  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.
