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.

翻译：计量学作为测量的科学，在先进制造中对于确保质量控制、工艺优化与预测性维护起着关键作用。然而，由于当前对自动化的侧重以及集成精密测量系统的复杂性，其在先进制造领域常被忽视。多年来，先进制造中的数字孪生技术因其通过物理数据集成与实时监控应对这些挑战的潜力而备受关注，但其在计量学中的应用仍十分有限。鉴于此，本研究提出了一种新颖的框架——计量与制造集成数字孪生，该框架聚焦于从两种计量设备（三坐标测量机与FARO Arm测量臂）采集的数据。在此过程中，我们测量了20个制造零件，每个零件均在两种不同温度条件下各评估一次。通过采用集成机器学习方法，我们所提出的方案能够精准预测测量偏差，取得了0.91的R2分数，并将均方根误差降低至1.59微米。我们的MM-DT框架通过优化计量流程证明了其高效性，并为致力于提升制造精度与质量的研究人员与实践者提供了宝贵的见解。