Monolith AI and Imperial to develop manufacturing solution

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.

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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.