Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology
It addresses the problem of maintaining dimensional precision in precision manufacturing for automated quality control, predictive maintenance, and design optimization, though it appears incremental as it combines existing methods.
This study tackled the challenge of accurately forecasting geometric deviations in manufactured components using 3D surface analysis, achieving a prediction accuracy of 0.012 mm at a 95% confidence level, which is a 73% improvement over conventional methods.
This study addresses the challenge of accurately forecasting geometric deviations in manufactured components using advanced 3D surface analysis. Despite progress in modern manufacturing, maintaining dimensional precision remains difficult, particularly for complex geometries. We present a methodology that employs a high-resolution 3D scanner to acquire multi-angle surface data from 237 components produced across different batches. The data were processed through precise alignment, noise reduction, and merging techniques to generate accurate 3D representations. A hybrid machine learning framework was developed, combining convolutional neural networks for feature extraction with gradient-boosted decision trees for predictive modeling. The proposed system achieved a prediction accuracy of 0.012 mm at a 95% confidence level, representing a 73% improvement over conventional statistical process control methods. In addition to improved accuracy, the model revealed hidden correlations between manufacturing parameters and geometric deviations. This approach offers significant potential for automated quality control, predictive maintenance, and design optimization in precision manufacturing, and the resulting dataset provides a strong foundation for future predictive modeling research.