One of the major limitations of productivity and quality in metal cutting is the machining accuracy of machine tools. The machining accuracy is affected by geometric errors, thermally-induced errors, and the deterioration of the machine tools. Geometric and thermal errors of machine tools should be measured and compensated to manufacture high quality products. In metal cutting, the machining accuracy is more affected by thermal errors than by geometric errors. About 40~70% of the errors occurring in machine tools account for the thermal errors. Therefore, a key requirement for improving the machining accuracy and product quality is to reduce the geometric and thermal errors of machine tools. Since the thermal errors of machine tools change over time by a variety of in-process heat sources, they should be measured and compensated on the machine.
This dissertation models of the geometric and thermal errors for error analysis and develops on-the-machine measurement system by which the volumetric error are measured and compensated. The geometric error is modeled using form shaping function (FSF) which is defined as the mathematical relationship between form shaping motion of machine tool and machined surface. Error parameters in the FSF are assumed to follow quadratic function. The constant terms included in the error model are found from the measurement results of the measurement system. Then, the error map is constructed by using the measurement results and error model. The thermal error is modeled by means of angularity errors of a column and thermal drift error of the spindle unit which are measured by a star type stylus and a designed spherical ball artifact. The thermal error model is used for constructing 3-dimensional thermal error map.
The developed measurement system consists of the spherical ball artifact(SBA), the touch probe unit with a star type stylus, the thermal data logger and the personal computer. Using the measurement system, geometric and thermal errors can be measured and analyzed on the machine while in process. Experiments, performed with the developed measurement system, show that the system provides a high measuring accuracy, with repeatability of ±2㎛ in X, Y and Z directions.
A neural network model is used for predicting thermal errors occurring while in process. The generalized delta rule model, a back propagation algorithm proposed by Rumelhart, is used to predict the thermal errors of machine tools caused by the temperature changes occurring at each point. Since the reliability of the neural network model depends on the learning condition, appropriate input patterns of the input layer must be suitable to the characteristics of machine tools. Thirteen thermocouples have been installed on the machine tools. The characteristics of the temperature were divided into three groups based on the degree of the temperature changes measured at several points on the machine tools. Eight different values of temperatures, selected among three groups, are used for input patterns of the neural network model. The output patterns of the neural network model are three scalar errors and three volumetric errors. The experimental results show that the modified model gives better predictive reliability than the 'basic' model under random experimental conditions.
The geometric and thermal errors are compensated by means of a custom macro of the machine tool controller and a method in which CL data are modified. As a compensation result, the machining accuracy of machine tools is improved by the error compensation.
It is believed that the developed measurement system can be also applied to the machine tools with CNC controller. In addition, machining accuracy and product quality can be improved by using the developed measurement system when the spherical ball artifact is mounted on the modular fixture.
