FEATURE Automotive Supply Chain
AI for the automotive industry
L
ondon-based mobility software company Monolith is rapidly scaling its artifi cial intelligence (AI) platform, which promises
to substantially reduce development and testing costs for new vehicles. Monolith software uses self-learning models to instantly predict the results of complex vehicle dynamics systems, reducing the number of physical tests and simulations. The company claims this approach will dramatically accelerate every stage of the automotive development process – from initial design and its iterations, to validation and production, which currently require repetitive, time- intensive and costly tests and simulations. With Monolith’s platform, there will be fewer physical prototypes and on-road testing, making the later stages of product validation safer and more sustainable.
The gaps in testing So far, automotive companies have used a combination of life-like virtual simulations and physical testing during vehicle development. For each design iteration, a simulation solves the physics that underpins the system’s modelling – a notoriously diffi cult and computationally- intensive process. Virtual simulations reduce the number of physical tests required, but the accuracy and fi delity of the results can be limited. Numerous physical tests are therefore still required, to calibrate and validate the virtual results and understand performance in operating conditions that can’t be simulated. For example, the study of aerodynamics optimises air fl ow over a vehicle to reduce drag and is rather diffi cult to solve mathematically, which reduces the accuracy of the simulated models. Owing to the highly- iterative nature of the automotive design process, engineers supplement virtual aerodynamics testing with hundreds of hours of wind-tunnel tests, in facilities that can cost thousands per hour.
Instead of solving the complex physics 14 May 2022 | Automation
of the system or performing a physical test, Monolith’s solution is based on the data emerging from virtual and physical tests, then used to train highly-accurate AI self- learning models, to instantly predict the performance of systems by understanding their behaviour.
“Optimising a system, or fi nding a new solution based on a decade of historical data, is like instantly off ering an engineer a decade of experience. That’s the power of AI – it supercharges an individual’s subject-matter expertise by unlocking the expertise stored within a company’s data,” said Dr Joel Henry, Monolith Principal Engineer.
Ready to scale
Monolith has spent the past six years developing its platform and working closely with some of the world’s top engineering teams to stress test it. Today it boasts a mature and proven technology that is being seamlessly integrated into customers’ day-to-day activities. Engineering teams at leading automotive OEM and Tier-1 suppliers are already realising substantial reductions in physical testing after working with Monolith: • Sensors and instruments company Kistler achieved a 72% reduction in sensor- based testing; • Honda recorded an 83% faster design
cycle; • JOTA Sports Endurance Racing Team
reduced the number of simulations and tests by 50% and associated costs by 66%. Built from the ground up, the no-code platform off ers a seamless user experience with powerful interactive dashboards. The Monolith team is made up of industry and software experts who work with customers to identify their most eff ective use cases that can rapidly realise the value of AI. “Monolith was founded to empower engineers with AI to instantly solve even their most intractable physics problems. We know this resonates, especially with automotive engineers who struggle to optimise hundreds of often confl icting
criteria with hundreds of complex simulations. Requiring hours or days to solve, engineers have grown frustrated by the considerable amount of physical testing still required to make up for the limitations of virtual tests. At the same time, the data that is created in the process represents an enormous opportunity when used with AI,” said Dr Richard Ahlfi eld, Monolith’s CEO and Founder. He continued: “Today, automotive companies are spending billions developing electrical architectures and software capabilities as they strive to win the race for electric, shared and autonomous mobility. This squeezes R&D budgets and product timelines in other areas, creating enormous pressure on the engineering teams working to develop higher-quality vehicle hardware systems in less time and with fewer resources. As Akio Toyoda, CEO of Toyota put it, “data is the new gold”, but the vehicle platform will be the backbone for mobility as a service for autonomy, for car sharing, for any number of services that we want to make possible.”
$46bn opportunity Monolith is currently focusing on automotive customers but wants to work with many industries. Its solutions can be used for any system that requires data, repetitive testing or digital-twin development, validation, production or evaluation. Digital twins, which are real- time virtual representations of a physical object or setup, are increasingly used in a wide range of industries including manufacturing, healthcare, supply chain and retail. The digital twin market is estimated to be worth over $46bn by 2026. Monolith is already working in this space with brands such as L’Oreal and pharmaceutical company Nanopharm.
CONTACT:
Monolith
www.monolithai.com
automationmagazine.co.uk
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