A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM

被引：53
作者：
Zhang, Lingxuan [1 ]
Jia, Zhenyuan [1 ]
Wang, Fuji [1 ]
Liu, Wei [1 ]
机构：
[1] Dalian Univ Technol, Key Lab Precis & Nontradit Machining Technol, Minist Educ, Dalian 116024, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2010年 / 51卷 / 5-8期
基金：
中国国家自然科学基金; 高等学校博士学科点专项科研基金;
关键词：
Micro-EDM; SVM; GA; Multi-objective optimization; NEURAL-NETWORK; SURFACE-ROUGHNESS; NSGA-II; COMPOSITES;
D O I：
10.1007/s00170-010-2623-5
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In micro-electrical discharge machining (EDM), processing parameters greatly affect processing efficiency and stability. However, the complexity of micro-EDM makes it difficult to determine optimal parameters for good processing performance. The important output objectives are processing time (PT) and electrode wear (EW). Since these parameters influence the output objectives in quite an opposite way, it is not easy to find an optimized combination of these processing parameters which make both PT and EW minimum. To solve this problem, supporting vector machine is adopted to establish a micro-EDM process model based on the orthogonal test. A new multi-objective optimization genetic algorithm (GA) based on the idea of non-dominated sorting is proposed to optimize the processing parameters. Experimental results demonstrate that the proposed multi-objective GA method is precise and effective in obtaining Pareto-optimal solutions of parameter settings. The optimized parameter combinations can greatly reduce PT while making EW relatively small. Therefore, the proposed method is suitable for parameter optimization of micro-EDM and can also enhance the efficiency and stability of the process.
引用
收藏
页码：575 / 586
页数：12
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