Papers by Keyword: Thermal Error Compensation

Abstract: Thermal errors cause serious dimensional errors to a workpiece in precision machining. A feed drive system generates more heat through friction at contact areas, such as the Linear encoder and the guide, thereby causing thermal expansion which affects machining accuracy. Therefore, the thermal deformation of a Linear encoder is one of the most important objects to consider for high-accuracy machine tools. This paper analyzes the increase of the temperature and the thermal deformation of a Linear encoder feed drive system. During temperature variation is measured by using thermocouples , meanwhile, the thermal error of the guide is measured by a laser interferometer. A thermal error model is proposed in this study by using back propagation neural network (BPN). An experiment is carried out to verify the thermal error of the guide under different feed rates and environmental temperature.

Abstract: In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.

Abstract: Thermally induced errors and geometric errors are two main sources that affect the machine tool accuracy when machining. In the last decade, real time compensation method had received wide attention for its ability to reduce the thermal error cost–effectively. Although real-time thermal error compensation techniques have been successfully demonstrated in laboratories, several difficulties hinder its widespread application. The selection of temperature variables and the setup of the error measurement system are the most critical ones among these difficulties. In this paper, a new on line measurement system and a new model that predicts the thermal error of a turning center are developed. The on-line measurement system using a Renishaw’s LT02S probe system is capable of measuring thermal error of a CNC turning center in real cutting conditions. The neural network uses the cutting conditions as the mapping inputs to avoid problems occurred in the traditional temperature-error mapping model. Results show the proposed measurement system and prediction model can be used to accurately estimate the thermally induced error in real cutting conditions.

Abstract: Aiming to deal with thermal error of NC machine tool which can cause reduce of machining accuracy, this paper uses an external error compensation which interacts with NC controllers and PMAC multi-axis and then revises the tool path by adding the error tested in real-time by PMAC card. The processing accuracy is improved eventually. This method can compensate machine geometric errors and thermal errors in real-time. Comparing with other methods of error preventing, this method is more effective and affordable.

Abstract: Great efforts have been made to improve the accuracy of NC machine tools, within which thermal error compensation is one of the most efficient ways. A new thermal error compensation instrument which is based on thermal modal analysis for NC machine tools is introduced in this paper. OMRONsCJ2M-CPU11 is used as microcontroller, and SAILING TECHNOLOGYs STA-A08 temperature measuring modules as temperature transmitter. Through hardware and software design, high precision and stability can be achieved. By measuring several key points temperature and making use of a thermal error compensation theory, real-time thermal error compensation can be output to the machine tool, thus thermal error can be reduced.

Abstract: Thermal error is the main factor which affects the precision of gear hobbing. The paper discusses the individual feature of gear hobbing machine of thermal error, and uses the weighted grey correlativity to determine the key thermal error source of gear hobbing machine. Based on Least-square method, a fundamental model of thermal error compensation was constructed. An adaptive correction algorithm of thermal error compensation model on gear hobbing machine was proposed. An adaptive compensation method of thermal error on gear hobbing machine was given based on open numerical control system.

Abstract: CNC machine tool dynamic thermal error compensation has always been a hot issue to improving precision. This dissertation proposes a method of machine tool thermal error modeling during processing, based on Bayesian network theory, by describing the correlation between the various factors of generated the heat error, through the sample data, analyzed and simplified the intrinsic correlation between these various factors, established the basic thermal error compensation model, and used the network’s good characteristic of self-studying, combining the result of update collection data, continually modify the model to reflect the machining process condition changes. Finally, the experimental results show the feasibility of Bayesian network model, it was a stronger application for achieving the thermal error compensation.

Abstract: In order to fulfill the thermal error modeling of precision abrasive machining, a neural networks model is presented through analyzing thermal error sources of the grinding machine, the structure and algorithm of the neural networks is expatiated then；And because of reasonable sample and systematical training, the accuracy of thermal error models is improved. The hardware system for thermal error compensation is proposed finally, and an experiment is accomplished on the grinding machine. The result shows thermal errors is reduced from 6μm to 1μm.