Metrology and Manufacturing-Integrated Digital Twin (MM-DT) for Advanced Manufacturing: Insights from CMM and FARO Arm Measurements

TL;DR

This paper introduces a novel Metrology and Manufacturing-Integrated Digital Twin (MM-DT) framework that leverages data from CMM and FARO Arm measurements, combined with machine learning, to enhance manufacturing precision and quality.

Contribution

It presents a new integrated digital twin framework that combines metrology data with machine learning for improved measurement accuracy in manufacturing.

Findings

Achieved an R2 score of 0.91 in predicting measurement deviations.

Reduced RMSE to 1.59 micrometers using ensemble machine learning.

Demonstrated improved metrology process efficiency and insights for manufacturing quality.

Abstract

Metrology, the science of measurement, plays a key role in Advanced Manufacturing (AM) to ensure quality control, process optimization, and predictive maintenance. However, it has often been overlooked in AM domains due to the current focus on automation and the complexity of integrated precise measurement systems. Over the years, Digital Twin (DT) technology in AM has gained much attention due to its potential to address these challenges through physical data integration and real-time monitoring, though its use in metrology remains limited. Taking this into account, this study proposes a novel framework, the Metrology and Manufacturing-Integrated Digital Twin (MM-DT), which focuses on data from two metrology tools, collected from Coordinate Measuring Machines (CMM) and FARO Arm devices. Throughout this process, we measured 20 manufacturing parts, with each part assessed twice under different temperature conditions. Using Ensemble Machine Learning methods, our proposed approach predicts measurement deviations accurately, achieving an R2 score of 0.91 and reducing the Root Mean Square Error (RMSE) to 1.59 micrometers. Our MM-DT framework demonstrates its efficiency by improving metrology processes and offers valuable insights for researchers and practitioners who aim to increase manufacturing precision and quality.