The precise evaluation of the straightness error of an inner-hole axis is crucial for ensuring part machining quality and equipment performance. However, straightness error evaluation is a complex nonlinear optimization problem that challenges the balance between accuracy and computational efficiency. To address this, an improved sparrow search algorithm (ISSA) is presented in this paper to evaluate axis straightness error. Based on the minimum zone criterion, a model for evaluating the straightness error of the axis is established, and the optimization objective function is obtained.To address the limitations of the traditional Sparrow Search Algorithm (SSA), three improvements are made to the algorithm, including adopting the Sobol sequence to optimize the spatial distribution of the population, balancing the global exploration and local exploitation capabilities through a nonlinear inertia weight, and introducing a dual-sample learning mechanism and Cauchy mutation strategy to effectively avoid the local optimal trap and enhance the robustness of the algorithm. Then, function simulation experiments demonstrate that ISSA achieves faster convergence and higher optimization precision. Finally, the algorithm was applied to the straightness error evaluation of the axis of hole-type parts. Experimental results show that under the same conditions, compared with the grey wolf optimizer, whale optimization algorithm, dung beetle optimizer, classic sparrow search algorithm, and multi-strategy improved sparrow search algorithm, the proposed algorithm improves the evaluation accuracy by 55.6%, 15.64%, 65.44%, 41.38%, and 0.295%, respectively.
Du et al. (Fri,) studied this question.