Free-form surface machining error compensation based on SSA-ICEEMDAN and blind source separation

To improve the CNC machining accuracy of free-form surface parts, a method of combining sparrow search algorithm-improved complete ensemble empirical mode decomposition with adaptive noise (SSA-ICEEMDAN) and blind source separation is proposed to reduce the influence of system errors. First, SSA is used to optimize the parameters of ICEEMDAN. the number of realization (NR) of ICEEMDAN and noise standard deviation (Nstd) of white Gaussian noise were optimized via the SSA using the minimum envelope entropy as the fitness function ICEEMDAN was subsequently used to decompose the machining errors into several intrinsic mode functions (IMFs) and one residual. The correlation coefficient and marginal spectrum of each intrinsic mode function were subsequently calculated via the MATLAB platform, the main frequency components were determined via the marginal spectrum distribution, and the intrinsic mode functions were grouped. Finally, the system errors and random errors were decomposed via the fast independent component analysis (FastICA) algorithm on the basis of kurtosis. The systematic errors decomposed from the machining errors are used to compensate the detection points along the normal direction, thereby reducing the influence of systematic errors. According to the system errors decomposed from the machining error, the detection point was modified along the normal direction. The surface was reconstructed via UG software, and a new machining code was generated from the reconstructed surface to compensate for system errors. The simulation results show that, compared with ensemble empirical mode decomposition (EEMD) and ICEEMDAN, the method combining SSA-ICEEMDAN and blind source separation has a larger signal-to-noise ratio (32.4372) and a smaller root-mean-square error (0.0055). The example of machining a free-form surface part shows that the surface accuracy increased by 91.6% after compensation. The simulation and experimental results show that this method can effectively improve the machining accuracy of free-form surface parts.