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

Hamidreza Samadi, Md Manjurul Ahsan, Shivakumar Raman

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.

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

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.

Paper Structure

This paper contains 14 sections, 8 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Objectives of the Study
  3. Methodology
  4. Experimental Setup
  5. Results and Analysis
  6. Measurement Variability Analysis
  7. Impact of Environmental Factors
  8. Machine Learning Model Performance
  9. Anomaly Detection
  10. Digital Twin Framework Performance
  11. Continuous Learning Pipeline Performance
  12. Discussion
  13. Limitations and Future Work
  14. Conclusion

Figures (8)

  • Figure 1: Illustration of (a) CMM and (b) FARO Arm machine used during this study for precise dimensional measurement and real-time inspection of parts, respectively.
  • Figure 2: Proposed Metrology and Manufacturing-Integrated Digital Twin (MM-DT) framework.
  • Figure 3: Framework for integrating metrology data into a DT system, consisting of four key stages: (1) Data Integration, where data is acquired from devices (e.g., CMM, FARO Arm) and environmental sensors, followed by standardization and preprocessing; (2) Analytics Layer, which applies statistical and machine learning models, enables real-time anomaly detection (e.g., Isolation Forest), adaptive modeling, and uncertainty quantification; (3) Visualization, where interactive 3D models, real-time dashboards, and trend analysis tools are used to track and report data; and (4) Decision Support, which provides maintenance recommendations, quality control alerts, parameter optimization, and "what-if" process analysis.
  • Figure 4: Comparison of measurement deviations between CMM and FARO Arm.
  • Figure 5: Impact of temperature on measurement deviations for CMM and FARO Arm.
  • ...and 3 more figures