Machining accuracy is the most critical indicator to evaluate the machining quality of parts in metal cutting industry. However, it is difficult to be identified before real cutting, because of a variety of error sources presented in a machining process system, such as assembly inaccuracy of machine tool, deformation caused by temperature variation and dynamic cutting force, tool wear, servo lag and so on. Consequently, it is difficult to determine whether a new machining process can satisfy accuracy requirements beforehand. Traditionally, a machining process is validated through the “trial and error” approach, which is time consuming and costly. If machining accuracy can be predicted to a large extent, a rational process can be planned to ensure the precision of parts and even to maximize resource utilization without trial cuts. For this purpose, this work focuses on machining accuracy prediction for five-axis peripheral milling based on the geometric errors. An error synthesis modeling method is proposed to integrate the geometric errors of the process system, including machine tool geometric error, workpiece locating error, cutting tool dimension error and setup error. From a multi-body system point of view, all these errors are synthesized to generate position error of the cutting contact point in the workpiece coordinate system. Then the machining error is obtained by projecting the position error to the workpiece normal vector, which can be measured by a coordinate measuring machine. The prediction model has been evaluated by a cutting test with our in-house-developed prototype software. The result shows that the proposed method is feasible and effective.
This article presents a new integrated geometric model that takes worktable as reference for five-axis machine tools. It could simplify the description of different machine kinetic structures and uniform the measurement coordinate system and machining coordinate system. Based on this model, a new method of measuring geometric errors with conventional instruments is proposed. It could break through some existing limitations, such as special instrument, specific machine kinetic structures, or errors incompletion. In addition, some deviation errors in measuring process could be eliminated to improve accuracy further. Finally, a series of experiments are conducted on a five-axis machine tool with rotary worktable and tilting head. The results show that the error model and measuring method are effective and applicable.
Non-contact R-test is an instrument to measure the synchronous errors of five-axis machine tools. However, there are still some deficiencies in its researches, such as the difficult and laborious calibrations. How to systematically improve the measurement accuracy with a good balance to minimum cost is a real problem in guiding practice. This paper proposes a new systematic optimization method to solve this problem based on comprehensive understanding of the non-contact R-test in terms of structure parameters and relations. Firstly, the algorithm for sphere center coordinates is established based on the self-adaptive differential evolution algorithm to obtain the definite computational accuracy and efficiency. Secondly, the parameters of the fixture structure are optimized to maximize the measurement stability, measuring space and noninterference space. Thirdly, the on-machine calibration is performed to replace pre-calibration and re-calibration, and to establish the positional relationships between sensors, the fixture and the machine tool simultaneously. It can reduce the difficulties of manufacture, maintenance and application. Fourthly, the measurement accuracy can be evaluated to determine whether the iterative optimization achieves the goal. The proposed method has been verified with case studies to support optimized non-contact R-test setting up, leading to cost-effective and accuracy test on five-axis machine tools.
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