The Line laser gear measuring center (LLGMC) is an innovative gear measurement equipment that offers high efficiency but low accuracy. One crucial factor that influences its measurement accuracy is the presence of geometric errors. In this study, we conducted a thorough analysis of these geometric errors and proposed a method for modeling spatial errors. Instead of directly considering the geometric errors, we replaced them with the installation errors of the gear and line laser probe. This approach simplifies and improves the error transmission relationship. Subsequently, the installation errors are converted into a unified representation of the height error of the incident light from the line laser. A spatial error model that considers nine installation errors is then further established. By numerically calculating the sensitivity of different error sources, we effectively identified the errors that have a significant impact on the accuracy of LLGMC. Moreover, accuracy distribution is carried out to ensure that LLGMC can meet the measurement accuracy requirements for gears with a tolerance class of 6. This article provides a theoretical foundation for the structural design and accuracy assurance of LLGMC during the research and development phase.

The determination of the optimal measurement area of the articulated arm measuring machine belongs to the multi-dimensional function optimization problem under complex constraints. To realize high-precision measurement of low-precision articulated arm measuring machine, we analyze the working principle and error source of the measuring machine, and establish the optimization target model of the optimal measurement area in this paper. We propose a method for determining the optimal measurement area of an articulated arm measuring machine based on improved FOA. The basic FOA algorithm is improved, the historical optimal individual and population centroid information are added in the population iteration update process, and the fruit fly individuals in each iteration are directly used as the taste concentration judgment value, which increases cooperation and information sharing among fruit fly individuals, and improves the global optimization ability and stability of the algorithm. In the designated area of the measuring machine, we have carried out comparative experiments on the optimization results of improved FOA and basic FOA, ACO, PSO, AL-SC-FOA, LGMS-FOA, IPGS-FFO. Experimental results show that the improved FOA, ACO, PSO, and IPGS-FFO algorithms do not fall into local optimum, and the optimal measurement area determined by them is consistent with the optimization results of other algorithms, and is superior to other algorithms in convergence speed and stability, so it is more suitable for determining the optimal measurement area of articulated arm measuring machine.

In order to solve the problem that a traditional articulated arm coordinate measuring machine (AACMM) cannot measure automatically, a self-driven AACMM is proposed. Based on the self-driven AACMM design indicators, the length of the connecting rods of the self-driven AACMM was allocated. The self-driven AACMM virtual prototype was designed and assembled based on joint module selection and joint component design, and its measurement space range was also verified. The key components of the structural optimized design and dynamic performance analysis of motion parameters are adopted, respectively, to correct the positioning error of the self-driven AACMM’s probe caused by static and dynamic deformations. The static deformation of the structure caused by the self-driven AACMM’s weight was analyzed. The influence of the dynamic flexible deformation on the positioning error of the probe of the self-driven AACMM during the variable-speed approach and the constant-speed touch measurement stage was studied. The results show that the measurement space range of the self-driven AACMM designed in this paper can meet the design index of the measuring radius. The probe position error caused by static deformation of the measuring machine after structural optimization is reduced by an order of magnitude, and the equivalent stress of the mechanical structure of the self-driven AACMM is within the allowable stress of the material. The positioning error of the probe caused by the dynamic deformation of the self-driven AACMM structure meets the positioning accuracy index. During the stage of self-driven AACMM constant-speed touch measurement, the instantaneous position error of the probe changes linearly with the touch time and the optimal touch speed (6.6 mm s ⁻¹ , 6.4 mm s ⁻¹ ) exists to minimize the probe positioning error. During the variable-speed approach stage, the influence of the angular acceleration and the angular velocity of each joint on the positioning error of the probe is negligible when the self-driven AACMM is in the typical posture. In the extreme posture, with the optimal joint angular acceleration ( 0.00055 ∘ s − 2 ) and angular velocity ( 0.0185 ∘ s − 1 ), the inertial force of the measuring machine structure and the instantaneous position error of the probe are the smallest, and the movement stability is the best. The structural optimization design and motion parameter performance analyses of the self-driven AACMM can provide a theoretical research foundation for subsequent motion parameter optimization and dynamic error compensation modeling.

The self-driven articulated arm coordinate measuring machine (AACMM) is a new non-orthogonal flexible coordinate measuring equipment providing automatic positioning and measurement with integrated joint modules introduced to its rotary joints. The dynamic measuring accuracy is the crucial indicator of the self-driven AACMM performance. However, the part manufacturing assembly error and structural dynamic deformation error will seriously reduce the dynamic measuring accuracy. Therefore, a modeling compensation approach based on error propagation and accumulation mechanism analysis of the above two aspects is necessarily required to preserve the automatic measuring accuracy of the self-driven AACMM. In this paper, the authors propose an innovative modeling method for evaluating and compensating for the dynamic measuring errors of the self-driven AACMM. The ideal measuring model is constructed assisted with on the classical Denavit–Hartenberg parameter methodology and the complex structure of the self-driven AACMM. The source and its propagation mechanism of the dynamic error from the self-driven AACMM were analyzed systematically to decouple the superposed error impact on measuring precision. The dynamic measurement error compensation mechanism model is presented considering the influence of instantaneous spatial attitude on the dynamic structural parameters of self-driven AACMM. Experimental results indicate that the single-point repeatability measurement error fluctuation of the self-driven AACMM is 0.046 mm. The radius measuring error of the gauge ball reduces from −0.26 mm to −0.19 mm after the error model compensation. Additionally, the measuring accuracy is demonstrated to be effectively improved by the dynamic error compensation mechanism model.