Prediction method of part surface roughness and tool wear based on multi-task learning

US11761930B2 · kind B2 · utility

Assignee

• Dalian University of Technology · CN

Inventors

• Yongqing Wang · 东风镇, CN
• Bo Qin · Beijing, CN
• Kuo Liu · Dalian, CN
• Mingrui Shen · Dalian, CN
• Mengmeng Niu · 东风镇, CN
• Honghui Wang · Beijing, CN
• Lingsheng HAN · 东风镇, CN

Key dates

• Filing date: Mar 6, 2020
• Grant date: Sep 19, 2023
• Expiry date: May 15, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B2219/50057
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.