Manufacturing and industrial coatings assessment could be reshaped under a new collaboration announced by Sparc Technologies Limited, which has partnered with the Australian Institute for Machine Learning (AIML) at the University of Adelaide to develop AI-driven software aimed at modernising how protective coatings performance is evaluated.
In a media release, Sparc said the software is being designed to improve manufacturing-related coatings testing by using computer vision and machine learning to assess corrosion performance, replacing what it described as labour-intensive manual methods that have remained largely unchanged for more than two decades.
According to Sparc, the project focuses on protective coatings used across manufacturing and infrastructure, where corrosion testing is typically carried out under internationally recognised standards.
A pilot project has validated a proof-of-concept under ISO 12944 corrosion boundary testing, with the company indicating the approach could be expanded to other scribe-based and damage-based testing protocols.
Sparc Managing Director Nick O’Loughlin said the partnership builds on the company’s existing position in the coatings sector.
“This collaboration with the Australian Institute for Machine Learning at Adelaide University leverages complementary skills from both teams to develop AI-based software for the protective coatings industry,” he said, adding that the technology has the potential to “significantly improve and accelerate R&D under testing methods which have been unchanged for more than 25 years.”
AIML Director Professor Simon Lucey said the institute viewed the project as an opportunity to apply advanced machine learning techniques to a long-standing industrial challenge. “The replacement of outdated manual corrosion assessment with computer vision holds great potential to increase efficiencies and improve outcomes for the coatings industry,” he said.
Protective coatings play a key role in manufacturing and industrial supply chains by limiting corrosion in steel assets, which Sparc noted carries significant economic cost globally.
Current testing methods typically rely on subjective human judgement to measure corrosion spread, a process Sparc said can take an experienced technician around 40 minutes per assessment.
The company said its AI-driven approach aims to deliver higher accuracy and consistency, faster assessment times and more detailed data outputs, while acknowledging that the software remains under development.
Sparc estimates the addressable market at about 850 laboratories worldwide across manufacturing, testing and research organisations, and said it has received letters of support from multiple industry participants.
Sparc said its commercialisation strategy is expected to focus on industry co-development and software licensing to testing laboratories and coatings manufacturers, with the goal of beta testing in third-party labs within 12 months.
The content of this article is based on information supplied by Sparc Technologies Limited. For more information, please refer to the official company announcement and communications from Sparc. Please consult a licensed and/or registered professional in this area before making any decisions based on the content of this article.
