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

A surface roughness, multi-task learning technology, applied in the field of machining, to reduce costs, avoid repetitive work, and improve production efficiency and quality

Active Publication Date: 2020-07-03
DALIAN UNIV OF TECH
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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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  • Part surface roughness and tool wear prediction method based on multi-task learning
  • Part surface roughness and tool wear prediction method based on multi-task learning
  • Part surface roughness and tool wear prediction method based on multi-task learning

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Embodiment Construction

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings.

[0050] On a three-axis vertical machining center, a cutting test was performed with a vertical milling cutter. Among them, the basic information of the three-axis vertical machining center is: the maximum travel of the X-axis, Y-axis and Z-axis is 710mm, 500mm and 350mm, and the maximum feed speed is 32m / min, 32m / min and 30m / min; The highest speed is 15000r / min. The basic information of the tool is: the tool type is vertical milling cutter; the tool material is carbide; the tool diameter is 10mm; the number of tool edges is 4. The basic information of the workpiece to be cut is: the material of the workpiece is 45# steel; the shape of the workpiece is 200mm X 100mm X 10mm. The cutting process parameters are: depth of cut is 2mm; feed rate is 80mm / min; spindle speed is ...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01B21/30G01B21/00G06N3/04G06N3/08
CPCG01B21/30G01B21/00G06N3/08G06N3/045
Inventor 王永青秦波刘阔沈明瑞牛蒙蒙王宏慧韩灵生
Owner DALIAN UNIV OF TECH
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