Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement.

Area of Science:

• Metrology and Measurement Science
• Statistical Modeling
• Machine Learning

Background:

• Surface measurement on multi-sensor instruments often requires efficient and accurate data acquisition.
• Traditional methods may lack the flexibility to adapt to complex surface geometries and varying scales.
• Bayesian inference offers a probabilistic framework for handling uncertainty in measurements.

Purpose of the Study:

• To develop an intelligent surface measurement system for multi-sensor instruments.
• To leverage Gaussian processes for adaptive sampling and data fusion.
• To enhance measurement accuracy and efficiency through a Bayesian approach.

Main Methods:

• Utilizing Gaussian processes as the mathematical foundation for Bayesian inference.
• Implementing multi-feature classification to extract geometric surface properties at different scales.
• Employing multi-dataset regression to fuse data from multiple sensors using a composite covariance kernel.
• Developing an adaptive sampling strategy based on the credibility of the fused Gaussian process model.

Main Results:

• A novel Bayesian inference system for intelligent surface measurement was successfully developed.
• The system demonstrated effective multi-feature classification and multi-dataset regression capabilities.
• Adaptive sampling strategies were realized through a consecutive learning process with full Bayesian treatment.
• The Gaussian process model exhibited flexibility in handling complex surfaces through various covariance kernels.

Conclusions:

• The proposed Gaussian process-based Bayesian inference system enables intelligent and adaptive surface measurement.
• The system effectively fuses multi-sensor data and refines sampling strategies for improved accuracy.
• This approach offers a flexible and statistically robust solution for complex surface metrology.