rpm), feed (ranging from 0.6 to 1 mm/rev), and depth of cut (6.8 mm). MATLAB code and script were employed to extract statistical features. These features were subsequently utilized to train seven algorithms (Decision Tree, Naive Bayes, Support Vector Machine (SVM), Efficient Linear, Kernel, Ensemble and Neural Network) as well as the application of Bayesian optimization for hyperparameter tuning and model evaluation were done. Amongst these algorithms, J48 Decision tree (DT) algorithm demonstrated impeccable accuracy, correctly classifying 100% of instances in the provided dataset. These algorithms stand out for their accuracy and efficiency in building, making them well-suited for this purpose. Based on ML model performance, it is recommended to employ J48 Decision Tree Model for the condition monitoring of a CNC hobbing cutter. The emerging confusion matrix was crucial in creating a condition monitoring system. This system can analyze statistical features extracted from vibration signals to assess the health of the cutter and classify it accordingly. The system alerts the operator when a hobbing cutter approaches a worn or damaged condition, enabling timely replacement before any issues arise.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Tambake,

Deshmukh,

Pardeshi

et al. 2024

Front. Mater.

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“…From the analysis, the conditional autoencoder's accuracy was higher than the other two methods [33]. A deep network method is used to forecast the tool wear with the help of motor power using the deep learning neural network theory, which can increase learning speed and enhance the training process [34].…”

Section: Introductionmentioning

confidence: 98%

Prediction of Flank Wear during Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach

Thangarasu¹,

Mohanraj

Devendran³

et al. 2023

Mathematical Problems in Engineering

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Currently, manufacturing industries focus on intelligent manufacturing. Prediction and monitoring of tool wear are essential in any material removal process, and implementation of a tool condition monitoring system (TCMS) is necessary. This work presents the flank wear prediction during the hard turning of EN8 steel using the deep learning (DL) algorithm. The turning operation is conducted with three levels of selected parameters. CNMG 120408 grade, TiN-coated cemented carbide tool is used for turning. Cutting force and flank wear are assessed under dry-cutting conditions. DL algorithms such as adaptive neuro-fuzzy inference system (ANFIS) and convolutional auto encoder (CAE) are used to predict the flank wear of the single-point cutting tool. The DL model is developed with turning parameters and cutting force to predict flank wear. The different ANFIS and CAE models are employed to develop the prediction model. Grid-based ANFIS structure with Gauss membership function performed better than ANFIS models. The ANFIS model’s average testing error of 0.0074011 mm and prediction accuracy of 99.81% are achieved.

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“…Deep belief networks (DBN),Convolutional neural networks (CNN), and other deep learning (DL) models have been developed in the last ten years as solutions to these issues. DL could address the aforementioned problems, as it related to a class of ML approaches in which several layers of data processing steps in hierarchical architectures were used for pattern categorization and prediction (Wang et al, 2021). In order to forecast surface unevenness and precise energy usage during 5-axis milling, Serin et al, (2017) used DMLP neural networks.…”

Section: Introductionmentioning

confidence: 99%

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

et al. 2023

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Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.

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A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning

Cited by 23 publications

References 40 publications

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Machine learning for monitoring hobbing tool health in CNC hobbing machine

Prediction of Flank Wear during Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach

A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models

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