scholarly journals Special Issue on Recent Control Technologies and Precision Machining/Processing. Thermal Deformation Control of a Machining Center Using Neural Network.

1993 ◽  
Vol 59 (9) ◽  
pp. 1447-1450
Author(s):  
Takaaki NAGAO ◽  
Mamoru MITSUISHI ◽  
Yotaro HATAMURA
Keyword(s):  
Neural Network ◽  
Thermal Deformation ◽  
Precision Machining ◽  
Special Issue ◽  
Deformation Control ◽  
Machining Center ◽  
Control Technologies

Finite Element Analysis and Optimization of Thermal Deformation of Resin Concrete Manufacturing Bases

2014 ◽  
Vol 543-547 ◽  
pp. 4010-4013
Author(s):  
Yao Chen ◽  
Xiu Xia Liang ◽  
Shuang Qiu
Keyword(s):  
Thermal Stability ◽  
Machine Tool ◽  
Thermal Deformation ◽  
Precision Machining ◽  
Element Analysis ◽  
Excellent Thermal Stability ◽  
Good Thermal Stability ◽  
Machining Center ◽  
Ultra Precision ◽  
Thermal Stability Of

Resin concrete generally has good mechanical properties, excellent thermal stability and great vibration resistance, the model of the ultra-precision machining center bed is established to study the thermal stability of the resin concrete using virtual reality and collaborative simulation technology based on Pro/E and ANSYS Workbench. The main factors that affect the machine tool bed thermal deformation were found through analyzing the deformation results and the materials and restrain conditions were optimized. The results proved that the optimized machine tool bed has good thermal stability and theoretical basis was provided to improve the thermal stability of the ultra-precision machining centers.

Novel thermal error modeling method for machining centers

2014 ◽  
Vol 229 (8) ◽  
pp. 1500-1508 ◽  
Author(s):  
Xiaolong Zhu ◽  
Sitong Xiang ◽  
Jianguo Yang
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Thermal Deformation ◽  
Clustering Algorithm ◽  
Thermal Error ◽  
Network Clustering ◽  
Elman Neural Network ◽  
Machining Center ◽  
Adaptive Vector Quantization ◽  
Thermal Error Model

Thermal deformation is one of the main contributors to machining errors in machine tools. In this paper, a novel approach to build an effective thermal error model for a machining center is proposed. Adaptive vector quantization network clustering algorithm is conducted to identify the temperature variables, and then one temperature variable is selected from each cluster to represent the same cluster. Furthermore, a non-linear model based on output-hidden feedback Elman neural network is adopted to establish the relationship between thermal error and temperature variables. The output-hidden feedback Elman network is adopted to predict the thermal deformation of the machining center. Back propagation (BP) neural network is introduced for comparison. A verification experiment on the machining center is carried out to validate the efficiency of the newly proposed method. Experimental verification shows that the adaptive vector quantization network clustering algorithm and output-hidden feedback Elman neural network is an accurate and effective method.

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