CAE (Computer-Aided Engineering) simulations have improved how manufacturability of industrial products, such as car doors, is assessed, but the final decision is still limited to the assessment of a small number of domain experts who need to assess simulation results.
For example, it is known from medical image processing used to diagnose lung cancer that machine learning models can provide a highly accurate assessment for new patients when trained with enough data. However, knowing the final prediction is not enough — doctors need to understand why and where the AI algorithm detected cancer. The same is true for engineering applications.
The goal of the project, led by Monolith AI’s Dr Joël Henry, is to build a new version of explainable AI that will provide clear feedback to engineers on how it arrived at its conclusions, removing the ‘black box’ dilemma.
Monolith AI and Imperial College London want to streamline the manufacturing process and provide a new competitive advantage to high volume manufacturers by using AI to learn from what could be manufactured in the past and predict what is best for new components. This would enable engineers to build expert simulations based on repetitive tasks and historic data.
The platform will be developed over the course of 18 months, and will be evaluated by multiple industry partners. If successful, researchers believe it could ‘revolutionise’ CAE within the manufacturing industry and allow engineers to run complex manufacturability assessments in seconds compared to weeks. This could then free up engineering skills for the most complex, value-added tasks.
“CAE has done a fantastic job advancing component manufacturing, but there are still many areas where physical simulations still cannot capture the true complexity of components,” said Dr Richard Ahlfeld, CEO and founder of Monolith AI.
“Large engineering companies collect a lot of data when assessing manufacturability and our goal is to make that data work to their advantage. This latest funding will allow us to explore this possibility and drive not only the automotive industry, but other sectors forward.”