An identification method for crucial geometric errors of gear form grinding machine tools based on tooth surface posture error model. (August 2019)

• Record Type: Journal Article
• Authors: Xia, Changjiu; Wang, Shilong; Sun, Shouli; Ma, Chi; Lin, Xiaochuan; Huang, Xiaodiao
• Is Part Of: Mechanism and machine theory. Volume 138(2019)
• Page Start: 76
• Page End: 94
• Publication Date: 2019-08
• Subjects: Crucial geometric errors -- Error identification -- Tool posture error model -- Tooth surface posture error model -- Improved Sobol method; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2019.03.016
• Languages: English
• ISSNs: 0094-114X

Abstract

Highlights: The TSPEM is built to reflect effects of geometric errors on machining precision. Crucial errors are identified based on the TSPEM and the improved Sobol method. The proposed method is verified with the modification experiment of crucial errors. The TSPEM is more effective for the identification of crucial errors than the TPEM. Abstract: The identification for crucial geometric errors of gear form grinding machine tools is essential for the later error accurate compensation, but complicated and difficult. To reveal the mapping rules between geometric errors and machining errors, the tool posture error model (TPEM) was first constructed but it failed to consider the effects of grinding process. Hence, the tooth surface posture error model (TSPEM) was established based on the discretization of the grinding trajectory and the calculation of the grinding contact lines. Then the improved Sobol method was performed to analyze the error sensitivity and identify the crucial errors and sensitive components by considering the stochastic and intercoupling characteristics of geometric errors. To validate the effectiveness of the proposed method, the identified results were compared with that of the Morris method and the modification experiment of crucial errors was conducted. It shows that the tooth surface errors in the δy -direction are reduced by 68.75% for the TSPEM and 43.36% for the TPEM and that the TSPEM is more effective for the identification of crucial geometric errors.