Part surface roughness and tool wear prediction method based on multi-task learning

Abstract

The invention belongs to the technical field of machining and provides a part surface roughness and tool wear prediction method based on multi-task learning. The method is characterized in that firstly, vibration signals in the machining process are collected, next, the surface roughness of a part and the abrasion condition of a cutter are measured, and the measured results are made to correspondto vibration signals respectively; secondly, sample expansion is carried out, and features are extracted and normalized; then, a multi-task prediction model based on a deep belief network is constructed, the surface roughness of the part and the cutter abrasion condition serve as model output, features are extracted as input, and a multi-task DBN network prediction model is established; and finally, test verification is performed, a vibration signal is inputted into the multi-task prediction model, and the surface roughness and the cutter wear condition are predicted. The method is mainly advantaged in that online prediction of the part surface roughness and the tool wear condition is achieved through one-time modeling, the hidden information contained in monitoring data is fully utilized,and the workload and model building cost are reduced.

Description

technical field [0001] The invention belongs to the technical field of mechanical processing, and relates to a method for predicting the surface roughness of parts and tool wear based on multi-task learning. Background technique [0002] Surface quality is an important factor that determines the machining performance. A high-quality part surface can significantly improve the fatigue strength, corrosion resistance and creep life of the part. In parts processing, surface roughness is one of the main indicators of machined surface quality. Surface roughness affects the functional properties of a part, such as surface friction and wear due to contact. Tool wear is a normal phenomenon in metal cutting. Machining will passivate the cutting edge of the tool, increase the friction between the tool and the workpiece, and also increase the power consumption. If the tool wear status cannot be judged in time, the dimensional accuracy of the workpiece will be reduced, the surface rough...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for predicting part surface roughness and tool wear based on multi-task learning, and to solve the problem that existing prediction methods can only realize part surface roughness prediction or tool wear prediction alone

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