Fuzzy regression integrated with genetic-tabu algorithm for prediction and optimization of a turning process

被引：0
作者：
Hadi Gholizadeh
Nikbakhsh Javadian
Hamed Fazlollahtabar
机构：
[1] Mazandaran University of Science and Technology,Department of Industrial Engineering
[2] Damghan University,Department of Industrial Engineering, College of Engineering
来源：
The International Journal of Advanced Manufacturing Technology | 2018年 / 96卷
关键词：
Turning process; Fuzzy regression; Genetic algorithm (GA); Tabu search;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Prediction of surface roughness is a key element for an automated machining center. In this regard, it is important to optimize the machining process. In this paper, fuzzy linear regression approach is employed to predict the surface roughness for a turning process in an uncertain condition. The important process parameters such as cutting speed, cutting depth, speed, and tool tip radius are considered as inputs to determine their significance for prediction. To handle uncertainty, fuzzy theory is employed. Thus, fuzzy liner regression is modeled. To optimize the estimated values of prediction errors, a genetic algorithm (GA) is developed. In addition, tabu search is used to facilitate GA for better performance. A numerical example is worked out to show the effectiveness of the proposed method.
引用
收藏
页码：2781 / 2790
页数：9
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