Periscope Detection Mechanism, Prediction System and Method for Cyclone Milling Tool

Abstract

The invention discloses a periscope detection mechanism, a prediction system and a method for whirlwind milling cutters, which solves the problems of limited camera installation space and poor image accuracy in the prior art, and has the advantages of avoiding camera damage and ensuring images Effectively obtained beneficial effects, the scheme is as follows: a periscope detection mechanism for cyclone milling tools, including a mechanical arm, the mechanical arm is rotatably mounted on the shell of the cyclone milling head, and a hollow lens barrel is arranged at the end of the mechanical arm. The side bending is set so that the lens barrel is periscope type, the lens barrel is equipped with an objective lens near the bent end, and a camera is set at the other end, a reflector is set at the bend in the lens barrel, and a first rotation is set between the mechanical arm and the lens barrel The mechanism drives the lens barrel to rotate relative to the mechanical arm, so that the end of the lens barrel provided with the supplementary light can be arranged toward the flank of the cyclone milling tool.

Description

technical field [0001] The invention relates to the field of mechanical intelligence, in particular to a periscope detection mechanism, prediction system and method for whirl milling cutters. Background technique [0002] Whirlwind milling is a processing method widely used in the production of complex spiral surface workpieces such as turbine worms, and has excellent processing efficiency. Since the radius of rotation of the tool during whirl milling is larger than that of general milling, it has a higher cutting speed and a higher material removal rate at the same speed and feed rate, which is accompanied by an acceleration of tool wear. Therefore, the irregular detection of tool wear during cyclone milling has become a necessary process, which can avoid sudden damage to the tool when it is severely worn, ensure that the processed parts have better surface processing quality, and improve the processing efficiency and economy of the enterprise. benefit. [0003] The tradi...

AI Technical Summary

Problems solved by technology

Among them, the mechanical contact detection has low efficiency and poor precision; the optical projection detection process is cumbersome, the measurement efficiency is low, and it is easy to form subjective errors; the traditional image measurement detection uses a hand-held microscope to measure, although the measurement accuracy is higher than other measurement methods. , but the measurement process is separated from the production process, and the tool handle needs to be disassembled to measure outside the machine tool, resulting in secondary clamping, repeated positioning, increased labor, reduced efficiency, and increased error

[0004] At present, more advanced image measurement detection adopts tool detection based on machine vision: that is, through the image acquisition device (CCD camera) to collect target image information, input the collected tool wear image to the image processing system, and use the designed algorithm to analyze the collected Tool wear map is processed and analyzed, image feature information is extracted, and then image information is recognized by image recognition technology. However, due to the limited structure of the whirl milling cutter head, it is not convenient to install the camera. Deep into the appropriate position, so the acquired image cannot accurately reflect the wear amount of the tool; in addition, the wear rate of the tool does not remain linear, and the wear rate will be accelerated when it enters the severe wear in the later stage, but the existing technology Unable to predict wear after a set time

Images