کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1696823 | 1519234 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Tool life predictions in milling using spindle power with the neural network technique
ترجمه فارسی عنوان
پیش بینی های عمر ابزار در آسیاب با استفاده از قدرت اسپیندل با تکنیک شبکه عصبی
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
زندگی ابزار، نظارت وضعیت ابزار، شبکه عصبی، پایان دادن به فرز، سیگنال قدرت اسپیندل، عدم قطعیت،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی صنعتی و تولید
چکیده انگلیسی
Tool wear is an important limitation to machining productivity and part quality. In this paper, remaining useful life (RUL) prediction of tools is demonstrated based on the machine spindle power values using the neural network (NN) technique. End milling tests were performed on a stainless steel workpiece at different spindle speeds and spindle power was recorded. The NN curve fitting approach with different MATLAB⢠training functions was applied to the root mean square power (Prms) values. Sample Prms growth curves were generated to take into account uncertainty. The Prms value in the time domain was found to be sensitive to tool wear. Results show a good agreement between the predicted and true RUL of tools. The proposed method takes into account the uncertainty in tool life and the percentage increase in nominal Prms value during the RUL prediction. Using MATLAB⢠on an Intel i7 processor, the computation takes 0.5 s Thus, the method is computationally inexpensive and can be incorporated for real time RUL predictions during machining.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Manufacturing Processes - Volume 22, April 2016, Pages 161-168
Journal: Journal of Manufacturing Processes - Volume 22, April 2016, Pages 161-168
نویسندگان
Cyril Drouillet, Jaydeep Karandikar, Chandra Nath, Anne-Claire Journeaux, Mohamed El Mansori, Thomas Kurfess,