AI model development for predicting powder coat thickness distributions

Surface coatings are an important part of many industries (e.g., aerospace, automotive) owing to their practical benefits. In particular, powder coating is now quite common given its ease of use, durability and eco-friendliness. Controlling the uniformity of the coating is highly desirable to improve the efficiency of the technique and to progress toward its automation. Powder coating presents a complex multiphysics problem. It is challenging to obtain a uniform coating thickness by optimizing the process parameters through simulation. This project aims to develop an AI-driven model to predict coating thickness distribution arising from arbitrary powder coating parameters.
NeurobotIA and Cadence want to use the AI model to develop and commercialize an automated powder coating technology as well as use the architecture to develop new applications (e.g., AI-assisted soldering). The vision of NeurobotIA is to continue to address labour shortages through technological solutions in collaboration with academic institutions, which will strengthen both industry and universities in Canada.

Faculty Supervisor:

Lucas Hof;Giuseppe Di Labbio

Student:

Partner:

Cadence Automation Inc;Technologies NeurobotIA

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Wholesale trade

University:

École de technologie supérieure

Program: