Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion
Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse a...
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| Vydáno v: | IEEE transactions on industrial informatics Ročník 16; číslo 8; s. 5150 - 5159 |
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| Jazyk: | angličtina |
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IEEE
01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1551-3203, 1941-0050 |
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| Abstract | Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse autoencoders and a nonlinear regression function for tool wear prediction. Multiple stacked sparse autoencoders consists of two main structures. One model is designed with multidimensional stacked sparse autoencoders, which can learn more features from different feature domains in the raw vibration signal, and another single-dimensional stacked sparse autoencoders is used for feature fusion and deeper features learning. And a modified loss function is applied that improves the learning ability. In addition, due to the good properties of tool wear process in nonstationarity and complex nonlinear, a nonlinear regression function is utilized to enhance the progressive tool wear prediction tasks. A dataset from a real manufacturing process is used to evaluate the performance of the proposed modeling framework. Experimental results show that tool wear can be predicted accurately and stably by the proposed tool wear predictive model, which outperforms the already developed methods. |
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| AbstractList | Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse autoencoders and a nonlinear regression function for tool wear prediction. Multiple stacked sparse autoencoders consists of two main structures. One model is designed with multidimensional stacked sparse autoencoders, which can learn more features from different feature domains in the raw vibration signal, and another single-dimensional stacked sparse autoencoders is used for feature fusion and deeper features learning. And a modified loss function is applied that improves the learning ability. In addition, due to the good properties of tool wear process in nonstationarity and complex nonlinear, a nonlinear regression function is utilized to enhance the progressive tool wear prediction tasks. A dataset from a real manufacturing process is used to evaluate the performance of the proposed modeling framework. Experimental results show that tool wear can be predicted accurately and stably by the proposed tool wear predictive model, which outperforms the already developed methods. |
| Author | He, Songping Li, Kai Li, Bin Luo, Bo Liu, Hongqi Shi, Chengming |
| Author_xml | – sequence: 1 givenname: Chengming orcidid: 0000-0002-6530-5695 surname: Shi fullname: Shi, Chengming email: 513864035@qq.com organization: Huazhong University of Science and Technology, Wuhan, China – sequence: 2 givenname: Bo surname: Luo fullname: Luo, Bo email: b.luo@sheffield.ac.uk organization: Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, U.K – sequence: 3 givenname: Songping orcidid: 0000-0002-7212-7591 surname: He fullname: He, Songping email: 8562576@qq.com organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 4 givenname: Kai surname: Li fullname: Li, Kai email: 15527965836@163.com organization: Huazhong University of Science and Technology, Wuhan, China – sequence: 5 givenname: Hongqi surname: Liu fullname: Liu, Hongqi email: liuhongqi328@163.com organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 6 givenname: Bin orcidid: 0000-0002-8722-8934 surname: Li fullname: Li, Bin email: li_bin_hust@163.com organization: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China |
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| SubjectTerms | Condition monitoring Deep learning Feature extraction feature fusion Force Ground penetrating radar Informatics Machine learning Modelling Prediction models Predictive models stacked sparse autoencoders Tool wear tool wear prediction Vibrations |
| Title | Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion |
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