Predictive Analytics for Manufacturing Processes
The application of quantum computing to predict faults in the manufacturing process of advanced materials.
Project Budget* - $0.4M
Partner Co-investment - $0.2M
Supercluster Co-investment - $0.2M
The root cause of failures in product testing is often difficult to determine, particularly when the failure signals are sparse relative to the available background data. Compounding the problem, the process must meet a variety of specifications for multiple customers simultaneously.
The Predictive Analytics for Manufacturing Processes project aims to create a digital twin of the metal finishing line, leveraging predictive analytics to analyze data captured from the process line, such as chemical compositions, temperature and voltage. This technology will provide new insights to help optimize the manufacturing process.
The project is led by D-Wave in partnership with Avcorp Industries, Solid State AI and Simon Fraser University. The team is leveraging its research capabilities in data analytics, predictive analytics tools and advanced machine learning techniques on a quantum computer to address situations when failure signals are sparse, relative to the available background data. These tools will move Avcorp’s manufacturing fault detection processes from reactive to predictive.
This project will demonstrate predictive capabilities that can also be deployed in other manufacturing processes, including industrial manufacturing and semiconductor fabrication plants.
The estimated market for these types of products is about $5 billion over the next 10 years. Some studies suggest that predictive maintenance could enable the world’s manufacturing industry to save up to US $700 billion over the next two decades. If successful, this initiative will help position Canada as a rising star in the global predictive analytics field.