1. School of Mechanical and Electrical Engineering,Shenyang Aerospace University, Shenyang, Liaoning 110136, China
2. School of Artificial Intelligence,Shenyang Aerospace University, Shenyang, Liaoning 110136, China
Abstract Aiming at the shortcomings in the minimum zone evaluation of flatness errors, such as easy to fall into local optimums and low convergence speed and low accuracy, a flatness error evaluation method based on the improved sparrow search algorithm (ISSA) was proposed. Firstly, Kent chaotic sequence was applied to generate initial population instead of traditional Logistic chaotic sequence, which is with better ergodicity and can enhance the global searching ability of the ISSA algorithm. Then, a new opposition-learning strategy based on the optical lens imaging principle was applied to avoid the algorithm failing to jump out of the local optimums. The effectiveness of ISSA was proved by the classic test functions, and the result was better than SSA. Finally, the proposed approach ISSA was used to evaluate flatness errors and compared with the other algorithms cited. The experimental results show that the optimal plane can be calculated in 0.4884s and 0.3705s can be saved by ISSA compared with SSA algorithm, and the calculation accuracy compared with the flatness error evaluation methods using the least square method, genetic algorithm and particle swarm optimization algorithm is reduced by 18.0325μm、2.3325μm、6.1325μm respectively。 Flatness error evaluation method based on ISSA has advantages in optimization efficiency, solution quality and stability, and calculating precision. It is suitable for the evaluation of position measuring instruments such as coordinate measuring machines.
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2023, Vol. 44 
