Journal ArticleDOI
Transfer learning enabled convolutional neural networks for estimating health state of cutting tools
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TLDR
By exploiting the integrated design of CNNs and transfer learning, viable PHM strategies for cutting tools can be established to support practical CNC machining applications.Abstract:
Effective Prognostics and Health Management (PHM) for cutting tools during Computerized Numerical Control (CNC) processes can significantly reduce downtime and decrease losses throughout manufacturing processes In recent years, deep learning algorithms have demonstrated great potentials for PHM However, the algorithms are still hindered by the challenge of the limited amount data available in practical manufacturing situations for effective algorithm training To address this issue, in this research, a transfer learning enabled Convolutional Neural Networks (CNNs) approach is developed to predict the health state of cutting tools With the integration of a transfer learning strategy, CNNs can effectively perform tool health state prediction based on a modest number of the relevant images of cutting tools Quantitative benchmarks and analyses on the performance of the developed approach based on six typical CNNs models using several optimization techniques were conducted The results indicated the suitability of the developed approach, particularly using ResNet-18, for estimating the wear width of cutting tools Therefore, by exploiting the integrated design of CNNs and transfer learning, viable PHM strategies for cutting tools can be established to support practical CNC machining applicationsread more
Citations
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Journal ArticleDOI
Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots
TL;DR: A generic framework for the enhancement of advanced physics-based models with degradation curves is introduced by introducing a generic framework in a case study coming from the white goods industry, where it is investigated whether the robot will experience some failure within the next 18 months.
Journal ArticleDOI
Application of Generalized Frequency Response Functions and Improved Convolutional Neural Network to Fault Diagnosis of Heavy-duty Industrial Robot
TL;DR: A novel CNN with the function of spectrum calculation and fault diagnosis is designed, in which the spectrum calculation network and the fault diagnosis network are connected in series and a new error cost function model is designed to guide the network parameters optimization in the direction of feature classification, which is conductive to improve the diagnosis accuracy.
Journal ArticleDOI
Application of Generalized Frequency Response Functions and Improved Convolutional Neural Network to Fault Diagnosis of Heavy-duty Industrial Robot
TL;DR: In this article , a novel CNN with the function of spectrum calculation and fault diagnosis is designed, in which the spectrum calculation network and the fault diagnosis network are connected in series, by extracting the optimized parameters of network, the nonlinear spectrum based on generalized frequency response function (GFRF) is obtained in the former network.
Journal ArticleDOI
Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning
TL;DR: In this article, a hybrid CNN-LSTM (convolutional neural network-long short-term memory network) model with an embedded transfer learning mechanism is designed for predicting the remaining useful life (RUL) of a cutting tool.
Journal ArticleDOI
Transfer learning-based thermal error prediction and control with deep residual LSTM network
TL;DR: In this paper, a transfer learning-based error control method is proposed to improve the robustness of high-accuracy machining of complex parts, where a pre-activated residual block is designed, and is embedded into the deep residual LSTMN (DRLSTMN).
References
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