Photogrammetry for Digital Twinning Industry 4.0 (I4) Systems

TL;DR

This paper demonstrates that consumer-grade devices like the iPhone 15 Pro can effectively create accurate digital twins of Industry 4.0 systems through photogrammetry, enabling cost-efficient smart manufacturing solutions.

Contribution

It introduces a novel approach using consumer devices for photogrammetry to develop digital twins in Industry 4.0, with validation of accuracy and cost efficiency.

Findings

Mean error of 4.97% between ground truth and 3D models

Standard deviation error of 5.54%

Cost-effective method suitable for iterative improvements

Abstract

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.