Measurement accuracy improvement and area optimization of CMM based on IPSO-SAA

Abstract Coordinate measuring machines (CMMs) are crucial in ensuring dimensional and technological accuracy of parts in fields demanding high-accuracy production, such as aerospace, shipbuilding, automotive, and large-scale equipment manufacturing industries. Uneven error distribution in the measurement space of CMMs significantly affects the accuracy. To achieve the high-precision measurement using CMMs, this paper proposes an optimal measurement area identification method based on the improved particle-swarm optimization-simulated annealing algorithm (IPSO-SAA). First, the laser tracer multi-station measurement technique was used to determine the volumetric error of the planning points in the CMM measurement space. Then, the geometric error was modelled using the least squares method. Building on this, an error model incorporating the geometric errors and volumetric errors of CMMs was established, and objective functions for straight lines and circular arcs were constructed. Finally, the IPSO-SAA algorithm was applied to determine the ideal measurement area of the CMM under varying measurement conditions. A multi-station measuring system utilizing a laser tracer and validation platform was developed for empirical evidence. The results show that when the measurand object was a 50 mm straight line, the maximum error was 1.13 µm in the optimal measurement area, and 2.47 µm in the worst measurement area, resulting in a 54.3% improvement in measurement accuracy. When the measurand object was a circular arc with a diameter of 15.8747 mm, the maximum error was 1.8 µm in the optimal measurement area and 11.7 µm in the worst measurement area, resulting in an 84.6% improvement in measurement accuracy.