Cite this document

ASTM Suggested Citation Style, Chicago Manual of Style 17th Edition Publication Manual of the American Psychological Association, 6th Edition MLA Handbook for Writers of Research Papers 7th Edition URL

Articulated arm coordinate measuring machine (AACMM) is a kind of portable coordinate measuring equipment, which employs a series of rotating joints. In order to improve the measuring accuracy and repeatability of AACMM, it is essential to calibrate the kinematic parameters of AACMM. The calibration process is a kind of nonlinear optimization problem and can be solved by employing various optimization algorithms most including Levenberg–Marquardt algorithm (LMA) and trust region algorithm. Recently, evolutionary computation (EC) has been extensively studied and applied to many engineering problems, since they have some positive features such as easy implementation, broad applicability and robust mechanism of escaping from the local optimum. Chaos optimization algorithm (COA) is one of the evolutionary computation, which utilizes chaotic numerical sequences. In this article, a new kinematic calibration approach for AACMM is proposed by using niching chaos optimization algorithm (NCOA). A hybrid objective function for kinematic calibration is proposed that reflects the various performance tests including single-point articulation performance test, effective diameter performance test and volumetric performance test. Levenberg–Marquardt algorithm and niching chaos optimization algorithm are applied for calibrating the kinematic parameters. Niching chaos optimization algorithm shows competitive calibration performance to Levenberg–Marquardt algorithm. The experimental results demonstrate that the measurement accuracy calibrated using NCOA has been better than that of using LMA in terms of the root-mean-square deviation. The experimental results demonstrate that the measurement accuracy calibrated using NCOA has been better than that of using LMA in terms of the root-mean-square deviation.