Umer Usama, Mian Syed Hammad, Mohammed Muneer Khan, Abidi Mustufa Haider, Moiduddin Khaja, Kishawy Hossam
Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia.
Machining Research Laboratory, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada.
Materials (Basel). 2022 Jun 7;15(12):4059. doi: 10.3390/ma15124059.
The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54-56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.
本研究评估了自驱式旋转硬质合金刀具在切削淬硬钢时的性能。尽管已经引入并应用了各种评估传统(固定)刀具磨损的模型,但现有文献中尚未有人致力于预测使用自驱式旋转刀具时刀具磨损的进展情况。利用工件与刀具的几何关系和经验函数,建立了自驱式旋转切削刀具的后刀面磨损模型。在硬度为54 - 56 HRC的AISI 4340钢上进行了不同切削速度和进给量的切削实验。在不同的时间间隔测量刀具磨损率。所提出模型中的常数通过遗传编程获得。当对实验和预测的后刀面磨损进行检验时,发现所建立的模型能够有效地估计旋转刀具后刀面磨损的进展速率。