Precision Calibration of Industrial 3D Scanners: An AI-Enhanced Approach for Improved Measurement Accuracy
DOI:
https://doi.org/10.5281/zenodo.11080006Keywords:
Artificial Intelligence, Industrial 3D Scanners, Precision Calibration, Measurement Accuracy, Machine Learning Algorithms, Sensor Fusion, Data AnalyticsAbstract
With the rapid development of intelligent manufacturing, there are important and challenging tasks in many aspects, especially in the calibration of 3D scanners. In order to improve the calibration accuracy, this paper proposes an innovative method that utilizes artificial intelligence (AI) for calibration. As we all know, precision 3D scanning is very important in many industrial applications. However, in complex environments, traditional calibration methods are often unable to meet the required accuracy requirements. To overcome the above limitations, we propose an innovative approach that combines advanced AI algorithms with traditional calibration processes. Through comprehensive and profound research, we use artificial intelligence enhanced technology to improve measurement accuracy. This reduces both time and resource costs. This research not only introduces a new calibration method for the field of industrial metrology, but also promotes the application of artificial intelligence in the field of precision engineering.
Downloads
References
Thurzo, A., Kosnáčová, H. S., Kurilová, V., Kosmeľ, S., Beňuš, R., Moravanský, N., ... & Varga, I. (2021, November). Use of advanced artificial intelligence in forensic medicine, forensic anthropology and clinical anatomy. In Healthcare (Vol. 9, No. 11, p. 1545). MDPI.
Su, J., Nair, S., & Popokh, L. (2023, February). EdgeGym: A Reinforcement Learning Environment for Constraint-Aware NFV Resource Allocation. 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC), 1–7. doi:10.1109/ICAIC57335.2023.10044182
Zeng, L., Li, H., Xiao, T., Shen, F., & Zhong, Z. (2022). Graph convolutional network with sample and feature weights for Alzheimer’s disease diagnosis. Information Processing & Management, 59(4), 102952.
Deng, Y., Kesselman, C., Sen, S., & Xu, J. (2019, December). Computational operations research exchange (core): A cyber-infrastructure for analytics. In 2019 Winter Simulation Conference (WSC) (pp. 3447-3456). IEEE.
Liu, S., Wu, K., Jiang, C., Huang, B., & Ma, D. (2023). Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach. arXiv preprint arXiv:2401.00534.
Shangguan, Z., Zheng, Z., & Lin, L. (2021). Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data. arXiv preprint arXiv:2111.14929.
Liao, J., Kot, A., Guha, T., & Sanchez, V. (2020). Attention Selective Network for Face Synthesis and Pose-Invariant Face Recognition. In textit{2020 IEEE International Conference on Image Processing (ICIP)} (pp. 748-752). https://doi.org/10.1109/ICIP40778.2020.9190677
Bao, W., Che, H., & Zhang, J. (2020, December). Will_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT. In A. Herbelot, X. Zhu, A. Palmer, N. Schneider, J. May, & E. Shutova (Eds.), Proceedings of the Fourteenth Workshop on Semantic Evaluation (pp. 301–306). doi:10.18653/v1/2020.semeval-1.
Zhou, H., Lou, Y., Xiong, J., Wang, Y., & Liu, Y. (2023). Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 6(1), 103-106.
Su, J., Nair, S., & Popokh, L. (2022, November). Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning. 2022 IEEE Ninth International Conference on Communications and Networking (ComNet), 1–7. doi:10.1109/ComNet55492.2022.9998475
Jin, X., Katsis, C., Sang, F., Sun, J., Bertino, E., Kompella, R. R., & Kundu, A. (2023). Prometheus: Infrastructure Security Posture Analysis with AI-generated Attack Graphs. arXiv preprint arXiv:2312.13119.
Jin, X., Katsis, C., Sang, F., Sun, J., Bertino, E., Kompella, R. R., & Kundu, A. (2023). Prometheus: Infrastructure Security Posture Analysis with AI-generated Attack Graphs. arXiv preprint arXiv:2312.13119.
Xiao, T., Zeng, L., Shi, X., Zhu, X., & Wu, G. (2022, September). Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 406-415). Cham: Springer Nature Switzerland.
Xu, J., & Sen, S. (2023). Compromise policy for multi-stage stochastic linear programming: Variance and bias reduction. Computers & Operations Research, 153, 106132.
Shangguan, Z., Lin, L., Wu, W., & Xu, B. (2021). Neural process for black-box model optimization under bayesian framework. arXiv preprint arXiv:2104.02487.
Liao, J., Sanchez, V., & Guha, T. (2022). Self-Supervised Frontalization and Rotation Gan with Random Swap for Pose-Invariant Face Recognition. In textit{2022 IEEE International Conference on Image Processing (ICIP)} (pp. 911-915). https://doi.org/10.1109/ICIP46576.2022.9897944
Popokh, L., Su, J., Nair, S., & Olinick, E. (2021, September). IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement. 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 1–7. doi:10.23919/SoftCOM52868.2021.9559076
Jin, X., & Wang, Y. (2023). Understand Legal Documents with Contextualized Large Language Models. arXiv preprint arXiv:2303.12135.
Wang, X., Xiao, T., Tan, J., Ouyang, D., & Shao, J. (2020). MRMRP: multi-source review-based model for rating prediction. In Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part II 25 (pp. 20-35). Springer International Publishing.
Xu, J., & Sen, S. (2021). Decision Intelligence for Nationwide Ventilator Allocation During the COVID-19 Pandemic. SN Computer Science, 2(6), 423.
Shangguan, Z., Zhao, Y., Fan, W., & Cao, Z. (2020, October). Dog image generation using deep convolutional generative adversarial networks. In 2020 5th international conference on universal village (UV) (pp. 1-6). IEEE.
Liao, J., Guha, T., & Sanchez, V. (2024). Self-Supervised Random Mask Attention Gan in Tackling Pose-Invariant Face Recognition. SSRN. Retrieved from https://ssrn.com/abstract=4583223
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Hengyi Zang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
