Quality estimation method for gear hobbing based on attention and adversarial transfer learning

Wu, Dayuan; Yan, Ping; Pei, Jie; Su, Yingtao; Zhou, Han; Yi, Runzhong; Hu, Guilong

doi:10.1016/j.measurement.2021.110383

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…Vibration Accelerometer [35] Tool fixture, workpiece fixture [35] Vibration analysis of the gear hobbing machine has been carried out for improvising product quality as vibration is dependent upon certain parameters, which reduces the effectiveness of the machine in the cutting process.…”

Section: Power Signalmentioning

confidence: 99%

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Hameed,

Junejo,

Amin

et al. 2023

Energies

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Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing.

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Section: Power Signalmentioning

confidence: 99%

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Hameed,

Junejo,

Amin

et al. 2023

Energies

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“…It is well known that the performance of deep learning networks is proportional to the size of the dataset [27]. In actual processing, it is time-consuming and labor-intensive to collect a large amount of processing data, which is difficult to achieve.…”

Section: Layermentioning

confidence: 99%

A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism

Tang

Liu

et al. 2022

Volume 2: 42nd Computers and Information in Engineering Conference (CIE)

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The exact removal of material in abrasive belt grinding determines the final machining quality of the workpiece. However, it is difficult to determine the removal state of materials in actual processing, which is affected by factors such as abrasive belt wear and processing errors. Therefore, a multi-scale attention convolutional neural network for material removal state prediction method is proposed based on the analysis of displacement data. First, the first-order difference and sliding-window expansion methods for displacement data are adopted, making it possible to use displacement data for deep learning, which is the premise of material removal state prediction. Then, the multi-scale convolutional neural network is Employed to extract important features of the displacement data. Due to the different importance of different features, Squeeze-and-Excitation Networks are used to independently assign the importance of features based on the loss function, so that the model pays more attention to those main features and ignores the secondary features, which can improve the convergence speed and prediction accuracy of the model. The K6 cross-validation of experiment results shows that this method can accurately predict the material removal state with an average prediction accuracy of 87.9%, which can be practically applied to the online prediction of the material removal state in industrial processing to further control the processing quality.

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“…Jiao et al [24] gave a two-layer adversarial adaptation scheme for fault diagnosis. Wu et al [25] illustrated a transfer diagnosis method using an adaptive Coral (correlation alignment) loss based attention mechanism and adversarial network. She et al [26] employed a adversarial transfer diagnosis scheme by using the diversity maximization strategy.…”

Section: Introductionmentioning

confidence: 99%

A meta transfer learning method for gearbox fault diagnosis with limited data

She,

Yang,

Duan

et al. 2024

Meas. Sci. Technol.

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Intelligent diagnosis of mechanical faults is an important means to guarantee the safe maintenance of equipment. Cross domain diagnosis may lack sufficient measurement data as support, and this bottleneck is particularly prominent in high-end manufacturing. This paper presents a few-shot fault diagnosis methodology based on meta transfer learning for gearbox. To be specific, firstly, the subtasks for transfer diagnosis are constructed, and then joint distribution adaptation is conducted to align the two domain distributions; secondly, through adaptive manifold regularization, the data of target working condition is further utilized to explore the potential geometric structure of the data distribution. Meta stochastic gradient descent is explored to dynamically adjust the model’s parameter based on the obtained task information to obtain better generalization performance, ultimately to achieve transfer diagnosis of gearbox faults with few samples. The effectiveness of the approach is supported by the experimental datasets of the gearbox.

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Quality estimation method for gear hobbing based on attention and adversarial transfer learning

Cited by 15 publications

References 36 publications

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism

A meta transfer learning method for gearbox fault diagnosis with limited data

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