BWI Group develops AI tool cutting suspension development time from weeks to one hour

- In-house deep-learning tool developed at BWI Group replaces weeks of iterative FEA simulation with a one-hour optimisation process, first validated on an air spring development project

- The tool uncovered viable suspension designs that experienced engineers had considered unachievable, demonstrating AI’s potential to expand the boundaries of chassis development

- Validated against physical test data with 99% correlation, the approach is already being extended to other suspension architectures as the chassis industry accelerates AI adoption

19th October 2026. BWI Group, a leader in advanced suspension and braking systems, has disclosed details of a bespoke artificial intelligence (AI) tool that reduces suspension development time from weeks to approximately one hour. Developed in-house at the company’s Technical Centre Kraków (TCK), the deep-learning system has been validated on an air spring development project and is now being extended to other suspension architectures.

Suspension development is among the most parameter-intensive challenges in chassis engineering. For air springs, the force-displacement performance characteristics demanded by an OEM are governed by the complex, non-linear interaction of geometry, internal pressure, material behaviour and reinforcement architecture. Matching a target performance curve typically requires repeated design iterations supported by finite element analysis (FEA), with each loop demanding new simulations, interpretation of results and informed design changes. Even for experienced teams, convergence routinely takes several weeks.

The methodology developed at TCK replaces this iterative process with a five-layer deep neural network trained on 180 high-quality FEA datasets. These datasets were generated using simulation models validated against extensive laboratory testing, meaning the AI learns from physically verified engineering data rather than theoretical approximations. The neural network acts as a fast-evaluating surrogate for conventional simulation, mapping the relationship between key design parameters and resulting suspension performance.

This deep learning-based surrogate model is able to accurately predict the system's behaviour, achieving an R² value of 99% compared to the FEA results,” said Miroslaw Siemieniuk, FEA Manager at BWI Group. “Every dataset the AI learns from has been generated by validated simulation models that correlate with physical test results. Without that foundation, the outputs would be meaningless. What the AI gives us, once learned, is speed. The ability to explore a design space in minutes that would otherwise take weeks.”

In the initial air spring project, the model optimised five critical parameters: piston radius, low support radius, sleeve thickness, design pressure and nylon fibre cord angles. A Newton-based optimisation algorithm searched for the combination that minimised the error between the predicted force-displacement curve and the OEM’s target. The optimisation process effectively converges, reaching a residual error of approximately 2% relative to the target curve.

Beyond the reach of manual methods

Perhaps the most significant outcome emerged when the AI explored areas of the design space that engineers had previously discounted. During manual development, the team had concluded that the OEM’s target performance curve could not be achieved without a specific structural constraint component. The AI tool, unconstrained by prior assumptions and able to evaluate the full parameter space rapidly, indicated that the target could in fact be met without it.

The finding represents a technical proof of feasibility rather than a validated production design. Nonetheless, the implications for OEMs could be considerable. Eliminating the component would reduce mass and material usage, simplify assembly and remove the associated tooling.

“This is not about replacing engineers,” continued Siemieniuk. “The quality of the training data, the assumptions in the simulation models, the selection of physically meaningful parameters, all of that requires deep engineering knowledge. What AI does is shift the engineer’s focus away from repetitive calculation and towards system-level understanding, judgement and innovation.”

A platform for broader suspension development

The approach reflects a broader shift taking hold across the automotive industry. As OEM development timelines compress and vehicle architectures grow heavier and more complex, chassis suppliers are under increasing pressure to deliver precise performance targets faster. AI-augmented engineering tools offer a route to meeting that demand, provided they are built on rigorous physical foundations.

The AI framework developed at TCK has been designed with scalability in mind. While the initial project assessed five critical parameters, this is being expanded to cover aspects such as sleeve height, piston geometry and air spring volume. BWI Group is already working to extend the same methodology to other suspension architectures, including passive valve systems.

“This first project is a clear proof of concept, but the ambition goes well beyond a single component,” added Siemieniuk. “We are building a platform that will allow us to bring AI-driven optimisation to multiple areas of chassis development. The engineering fundamentals come first and the AI amplifies what our teams can achieve with them.”

About BWI Group

BWI Group is a tier-1 full-service supplier of intelligent chassis systems, designing and manufacturing brake and suspension systems for the global transportation market.

With a heritage reaching back over 100 years, our 9 manufacturing plants, 7 technical centres, people and intellectual property enable us to serve more than 50 passenger vehicle brands globally, including Audi, BMW, BYD, Cadillac, Chevrolet, Citroen, Ferrari, Ford, Jaguar, Lamborghini, Land Rover, Mercedes-Benz, Nio, Peugeot, Porsche, Volkswagen, Volvo.

BWI Group bring to market and expertly integrates a broad product range. This includes basic technology such as twin-tube suspension dampers, to innovative and impactful solutions, such as MagneRide® active damping, AeroRide™ air suspension, and Semi-Active Roll Control (SARC). The company also provides  Electro-Mechanic Brake systems, DBC1280 ESC, iDBC1 One-Box, and iDBC2 Two-Box brake systems.