In view of the problem of machining stagnation and installation errors caused by the frequent unloading of the tool in the traditional offline inspection method of end mills, which seriously affects the inspection efficiency and inspection accuracy; this paper proposes a telescopic on-machine vision inspection method built on the side observation window of the machining center to realize On-machine inspection of end mill wear which is independent of machining center. For the end mill cutting edge wear detection algorithm, the improved OTSU is used to obtain the binarized image of the worn area, the canny operator and the sub-pixel edge are fused to extract the wear contour, and the original cutting edge is reconstructed by the least square method. Quick detection of end mill wear. The end milling experiment was carried out with the four-tooth carbide end mill as the object to study the nonlinear change of the wear rate of the tool under different wear degrees. The experimental results are in line with the wear change law in the tool life cycle: the wear rate in the initial and sharp wear stages has a large change range, and the change range in the normal wear stage is small; the results show that the measurement deviation of the detection system algorithm is less than 0.01mm, and the average accuracy rate reaches 95.03%, to ensure the accuracy and stability of the detection system.
From precision machining to green manufacturing, the search for an economical and effective green manufacturing chain system has gradually become the focus of the metal processing industry in order to ensure the quality of workshop manufacturing, increase the production capacity per unit of energy consumption, and reduce processing consumables. The existing research The focus is more on the establishment of the objective function model, the optimization method used and the targeted optimization direction are relatively single, and a more complete manufacturing system has not been established. There are three main optimization goals for processing process parameters in this subject: starting from the direction of processing quality, reducing the surface roughness of the workpiece as the optimization goal, establishing a cutting surface integrity (Ra) function; starting from the direction of enterprise manufacturing, to improve milling efficiency Optimize the objective and establish the cutting efficiency (SEC) function; starting from the processing cost, to reduce the amount of tool wear as the optimization objective, build a machine tool tool wear visual inspection mechanism to assist in the establishment of the tool wear (VB) function, and introduce a non-dominant sorting genetic algorithm to obtain Pareto After the frontier solution, different optimization suggestions are proposed for the refinement of rough/finish processing and general processing. The results show that the NSGA-Ⅱ model has a stronger search ability than the GA model when faced with multiple quasi-measurement decision problems. The processing efficiency can be increased by 42.7%, the tool cost can be saved by 25%, and the workpiece quality can be increased by 21.8%.
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