Dynamic error modeling of CMM based on Bayesian statistical principle

Hong-tao Yang; Shen-Wang Lin; Ye-tai Fei; Li Sheng; Zhen-ying Cheng

doi:10.1117/12.819553

31 December 2008 Dynamic error modeling of CMM based on Bayesian statistical principle

Hong-tao Yang, Shen-Wang Lin, Ye-tai Fei, Li Sheng, Zhen-ying Cheng

Proceedings Volume 7130, Fourth International Symposium on Precision Mechanical Measurements; 71300H (2008) https://doi.org/10.1117/12.819553
Event: Fourth International Symposium on Precision Mechanical Measurements, 2008, Anhui, China

Abstract

The dynamic error sources of CMM were analyzed and the character of the dynamic error data was investigated in this paper. Based on the character, the dynamic error model of CMM was built by using Bayesian statistical principle combined with the standard quantity interposition method. The specific error model building procedures was deduced. The CMM dynamic error separating and contrasting experimental devices were designed by using the laser interferometer and the measuring block group. The theoretical analysis and the experiment result indicate that all influences of the CMM dynamic error sources is considered in the model building method by using Bayesian statistical principle combined with the standard values interposition method which meets the CMM working condition. The error model accuracy reaches 2.4 μm and meets the CMM demand. The needed error data size is greatly reduced by using the dynamic error model building method. The error separating principle by lapping-in the measuring block group is simple, which is implemented easily and meets the timing dynamic error correcting needs of the ordinary CMM user.

Citation Download Citation

Hong-tao Yang, Shen-Wang Lin, Ye-tai Fei, Li Sheng, and Zhen-ying Cheng "Dynamic error modeling of CMM based on Bayesian statistical principle", Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 71300H (31 December 2008); https://doi.org/10.1117/12.819553

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available