Position-dependent milling process monitoring and surface roughness prediction for complex thin-walled blade component

Freeform surface parts, such as blades, exhibit complex structures and excellent aerodynamic performance, making them widely utilized in aerospace propulsion systems. However, monitoring and ensuring surface quality during the milling process of such components is challenging, leading to high scrap rates and unguaranteed processing efficiency. To address these issues, this paper investigated the milling process monitoring and position-dependent surface roughness prediction for the thin-walled blade with the material of Ti-6Al-4 V. The monitored blade-root acceleration signal was utilized to develop a discrete spatial vibration model based on the machining characteristics of the blade. This involved using Fourier transform and inverse techniques to combine the frequency response functions and cutting force for acceleration calculation, which was then compared to measured values to validate the model’s monitorability. Aiming at the surface roughness prediction, a predictive method for the entire machined surface was proposed, consisting of signal pre-processing, feature extraction and selection, and construction of extreme learning machine (ELM) model. Time-domain, frequency-domain, and time–frequency-domain methods were adopted for feature extraction. To enhance the generalization ability and accuracy of the predictive model, the maximal information coefficient was employed for correlation analysis, resulting in the selection of 12 features as input for the ELM-based surface roughness prediction system. Comparison of the measured and predicted surface roughness results revealed that all errors were less than 14%, with an average error of only 6.70%, demonstrating the validity and reliability of the prediction method. Notably, the proposed monitoring method does not interfere with the milling process and enables prediction of surface roughness at arbitrary positions on the entire surface during variable-parameter processing of freeform surface parts, thereby improving the quality and precision of the machined surface. The potential application of this paper lies in the final inspection of defects in industrial products and reducing the service risk of non-conforming parts.