کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7003821 | 1454936 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Notch wear prediction model in high speed milling of AerMet100 steel with bull-nose tool considering the influence of stress concentration
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
شیمی کلوئیدی و سطحی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Notch wear is one of the predominant types of tool failure in high speed milling of difficult-to-cut materials. This paper aims to reveal the formation and impact mechanisms of notch wear in bull-nose milling. Tool wear on rake face during dry milling of AerMet100 steel is investigated, severe notch wear is observed in lots of experiments. Based on the comparison of uncut chip thickness variations under two different cutting types, an attempt is made to explain the occurrence of notch wear from the view point of stress concentration. In order to quantitatively characterize the degree of stress concentration at any position on tool edge, a stress concentration factor is defined in this paper. The non-uniform and instantaneous variable characteristics of stress distribution along the cutting edge during bull-nose milling are taken into consideration. Then a predictive model of notch wear depth considering the influence of stress concentration is developed. Series of cutting tests are performed to validate the notch wear depth model, and the results indicate that the proposed model is feasible. In addition, the effects of different cutting parameters on notch wear are discussed. Further, this work can be applied to optimize process parameters for controlling notch wear, improving machining efficiency and reducing production cost.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Wear - Volumes 408â409, 15 August 2018, Pages 228-237
Journal: Wear - Volumes 408â409, 15 August 2018, Pages 228-237
نویسندگان
Haohao Zeng, Rong Yan, Pengle Du, Mingkai Zhang, Fangyu Peng,