Application of neural network for thermal error compensation in CNC machine tool

The thermal deformation error that is the biggest error of effecting the machining precision of Direct-drive A/C Bi-rotary Milling Head was narrated in brief. Based on the introduce of the study status on the thermal error compensation techniques of CNC Machine tool, the momentum of thermal deformation of Bi-rotary Milling Head was analyzed. According to the Trigonometric Relations in A/C axis rotation angle of Bi-rotary Milling Head and the momentum of thermal deformation in Bi-rotary Milling Head and -axis respectively, a thermal error compensation model was established to make the Machine tool to compensate for thermal errors in -axis.

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Identification of position of key thermal susceptible points for thermal error compensation of machine tool by neural network

10.1117/12.217535 ◽

1995 ◽

Cited By ~ 3

Author(s):

Dexian Zhang ◽

Xiaolian Liu ◽

Hanming Shi ◽

Ri-Yao Chen

Keyword(s):

Neural Network ◽

Machine Tool ◽

Error Compensation ◽

Thermal Error ◽

Thermal Error Compensation

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Thermal Error Compensation of Spindle System of Computer Numerically Controlled Machine Tools Through Experiments and Modeling

Instrumentation Mesure Métrologie ◽

10.18280/i2m.190408 ◽

2020 ◽

Vol 19 (4) ◽

pp. 301-309

Author(s):

Jianchen Wang ◽

Tao Jiang ◽

Junquan Shen ◽

Junhao Dai ◽

Zequan Pan ◽

...

Keyword(s):

Machine Tool ◽

Error Compensation ◽

Machine Tools ◽

Thermal Error ◽

Local Optimum ◽

Cnc Machine ◽

Machining Error ◽

Generalization Ability ◽

Computer Numerically Controlled ◽

Thermal Error Compensation

This paper attempts to solve the insufficient machining precision of computer numerically controlled (CNC) machine tools, which is induced by the thermal error of the spindle. Firstly, the relationship between machining error and thermal sensitive points was analyzed through experiments. On this basis, the backpropagation neural network (BPNN) was improved by particle swarm optimization (PSO). Next, the improved network (PSO-BPNN) was used to build a thermal error compensation (TCE) model for the spindle of machine tools. Taking VM-500T precision machine tool as the object, the temperature data were grouped through the optimization based on thermal imaging, grey relational analysis (GRA), and fuzzy clustering, to determine the temperature sensitive items that causes the thermal error. To speed up network convergence, the PSO algorithm was introduced to optimize the number of hidden layers and the number of hidden layer nodes of the BPNN, lifting the network from the local optimum trap. To enhance the generalization ability, the weights and thresholds of the BPNN were also improved by the PSO. After that, two TCE models were established for the spindle of the machine tool, respectively based on the original BPNN and PSO-BPNN. Contrastive experiments show that the PSO-BPNN TCE model achieved the better generalization ability, and improved the prediction accuracy of the machining error of the CNC machine tool.

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