With a new administration assuming control of the federal government in January 2025, there is much uncertainty for U.S. automakers.
What will become of aggressive automotive emissions-reduction policies? Are the Inflation Reduction Act’s consumer tax credits for electrical vehicle (EV) purchases soon to be eliminated? How will tariffs on foreign-made products reshape the marketplace? Will the UAW’s stronghold on automotive labor policies erode?
These are just a few of the challenges, and potential opportunities, that face the U.S. auto industry as it balances long product development and lifecycle timelines alongside political influences that seem to oscillate every four years.
One thing that is certain, however, is that we are entering a critical period in the transition from internal combustion engine (ICE) vehicle to EVs. OEMs and Tier Suppliers have made massive investments to develop essential energy storage resources, supply chain relationships and production capabilities. Though the market has not taken off quite as rapidly as many expected, it is a matter of ‘when’, not ‘if,’ EVs become the standard mode of over-the-road personal and commercial transportation.
EVs: A new Class of Vehicle Manufacturing
In addition to designing and engineering an entirely new class of vehicle, manufacturers have also had to figure out a way of building them, and to do so in a way that is cost-effective, sustainable, and achieves optimum quality levels.
“EV plants have radically different layouts and production methods. ICEs may have traditionally been built elsewhere and shipped to an assembly plant as a completed unit,” said Patrick Matthews, global group manager of ABB Robotics’ EV assembly business. “But with EVs the powertrain is often integrated far earlier in the production process. Battery packs are increasingly assembled on-site from their individual modules in a parallel process, with the completed units – now designed as a structural element in many new EVs – joining the body at a much earlier stage.”
To compound matters, EV powertrain systems are still evolving rapidly, with changes that affect, to varying degrees, how a production facility is designed. Solving this dilemma demands unprecedented flexibility. As with ICE vehicles, the flexibility of robotic automation will play a key role in the manufacturing of EVs of all types.
Robotic Automation helps OEMs adapt Production to Market Demand
With robotic automation, OEMs can start small and ramp up production as demand increases. Key to this approach has been the adoption of AGVs (automated guided vehicles) and AMRs (autonomous mobile robots). These robotic platforms transport parts throughout the production facility, perhaps from logistics to the point of need on the line. While AGVs tend to follow pre-defined routes, AMRs are capable of intelligently navigating within their space, avoiding obstacles and even optimizing their journey.
“AMRs and AGVs help auto manufacturers unravel traditional long-line production architectures and deploy dedicated modular cells, providing the ability to modify or even replace individual cells without incurring costly production interruptions,” says Joerg Reger, managing director of ABB Robotics’ automotive business. “These zero-loss production changes allow OEMs to start small and scale up key parts of the assembly process by adding or redeploying cells as demands change. By engineering flexibility into the process, we create the means to not just manage this rising complexity, but to turn it into an opportunity.”
Gone is the traditional two-week shutdown to allow lines to be reconfigured with each model year; these changes can now be made almost on the fly, and without the need for the huge capital expenditure that comes from wholesale layout changes.
Challenges Specific to EV Manufacturing
EVs may use fewer parts overall, but those parts tend to be heavier than their ICE vehicle equivalents. Gas tanks have been replaced by batteries, while electric motors with rotors and stators are replacing engine blocks, pistons and cylinder heads. Complex transmissions are being replaced by much simpler gearboxes. This is changing well-known and well-defined manufacturing processes and driving both suppliers and OEMs to re-think their automation solutions. Robots and AGVs working together will be a key driver in this transformation, with many typical use cases calling for high payload and extended reach, a challenging process for the typical automotive six-axis robot.
As well as weight and reach, the industry demands speed. Thanks to advances in control software, robots can operate at higher speeds without sacrificing accuracy. Even when manipulating heavy payloads at maximum extension, fine control can be preserved, and this has allowed the new generation of more powerful six-axis robots to replace long and winding conveyor routes to transport vehicle bodies within a far more compact footprint than before, saving space, time, and creating flexibility.
Improvements in vision systems over recent years have opened the door to a range of exciting new developments that are already helping to drive even greater levels of efficiency in manufacturing. Although modern car production appears as a continuously moving stream, in reality at key points in the cycle the vehicle must stop to receive a key component or undergo a critical process, such as the installation of the pre-assembled powertrain.
Vision- and Force-Control Enabled Robots
With vision- and force control-enabled robots interacting with AGVs, it’s now possible to install components such as underbody battery packs while the vehicle is still in motion, with the moving platform matching its speed to that of the suspended body while the on-board cameras detect and synchronize with critical mounting points.
This moving-line assembly isn’t just faster; with the requirement to stop removed, floor-mounted shock pins are no longer needed. That increases flexibility that can ripple throughout the factory, and makes the assembly line less model specific, making it possible to use the same line or production cell for a variety of different models. That, in turn, leads to a smaller overall factory footprint, as well as a significantly reduced capital expenditure.
Vision systems are improving the speed and quality of welding operations, too, and when coupled with advanced artificial intelligence and machine learning systems, their ability to spot minute defects can now match or even surpass that of the most experienced human operative. Using models trained on millions of examples, these automated systems can automatically assess the quality of a weld in a fraction of a second just by analyzing a few rapid-fire photographs.
Of course, this approach generates and relies upon vast quantities of data. From tracking information for each individual component to the torque delivered to every bolt and screw, the challenge once all this data has been gathered is to figure out how best to use it. One possible solution is to create a machine-based equivalent of the experienced and reliable human that customers call on whenever a problem arises. By understanding the steps the human follows when presented with a particular situation, a machine can be made to replicate that diagnostic process, supported by the reams of collected data. As a result, production cells can address problems automatically, while optimizations can be made that cut installation times and improve cycle times.
That learning process is even being applied to the humble fastener. With force control, a robot can ‘feel’ their way around a mounting point, making minute adjustments to its position and sensing when the part begins to locate, adapting the force applied all the while, much as a human would.
Summary
Robotic automation in EV manufacturing is not just a matter of improving efficiency, reducing costs, and ensuring product quality, it is also a valuable means to innovate and adapt to new market demands with unmatched flexibility. From battery production to final assembly, robots will continue to play a central role in scaling up EV production, and, as technology advances, more sophisticated systems will emerge, further integrating AI, vision and data analysis into a highly advanced manufacturing algorithm.
