Dalian University of Technology

Mengmeng Niu

Yongqing Wang

Kuo Liu

Mingrui Shen

Bo Qin

Haibo Liu

Xu Li

Lingsheng Han

Renjie Huang

Ziyou Ban

Honghui Wang

An on line prediction method of part surface roughness based on SDAE-DBN algorithm. The tri-axis acceleration sensor is adsorbed on the rear bearing of the machine tool spindle through the magnetic seat to collect the vibration signals of the cutting process, and a microphone is placed in the left front of the processed part to collect the noise signals of the cutting process of the machine tool; the trend term of dynamic signal is eliminated, and the signal is smoothed; a stacked denoising autoencoder is constructed, and the greedy algorithm is used to train the network, and the extracted features are used as the input of deep belief network to train the network; the real-time vibration and noise signals in the machining process are input into the deep network after data processing, and the current surface roughness is set as output by the network.

On-line prediction method of surface roughness of parts based on SDAE-DBN algorithm

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.

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

A method for determining the preload value of the screw based on thermal error and temperature rise weighting. Firstly, thermal behavior test of the feed shaft under typical working conditions is carried out to obtain the maximum thermal error and the temperature rise at the key measuring points in each preloaded state. Then, a mathematical model of the preload value of the screw and the maximum thermal error is established; meanwhile, another mathematical model of the preload value of the screw and the temperature rise at the key measuring points is also established. Finally, the optimal preload value of the screw is obtained. The thermal error of the feed shaft and the temperature rise of the moving components are comprehensively considered, improving the processing accuracy and accuracy stability of the machine tool, and ensuring the service life of the moving components such as bearings.

Method for determining the preload value of the screw based on thermal error and temperature rise weighting

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Dalian, China

Mengmeng Niu