Intelligent machining error prediction and compensation system based on convolutional neural networks and improved genetic algorithm

Machining errors continue to hinder dimensional accuracy in precision manufacturing. These errors arise from complex, nonlinear couplings among thermal deformation, tool wear, and fixture deviations—phenomena that traditional empirical models and statistical regression approaches often fail to capture adequately. Physics-based methods demand exhaustive parameter identification and tend to break down under changing operational conditions, which motivates the development of data-driven prediction strategies that can learn error patterns directly from process data. This study presents an integrated system that combines convolutional neural networks (CNN) with an improved genetic algorithm (GA) for machining error prediction and compensation. The CNN was chosen for its capacity to capture local patterns and short-term dependencies within multi-sensor time-series features, while the GA provides global optimization that circumvents local minima common to gradient-based compensation parameter tuning. Taking manually extracted time-domain and frequency-domain features from force, vibration, and thermal sensor signals as inputs, the CNN identifies hierarchical representations and predicts machining errors with a root mean square error of 3.42 micrometers, surpassing conventional methods by 38–47%. The improved GA employs adaptive crossover and mutation operators to optimize compensation parameters, yielding a 74.6% average error reduction across varied operating conditions. Experimental validation on a five-axis machining center processing aluminum alloy workpieces showed that the system reduces dimensional deviations from 12.8 to 2.9 micrometers and raises first-pass acceptance rates from 78.3 to 96.1%. This unified prediction-compensation framework marks a shift from reactive to proactive quality control in smart manufacturing settings.