Thermal error optimization modeling and real-time compensation on a CNC turning center

Wu, Hao; Zhang, Hongtao; Guo, Qianjian; Wang, Xiushan; Yang, Jing

doi:10.1016/j.jmatprotec.2007.12.067

Cited by 85 publications

(28 citation statements)

References 20 publications

(20 reference statements)

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“…Productivity basically consist of three basis which are the production input, production output and effectiveness of product distribution from inputs to output (Bhattasali and Bhattasali, 1972). Artificial Neural Network (ANN) is a calculation system for sets of data in nonlinear experiment that uses system neurons with ability mapping linear line (Hao et al, 2008). Hayati (2007) defines the Artificial Neural Network as an interconnection of simple processing element that use simple biological neuron as it functionality.ANN operating similarly like human brain function to resolve problems which involve mathematical calculation (Zhou et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Temperature Level to Enhance Worker Performance in Automotive Industry

Ismail

Yusof

Makhtar

et al. 2010

American Journal of Applied Sciences

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Problem statement: Production of automotive parts is among the largest contributor to economic earnings in Malaysia. The dominant work involve in producing automotive part were manual assembly process. Where it is definitely used a manpower capability. Thus the quality of the product heavily depends on workers comfort in the working condition. Temperature is one of the environmental factors that give significant effect on the worker performance. Approach: Temperature level and productivity rate were observed in automotive factory. An automotive manufacturing firm was chosen to observe the temperature level and workers productivity rate. The data were analyzed using Artificial Neural Networks analysis (ANN). ANN analysis technique is usual analysis method used to form the best linear relationship from the collected data. Results: It is apparent from the linear relationship, that the optimum value of production (valueâ1) attained when temperature value (WBGT) is 24.5°C. Conclusion: Optimum value production rate (valueâ1) for one manual production line in that particular company is successfully achieved. Through ANN method, the optimum temperature level for the optimum manual workers performance manage to be predicted

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Section: Introductionmentioning

confidence: 99%

Optimization of Temperature Level to Enhance Worker Performance in Automotive Industry

Ismail

Yusof

Makhtar

et al. 2010

American Journal of Applied Sciences

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“…In addition, there are many physical limitations in implementing process, which cannot be overcome solely by design techniques. As a result, error compensation technique to improve machine accuracy cost-effectively has received significant attention in recent years [14,15]. It is important to compensate the thermal error once the relative displacement between the tool and the workpiece is identified during machining.…”

Section: Introductionmentioning

confidence: 99%

Temperature distribution and thermal error prediction of a CNC feed system under varying operating conditions

Jin

2014

Int J Adv Manuf Technol

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In high-speed and high-precision feed systems, the thermal errors are mainly caused by the non-uniform temperature variations and time-varying thermal deformations under different operating conditions. The research work undertaken herein ultimately aims to develop a generic method which is capable of evaluating the temperature rises of heat sources (such as supporting bearings and ball screw nut) and thermal positioning error of the feed system induced by varying operating conditions (feed speed, cutting load, and preload of ball screw). Experiments were carried out on a high-speed feed system experimental bench, and the influences of operating conditions on temperature rises of supporting bearings and ball screw nut were analyzed. Based on wavelet neural network and NARMA-L2 model, the relationship between temperature rise of supporting bearings and operating conditions was established. Furthermore, with the temperature of ball screw nut set to be a moving heating source load, the temperature and thermal deformation distributions of ball screw under bearings and ball screw nut heat sources were simulated. The experiment and simulation results show that the recommended modeling method can be used to predict thermal positioning errors of feed system with good accuracy. The work described lays a solid foundation of thermal error prediction and compensation in a feed system under varying operating conditions.

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“…Also in many cases physical limitations to accuracy, this cannot be overcome solely by design techniques. Therefore it is well known that the most costeffective way of achieving high quality products with high productivity is by the use of the error compensation techniques (ECT).Thermally induced errors can account for as much as 70% of the dimensional errors of a workpiece by Wu Hao and Zhang Hongtao (2008). Precise modeling of errors is a critical part of error compensation.…”

Section: Introductionmentioning

confidence: 99%

Real-time Thermal Error Compensation Module for Intelligent Ultra Precision Turning Machine (iUPTM)

Reddy

Shanmugaraj

Vinod

et al. 2014

Procedia Materials Science

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Accuracy & precision are the main requirements for ultra precision machine tools. Many factors affect the performance of the system that in turns affect the product quality. Among all sources of errors, the thermo mechanical deformation errors are the main contributor for the overall geometrical errors. This paper mainly aims at establishment of methodology to compensate thermal deformation errors in real-time for ultra precision machine tools. The real-time thermal error compensation module has been developed and integrated to intelligent Ultra Precision Turning machine. The module includes temperatures as inputs, neural network algorithm for computing the thermal deformations errors, 'C' programming for real-time calculations and integration with open architecture CNC controller. The module runs in silent mode which avoids human intervention for correction of thermal deformation errors.

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Thermal error optimization modeling and real-time compensation on a CNC turning center

Cited by 85 publications

References 20 publications

Optimization of Temperature Level to Enhance Worker Performance in Automotive Industry

Optimization of Temperature Level to Enhance Worker Performance in Automotive Industry

Temperature distribution and thermal error prediction of a CNC feed system under varying operating conditions

Real-time Thermal Error Compensation Module for Intelligent Ultra Precision Turning Machine (iUPTM)

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