The Royal Institution for the Advancement of Learning/McGill University

Published purpose

Surface engineering and thermal spray (TS) coating technologies are used to enhance base materials sustainability and to add functionality to products with applicability in a variety of industries and sectors e.g., automotive, aerospace, oil and gas, energy, and defense. Constantly changing customer needs and demands require TS coating manufacturers to be flexible enough to augment the output of TS processes in an increasingly competitive global market while delivering high-quality products at low cost. These targets can be reached by making use of existing digital technologies to capture and exploit data, leading to highly flexible and reconfigurable coating production processes, optimized equipment usage, predictive maintenance, on-line spray monitoring, and end product (i.e., coatings) quality control. Within the proposed Project it is intended to instrument a high power/enthalpy APS torch with video cameras and acoustic sensors to record continuously the process and further analyze through images and sound processing capabilities based on machine learning algorithms. These video and audio data-sets will be examined for feature selection engineering and extraction and possible trends which can indicate discernible patterns. Ultimately, if a pattern were to arise, machine learning could be leveraged to monitor and automate the spraying process.