Explainable AI system to rationalise accelerated decision making on automotive component performance and manufacturability

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Computer-aided-engineering (CAE) has dramatically changed how industrial products, components and systems are produced, reducing development costs, risk, and time to market, whilst improving product quality and design accuracy. R&D into auto-components manufactured by metal stamping and die casting, the most common manufacturing methods in the automotive industry, represents a $6.5Bn global industry. However, scenario analysis simulating plastic strain, thickness, distribution of hardness and other key properties on component manufacturability requires millions of simulations and much skilled engineering labour. With the transition to zero emission vehicles fast gathering pace, the UK still suffers from an annual shortfall of 37-59K level 3+ engineers (UK-State-of-Engineering-2019).AI can revolutionise CAE. With it, industry can run complex design scenarios many thousands of times faster, freeing up precious engineering skills for the most complex, value-added tasks. Monolith's proprietary AI engine, developed with input from several leading automotive industry leaders, lets engineers build expert simulations based on repetitive tasks and historic data. However, like other AI, it is hampered by the 'black box' dilemma, whereby engineers lack uninhibited freedom to respond to AI outputs because they cannot rationalise/control underlying assumptions. Inability to explain AI's underlying assumptions and factor in manufacturability of new component designs particularly hinder mission-critical go/no-go decisions on component feasibility made under stringent economic and/or regulatory constraints.The project builds on cutting-edge academic theory to craft the world's first AI-explainability tool for complex 3D designs with manufacturability taken into account. Working with world-class manufacturability experts at Imperial College London (ICL), Monolith will develop a system that rapidly predicts component manufacturability as well as performance, and that provides clear feedback to engineers on how the AI arrived at its conclusions. The platform will be evaluated with an industry-leading manufacturer in the project. The technology has potential to generate £19.9M cumulative profits (70% exports) by 2027\. In a heavily regulated and scrutinised industry, it will help realise AI's full potential and greatly accelerate industrial product development. There is strong translational potential in multiple sectors including aerospace and energy.