2026-10-13T22:37:00Z https://www.scidb.cn/oai

10.57760/sciencedb.05992 2022-11-07T10:11:20Z

2022-11-07 Result data of tool wear estimation and life prediction in milling doi:10.57760/sciencedb.05992 en Table 1 lists the experimental workpiece materials and processing parameters, including spindle speed nt (rpm), workpiece feed f (mm/rev) and tool milling depth ap (mm). The milling cutter is an end milling cutter with 70mm diameter and 6-flute indexable inserts. The overall dimension of the workpiece is 483mm × 178mm × 51mm.Table 2 lists the wear coefficients calculated based on the experimental wear data under each working condition according to the aforementioned wear model. According to the experimental data of tool wear, through the comparison between Experiment 2/Experiment 14 and Experiment 3/Experiment 13, under the same processing conditions, when the workpiece material is alloy steel, the wear coefficients A, C, KA2, KC2 and KD1 are greater than those of cast iron. The wear trend analysis shows that using alloy steel as workpiece material will cause more rapid tool wear. Comparing the wear coefficients obtained in experiment 2 and experiment 9, the tool wear increases with the increase of milling depth ap. Tool wear modeling; Adjustable coefficients; Milling Yu Zhang Kunpeng Zhu Xianyin Duan Si Li PUBLIC https://creativecommons.org/licenses/by-nc-sa/4.0/ dataset http://www.doi.org/10.1016/j.ymssp.2021.107617 Science Data Bank