Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology

TL;DR

This paper introduces a hybrid machine learning approach combining CNNs and decision trees to accurately predict geometric deviations in manufactured components from 3D surface data, enhancing quality control processes.

Contribution

It presents a novel hybrid ML framework utilizing high-resolution 3D surface data for precise geometric deviation prediction in manufacturing.

Findings

Prediction accuracy of 0.012 mm at 95% confidence

73% improvement over traditional statistical methods

Revealed correlations between manufacturing parameters and deviations

Abstract

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.