Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural Network
With the rapid development of social economy and modern industry, the performance requirements of some important nanomaterials in various fields are constantly improving. The processing of these nanomaterials will have a direct impact on the development level of some core industries, such as aerospa...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1904431 |
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| author | Haiyan Wang Fanwei Meng |
| author_facet | Haiyan Wang Fanwei Meng |
| author_sort | Haiyan Wang |
| collection | DOAJ |
| description | With the rapid development of social economy and modern industry, the performance requirements of some important nanomaterials in various fields are constantly improving. The processing of these nanomaterials will have a direct impact on the development level of some core industries, such as aerospace, medical devices, and automobile manufacturing. In the early stage of the machining process, the BP neural network is generally used to control the CNC machining. However, it also has some shortcomings, such as the inability to determine the initial parameter weights according to the errors in the processing process, which limits its application in processing control. Therefore, this paper used RBF neural network to solve the problems in the process of CNC machining of nanomaterials and, at the same time, integrated RBF neural network technology into CNC electronic machining control, so as to improve the precision of CNC electronic machining of nanomaterials and avoid the occurrence of errors to the greatest extent. The method proposed in this paper used the self-learning and self-adaptive ability of RBF neural network to adjust the parameters of CNC machining control and relied on its fast convergence speed and strong approximation ability to achieve better CNC machining control effect. The experimental results showed that, after integrating the control technology of RBFNN in the CNC machining process of nanomaterials, the roundness error and roughness error of the machined workpiece were reduced by 70% and 50%, respectively. The control method proposed in this paper has high precision and strong stability. |
| format | Article |
| id | doaj-art-fa5ce0e1e11a4ad6ba9b6cda577c7f22 |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-fa5ce0e1e11a4ad6ba9b6cda577c7f222025-02-03T06:11:51ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/1904431Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural NetworkHaiyan Wang0Fanwei Meng1College of Mechanical EngineeringMilitary Sports DepartmentWith the rapid development of social economy and modern industry, the performance requirements of some important nanomaterials in various fields are constantly improving. The processing of these nanomaterials will have a direct impact on the development level of some core industries, such as aerospace, medical devices, and automobile manufacturing. In the early stage of the machining process, the BP neural network is generally used to control the CNC machining. However, it also has some shortcomings, such as the inability to determine the initial parameter weights according to the errors in the processing process, which limits its application in processing control. Therefore, this paper used RBF neural network to solve the problems in the process of CNC machining of nanomaterials and, at the same time, integrated RBF neural network technology into CNC electronic machining control, so as to improve the precision of CNC electronic machining of nanomaterials and avoid the occurrence of errors to the greatest extent. The method proposed in this paper used the self-learning and self-adaptive ability of RBF neural network to adjust the parameters of CNC machining control and relied on its fast convergence speed and strong approximation ability to achieve better CNC machining control effect. The experimental results showed that, after integrating the control technology of RBFNN in the CNC machining process of nanomaterials, the roundness error and roughness error of the machined workpiece were reduced by 70% and 50%, respectively. The control method proposed in this paper has high precision and strong stability.http://dx.doi.org/10.1155/2022/1904431 |
| spellingShingle | Haiyan Wang Fanwei Meng Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural Network Advances in Materials Science and Engineering |
| title | Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural Network |
| title_full | Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural Network |
| title_fullStr | Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural Network |
| title_full_unstemmed | Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural Network |
| title_short | Control Method of Nanomaterial Numerical Control Electronic Processing Based on RBF Neural Network |
| title_sort | control method of nanomaterial numerical control electronic processing based on rbf neural network |
| url | http://dx.doi.org/10.1155/2022/1904431 |
| work_keys_str_mv | AT haiyanwang controlmethodofnanomaterialnumericalcontrolelectronicprocessingbasedonrbfneuralnetwork AT fanweimeng controlmethodofnanomaterialnumericalcontrolelectronicprocessingbasedonrbfneuralnetwork |