کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9705257 | 1464609 | 2005 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی صنعتی و تولید
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing](/preview/png/9705257.png)
چکیده انگلیسی
This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Machine Tools and Manufacture - Volume 45, Issue 15, December 2005, Pages 1726-1734
Journal: International Journal of Machine Tools and Manufacture - Volume 45, Issue 15, December 2005, Pages 1726-1734
نویسندگان
Z.G. Wang, M. Rahman, Y.S. Wong, J. Sun,