To address the problems of the low accuracy of geometric error identification and incomplete identification results of the linear axis detection of computer numerical control (CNC) machine tools, a new 21-item geometric error identification method based on double ball-bar measurement was proposed. The model between the double ball-bar reading and the geometric error term in each plane was obtained according to the three-plane arc trajectory measurement. The mathematical model of geometric error components of CNC machine tools is established, and the error fitting coefficients are solved through the beetle antennae search particle swarm optimization (BAS–PSO) algorithm, in which 21 geometric errors, including roll angle errors, were identified. Experiments were performed to compare the optimization effect of the BAS–PSO and PSO and BAS and genetic particle swarm optimization (GA–PSO) algorithms. Experimental results show that the PSO algorithm is trapped in the local optimum, and the BAS–PSO is superior to the other three algorithms in terms of convergence speed and stability, has higher identification accuracy, has better optimization performance, and is suitable for identifying the geometric error coefficient of CNC machine tools. The accuracy and validity of the identification results are verified by the comparison with the results of the individual geometric errors detected through laser interferometer experiments. The identification accuracy of the double ball-bar is below 2.7 µm. The proposed identification method is inexpensive, has a short processing time, is easy to operate, and possesses a reference value for the identification and compensation of the linear axes of machine tools.

针对计算机数控机床几何误差识别精度低，线性轴检测识别结果不完全的问题，提出了一种基于双球杆测量的21项几何误差识别新方法。根据三平面弧形轨迹的测量，获得了双球杆读数与每个平面中的几何误差项之间的模型。建立了数控机床几何误差分量的数学模型，并通过甲虫触角搜索粒子群算法（BAS-PSO）求解误差拟合系数，确定了21个几何误差，包括侧倾角误差。进行了实验，以比较BAS–PSO和PSO以及BAS和遗传粒子群优化（GA–PSO）算法的优化效果。实验结果表明，PSO算法被困在局部最优中，而BAS–PSO在收敛速度和稳定性方面均优于其他三种算法，具有更高的识别精度，更好的优化性能，适合于识别最优解。数控机床的几何误差系数 通过与通过激光干涉仪实验检测到的单个几何误差的结果进行比较，验证了鉴定结果的准确性和有效性。双球杆的识别精度低于2.7 µm。所提出的识别方法便宜，处理时间短，易于操作，