دانلود مقاله ISI انگلیسی شماره 79659
ترجمه فارسی عنوان مقاله

روش گام به گام برای تکامل مدل برنامه نویسی ژنتیکی کلی در پیش بینی صافی سطح از فرایند عطف

عنوان انگلیسی
Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
79659 2014 12 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Advances in Engineering Software, Volume 78, December 2014, Pages 16–27

ترجمه کلمات کلیدی
پیش بینی خشونت سطح؛ اموال سطحی - عطف؛ برنامه نویسی ژنتیک؛ رگرسیون گام به گام - رگرسیون بردار پشتیبان
کلمات کلیدی انگلیسی
Surface roughness prediction; Surface property; Turning; Genetic programming; Stepwise regression; Support vector regression
پیش نمایش مقاله
پیش نمایش مقاله  روش گام به گام برای تکامل مدل برنامه نویسی ژنتیکی کلی در پیش بینی صافی سطح از فرایند عطف

چکیده انگلیسی

Due to the complexity and uncertainty in the process, the soft computing methods such as regression analysis, neural networks (ANN), support vector regression (SVR), fuzzy logic and multi-gene genetic programming (MGGP) are preferred over physics-based models for predicting the process performance. The model participating in the evolutionary stage of the MGGP method is a linear weighted sum of several genes (model trees) regressed using the least squares method. In this combination mechanism, the occurrence of gene of lower performance in the MGGP model can degrade its performance. Therefore, this paper proposes a modified-MGGP (M-MGGP) method using a stepwise regression approach such that the genes of lower performance are eliminated and only the high performing genes are combined. In this work, the M-MGGP method is applied in modelling the surface roughness in the turning of hardened AISI H11 steel. The results show that the M-MGGP model produces better performance than those of MGGP, SVR and ANN. In addition, when compared to that of MGGP method, the models formed from the M-MGGP method are of smaller size. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the turning phenomenon of AISI H11 steel by unveiling dominant input process parameters and the hidden non-linear relationships.