کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
409218 | 679062 | 2008 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Artificial-neural-networks-based surface roughness Pokayoke system for end-milling operations
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Surface roughness is an important indicator of the quality of machined parts. Commonly, the off-line, manual technique of direct measurement is utilized to assess surface roughness and part quality, which is found to be very time-consuming and costly. For that reason, the neural network-based surface roughness Pokayoke (NN-SRPo) system is developed to keep the surface roughness within a desired value in an in-process manner. Both the surface roughness prediction and machining parameters control are performed online during the machining process. A testing experiment demonstrated the efficacy of this NN-SRPo system.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 71, Issues 4–6, January 2008, Pages 544–549
Journal: Neurocomputing - Volume 71, Issues 4–6, January 2008, Pages 544–549
نویسندگان
Bernie P. Huang, Joseph C. Chen, Ye Li,