An approach to enhancing machining accuracy of five-axis machine tools based on a new sensitivity analysis method

Autor: Haohao Tao, Tongjie Li, Feng Chen, Jinwei Fan, Ri Pan
Rok vydání: 2022
Předmět:
Zdroj: The International Journal of Advanced Manufacturing Technology. 124:2383-2400
ISSN: 1433-3015
0268-3768
DOI: 10.1007/s00170-022-10365-2
Popis: Identification of key geometric errors is an essential prerequisite for improving the machining accuracy of five-axis machine tools. This paper presents a new sensitivity analysis (SA) method to extract key geometric errors, and then to improve the machining performance of machine tools by compensating key geometric error components. Development of geometric error prediction model is involved to obtain geometric error values at arbitrary positions at first. Based on the multi-body system theory and flank milling theory, the machining error model is developed, which considers 37 geometric errors. Then, a new SA method is introduced by taking the machining error model as sensitivity analysis model and taking the geometric errors as analytical factors. Meanwhile, a sensitivity index, which has the characteristics of simple expression and clear physical meaning, is proposed, i.e., the peak value of the machining error caused by each geometric error. Moreover, the simulations analysis is carried out to obtain the sensitivity coefficient of each geometric error and the key error components. Finally, the validity and correctness of the proposed method is demonstrated by the experiments. Furthermore, the SA method can be extended to multi-axis machine tools.
Databáze: OpenAIRE
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