Digital twin for early-stage fault detection in a hybrid manufacturing cell

As technology is evolving, newer and more advanced tools allow for the construction of more productive yet complex systems. Modern customers seek personalised products, creating a demand for highly flexible production. In an attempt for consumer products to become more competitive, it is necessary to reduce costs. This means that fast and accurate decision-making is needed to avoid the production of defective products. This work presents a Digital Twin of a hybrid manufacturing cell which utilises near real-time data and Artificial Intelligence to speed up the decision-making process of whether a product is defective before its production is complete. A real use case will be analysed to determine the feasibility of automating adaptive control in this manner.