Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization
This paper introduces an intelligent machining system (IMS) using an adaptive-network-based fuzzy inference system (ANFIS) predictor and the particle swarm optimization (PSO) algorithm with a hybrid objective function. The proposed IMS provides suitable machining parameters for the users, to satisfy...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9034184/ |
| _version_ | 1818875869151625216 |
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| author | Hung-Wei Chiu Ching-Hung Lee |
| author_facet | Hung-Wei Chiu Ching-Hung Lee |
| author_sort | Hung-Wei Chiu |
| collection | DOAJ |
| description | This paper introduces an intelligent machining system (IMS) using an adaptive-network-based fuzzy inference system (ANFIS) predictor and the particle swarm optimization (PSO) algorithm with a hybrid objective function. The proposed IMS provides suitable machining parameters for the users, to satisfy different machining requirements such as accuracy, surface smoothness, and speed. First, the key computer numerical control parameters are selected, and the actual trajectories under different machining parameters obtained by linear scales are collected. These data are analyzed to obtain the machining time, contouring error, and tracking error, corresponding to the speed, milling accuracy, and surface smoothness, respectively. Second, a data-driven approach using ANFIS is established to obtain the corresponding relationship model between the machining parameters and three aforementioned performance indices. Subsequently, to establish the IMS, we combine the trained ANFIS model and establish a hybrid objective function optimization problem solved by PSO algorithm according the specific requirement of the user. Finally, the performance and effectiveness of the proposed machining system is demonstrated by experimental practical machining. |
| first_indexed | 2024-12-19T13:33:21Z |
| format | Article |
| id | doaj.art-36d11d3ee4b6485bbc9db6c3592994c9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:33:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-36d11d3ee4b6485bbc9db6c3592994c92022-12-21T20:19:18ZengIEEEIEEE Access2169-35362020-01-018510625107010.1109/ACCESS.2020.29802869034184Intelligent Machining System Based on CNC Controller Parameter Selection and OptimizationHung-Wei Chiu0Ching-Hung Lee1https://orcid.org/0000-0003-3081-362XDepartment of Mechanical Engineering, National Chung Hsing University, Taichung, TaiwanDepartment of Mechanical Engineering, National Chung Hsing University, Taichung, TaiwanThis paper introduces an intelligent machining system (IMS) using an adaptive-network-based fuzzy inference system (ANFIS) predictor and the particle swarm optimization (PSO) algorithm with a hybrid objective function. The proposed IMS provides suitable machining parameters for the users, to satisfy different machining requirements such as accuracy, surface smoothness, and speed. First, the key computer numerical control parameters are selected, and the actual trajectories under different machining parameters obtained by linear scales are collected. These data are analyzed to obtain the machining time, contouring error, and tracking error, corresponding to the speed, milling accuracy, and surface smoothness, respectively. Second, a data-driven approach using ANFIS is established to obtain the corresponding relationship model between the machining parameters and three aforementioned performance indices. Subsequently, to establish the IMS, we combine the trained ANFIS model and establish a hybrid objective function optimization problem solved by PSO algorithm according the specific requirement of the user. Finally, the performance and effectiveness of the proposed machining system is demonstrated by experimental practical machining.https://ieeexplore.ieee.org/document/9034184/Machine toolsmachining parametersANFISPSOoptimization |
| spellingShingle | Hung-Wei Chiu Ching-Hung Lee Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization IEEE Access Machine tools machining parameters ANFIS PSO optimization |
| title | Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization |
| title_full | Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization |
| title_fullStr | Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization |
| title_full_unstemmed | Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization |
| title_short | Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization |
| title_sort | intelligent machining system based on cnc controller parameter selection and optimization |
| topic | Machine tools machining parameters ANFIS PSO optimization |
| url | https://ieeexplore.ieee.org/document/9034184/ |
| work_keys_str_mv | AT hungweichiu intelligentmachiningsystembasedoncnccontrollerparameterselectionandoptimization AT chinghunglee intelligentmachiningsystembasedoncnccontrollerparameterselectionandoptimization |