Pérez-Salinas Cristian F, Del Olmo Ander, López de Lacalle L Norberto
Faculty of Civil and Mechanical Engineering, Universidad Técnica de Ambato, Ambato 180103, Ecuador.
Aeronautics Advanced Manufacturing Centre (CFAA), University of the Basque Country (UPV/EHU), Parque Tecnológico de Bizkaia-Ed.202, 48170 Zamudio, Spain.
Materials (Basel). 2022 Jul 24;15(15):5135. doi: 10.3390/ma15155135.
In recent years, cutting edge preparation became a topic of high interest in the manufacturing industry because of the important role it plays in the performance of the cutting tool. This paper describes the use of the drag finishing cutting edge preparation process on the cutting tool for the broaching process. The main process parameters were manipulated and analyzed, as well as their influence on the cutting edge rounding, material remove rate , and surface quality/roughness (, ). In parallel, a repeatability and reproducibility analysis and cutting edge radius prediction were performed using machine learning by an artificial neural network . The results achieved indicate that the influencing factors on , , and roughness, in order of importance, are drag depth, drag time, mixing percentage, and grain size, respectively. The reproducibility accuracy of is reliable compared to traditional processes, such as brushing and blasting. The prediction accuracy of the of preparation with is observed in the low training and prediction errors 1.22% and 0.77%, respectively, evidencing the effectiveness of the algorithm. Finally, it is demonstrated that the has reliable feasibility in the application of edge preparation on broaching tools under controlled conditions.
近年来，由于其在切削刀具性能中所起的重要作用，前沿刃口制备成为制造业中备受关注的一个话题。本文描述了在拉削加工刀具上使用拖曳光整刃口制备工艺的情况。对主要工艺参数进行了操控和分析，以及它们对刃口倒圆、材料去除率和表面质量/粗糙度的影响。同时，通过人工神经网络利用机器学习进行了重复性和再现性分析以及刃口半径预测。所取得的结果表明，对材料去除率、表面粗糙度和刃口倒圆的影响因素，按重要性排序分别为拖曳深度、拖曳时间、混合百分比和粒度。与刷光和喷砂等传统工艺相比，表面粗糙度的再现性精度是可靠的。观察到使用人工神经网络进行刃口制备的预测精度在低训练误差和预测误差分别为1.22%和0.77%的情况下，证明了该算法的有效性。最后，证明了在受控条件下，拖曳光整在拉削刀具刃口制备应用中具有可靠的可行性。
