arXiv subjects

Hamidreza Samadi

Publications and source records attributed to Hamidreza Samadi.

3 recordsLinked to original sources

arXiv2026

Tolerance-Dependent Inspection Disagreement Between a Fixed CMM and a Portable Articulated-Arm CMM

Fixed coordinate measuring machines (CMMs) and portable articulated-arm CMMs are often assigned to the same inspection task, but their nominal accuracy specifications do not show whether a change of instrument will preserve the disposition of a part. The question is not simply how far the two results differ, but whether that difference crosses the tolerance boundary. We examined this issue with recorded measurements of cylindrical, cubic, and spherical features under nominal 20 {\deg}C and 30 {\deg}C conditions. Repeated records and two roughness profiles without sufficient acquisition information were removed, leaving six dimensional and four form profiles. For each dimensional feature, the distances of the two system means from nominal define the exact tolerance interval in which the systems receive opposite direct labels. The fixed-CMM stream was approximately 11.2 {\mu}m higher than the articulated-arm stream at both conditions. All four form profiles fell on opposite sides of the recorded 10 {\mu}m upper limit. The dimensional disagreement intervals also overlapped strongly; their mean widths were 6.573 {\mu}m at 20 {\deg}C and 4.995 {\mu}m at 30 {\deg}C. The results clarify why an average difference between instruments is not, by itself, a measure of substitution risk. The proposed tolerance map identifies the feature-tolerance combinations for which instrument choice can change the recorded inspection label and, therefore, where a controlled equivalence study and a task-specific uncertainty budget are needed before substitution.

cs.RO↗

arXiv2025

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

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.

cs.CV↗

arXiv2024

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

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.

cs.CE↗