Enhancing the Accuracy of Digital Twin Simulation with Machine Learning and Predictive Analytics

Manufacturing companies often face challenges like unexpected equipment breakdowns, shifting production needs, and resource shortages or surpluses, which can lead to delays in production schedules. Traditional systems struggle to manage these issues effectively. Our project introduces a better solution using Digital Twin (DT) technology, which creates a real-time digital replica of a factorys operations. This digital twin helps predict and adapt to changes quickly. With advanced artificial intelligence, we can foresee problems, such as potential machine failures, and adjust plans in advance. This approach will enable our partner, Longterm Technology Services, to provide a more efficient digital solution, enhancing productivity and ensuring smooth operations for its clients.

Faculty Supervisor:

Bissan Ghaddar

Student:

Partner:

Longterm Technology Services

Discipline:

Engineering

Sector:

Advanced Manufacturing

University:

The University of Western Ontario

Program: