کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
796682 | 902836 | 2006 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Drill wear monitoring using back propagation neural network
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی صنعتی و تولید
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چکیده انگلیسی
Present work deals with prediction of flank wear of drill bit using back propagation neural network (BPNN). Drilling operations have been performed in mild steel work-piece by high-speed steel (HSS) drill bits over a wide range of cutting conditions. Important process parameters have been used as input for BPNN and drill wear has been used as output of the network. Inclusion of chip thickness as an input in addition to conventional parameters leads to better training of the network. Performance of the neural network has been found to be satisfactory while validated with experimental result.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Materials Processing Technology - Volume 172, Issue 2, 28 February 2006, Pages 283–290
Journal: Journal of Materials Processing Technology - Volume 172, Issue 2, 28 February 2006, Pages 283–290
نویسندگان
S.S. Panda, A.K. Singh, D. Chakraborty, S.K. Pal,