Abstract
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.
Highlights
Hu et al.7 determined the measurement space according to the structural parameter analysis of articulated arm coordinate measuring machine (AACMM), divided the measurement space of the measuring machine into several small cubic areas at equal intervals, and used the improved ant colony algorithm to find the maximum measurement error of each small area, but the convergence speed of the algorithm was slow and it was easy to fall into local optimum
The results show that the improved fly optimization algorithm (FOA) algorithm and particle swarm optimization (PSO) algorithm can solve the error distribution of the articulated arm coordinate measuring machine, and the optimal measurement area in the measurement space is 0mm4X420mm, À20mm4 Y40mm, 0mm4Z420mm, the maximum measurement error of this area is 0.056 mm
The results show that lgms-foa algorithm falls into local optimal solution, PSO algorithm, ACO algorithm and improved FOA algorithm are not trapped in local optimal solution, and the improved FOA algorithm has better convergence speed and single optimization operation speed
Summary
The error sources of articulated arm coordinate measuring machine (AACMM) mainly include circular encoder measurement errors with a sinusoidal variation law, structural parameter errors, thermal deformation errors, force deformation errors, motion errors, and data acquisition system errors; in addition, these error sources include probe errors and measurement errors caused by improper measurement methods and measurement environment with complex variation laws given the series mechanical structure of the AACMM, thereby resulting in amplification effect of each joint error; the measurement accuracy of the measuring machine is lower. The object measured can be placed in the optimal measuring zone of the measuring machine to achieve high-precision measurement of the low-precision AACMM; the maximum measurement error in this zone is the minimum. To determine the precise location of the optimal measuring zone for the measuring machine, the variation law of each error component, transfer relation, and comprehensive error distribution law of the measuring machine must be analyzed to find a precise determination location of the optimal measuring zone through an appropriate optimization algorithm. Zheng selected v-SVM and RBF kernel function to construct the spatial error distribution model of flexible coordinate measuring machine, and obtained the optimal measurement area model aiming at single point measurement and spatial distance measurement by using support vector machine theory This method is not practical, and the model is based on a large number of measured data, and its measurement error does not include the angle measurement error of circular encoder. To apply the FOA with fast search speed and good real-time performance to the real-time determination of the optimal measurement area of the measuring machine, an improved FOA is proposed to enhance its global optimization ability and algorithm stability, and the optimization results of different algorithms are compared and verified.
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