CAE Automation Today, AI Tomorrow

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Synera GmbH

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Automation is already more than just an efficiency tool today—it saves weight, time, and emissions while opening up new design freedoms. But the next step is AI agents, which enable a new form of collaboration between humans and machines.

"China Speed"—hardly any other buzzword describes the dynamism with which Chinese manufacturers develop and bring vehicles to market. Development cycles that take months there often stretch over years in Europe. Time thus becomes a crucial resource for German automakers and suppliers. Especially in vehicle design, where new platforms, drivetrains, and lightweight construction concepts must be developed in parallel, it is evident: those who want to remain internationally competitive must consistently rethink development and manufacturing processes.

The same pattern is evident in many companies: engineering teams spend a significant portion of their working time on manual, repetitive tasks that are very time-consuming. To remain competitive, components and assemblies must be designed in weeks instead of months, and quotes must be prepared in days instead of weeks. At the same time, the pressure to become cost-efficient and save personnel is increasing.

Synera, an expert in process automation and AI agent platform for engineers, offers solutions to these challenges. The software connects more than 70 common CAD, CAE, and manufacturing systems, enabling seamless workflows.

Automated Workflows in Additive Manufacturing

BMW is already using the innovative solution in several development areas to harness the potential of bionic and topologically optimized structures for industrial 3D printing. The technology not only enables new design freedoms but also significant efficiency gains in manufacturing.

A particularly striking example is provided by the Landshut plant: there, an innovative gripper for the production of CFRP roofs was developed and manufactured using 3D printing in just 22 hours. The result is impressive across the board—the gripper is approximately 20 percent lighter than its conventional predecessor, and CO₂ emissions during production were reduced by about 60 percent.

The gripper is about 20 percent lighter than its conventional predecessor, and CO₂ emissions during manufacturing were reduced by approximately 60 percent.

At other locations such as Munich and Regensburg, BMW is also relying on the next generation of additively manufactured grippers. There, they are used, among other things, for handling the entire vehicle floor of the BMW i4. The combination of automated manufacturing processes and additive production enables a weight reduction of around 30 percent—while simultaneously improving the load structure and increasing functional efficiency.

Additive manufacturing has evolved from a prototyping tool to an industrial key technology in vehicle development.

Automated CAE Processes for Rear Axle Concepts

MAN Truck & Bus SE uses Synera to automate and significantly accelerate the development of rear axle concepts. In this project, developers were able to simultaneously examine and compare hundreds of axle concepts, including complex load cases and variations in passenger load.

The result: The entire development process was shortened by about 50 percent compared to previous methods. At the same time, the weight of the component was reduced by 36 percent compared to its predecessor, without compromising load capacity or stability.

Even in heavy-duty applications, such as commercial vehicles with high loads and safety requirements, automated CAE workflows today achieve more than just time savings—they deliver tangible physical efficiency gains (weight, material, energy) and thus offer great potential for sustainability and cost-effectiveness.

AI Agents in Vehicle Design: the Start of A New Era

The advantage of these possibilities in product development lies not only in speed. Variants that were previously not considered due to the high simulation effort can now be examined in large numbers. This leads to more robust concepts, better component quality, and a clear decision-making basis for series development.

But the real revolution is yet to come. Additive manufacturing presents a particular challenge: design and manufacturing knowledge must be closely interconnected. Orientation, support structures, and post-processing significantly affect the material properties of a component. This often leads to complex rework cycles and extends development time.

AI agents combine the cognitive intelligence of modern language models with direct access to CAD, CAE, and ERP systems. This enables them to not only prepare tasks but also execute them independently.

AI agents could resolve this bottleneck. They combine the cognitive intelligence of modern language models with direct access to CAD, CAE, and ERP systems. This enables them not only to prepare tasks but also to execute them independently: optimizing geometries, simulating mechanical load parameters, or designing components for 3D printing. Multiple agents can team up to form a digital engineering team that works autonomously around the clock like a virtual colleague.

Particularly in the quotation process, the RFQ, this opens up enormous potential. What today requires weeks of coordination between design, simulation, procurement, and costing could be accomplished in just a few days with agent-based workflows. Time that often makes the difference between winning or losing a contract in international competition.

Conclusion: From Automating to Orchestrating

The examples from the automotive industry show that automation is already more than just an efficiency tool—it saves weight, time, and emissions while opening up new design freedoms. However, the industry must not settle for this. The next step is AI agents that not only accelerate individual processes but also orchestrate entire development cycles and enable a new form of collaboration between humans and machines.

AI agents not only accelerate individual processes but also orchestrate entire development cycles.

For the German automotive industry, this is not a distant vision but an immediate necessity. Those who consistently implement automation and test AI agents early in pilot projects can halve development times, achieve sustainability goals faster, and secure their competitiveness in the race against "China Speed." The decisive question is not whether these technologies will come, but who will successfully establish them in vehicle design first.

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