کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5469503 | 1399002 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling
ترجمه فارسی عنوان
بررسی داده های قدرت اسپیندل با شبکه عصبی برای پیش بینی زمان واقعی سایش ابزار / شکستن در حین حفاری ناقلین
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
Nowadays, spindle power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate spindle power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle power data were collected from the power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the spindle power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8-18.4% with power data as compared to that between 0.4-17.9% with force data. Findings suggest that spindle power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Manufacturing Systems - Volume 43, Part 2, April 2017, Pages 287-295
Journal: Journal of Manufacturing Systems - Volume 43, Part 2, April 2017, Pages 287-295
نویسندگان
Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas Kurfess,