Photogrammetry for Digital Twinning Industry 40 (I4) Systems
<|reference_start|>Photogrammetry for Digital Twinning Industry 40 (I4) Systems: The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increase productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, representing the physical process in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the 'Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97\% and the overall standard deviation error is 5.54\% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time.<|reference_end|>
@article{alhamadah2024photogrammetry,
    title={Photogrammetry for Digital Twinning Industry 4.0 (I4) Systems},
    author={Ahmed Alhamadah, Muntasir Mamun, Henry Harms, Mathew Redondo, Yu-Zheng
  Lin, Jesus Pacheco, Soheil Salehi, and Pratik Satam},
    journal={arXiv preprint arXiv:2407.18951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18951},
    primaryClass={cs.CV eess.IV}
}