By Pär-Ola Jansell, Altair’s Vice President and Global Technical Lead for Heavy Equipment, Trucks and Rail
For most of its history, the heavy equipment industry has relied on physical prototyping. In many cases, physical prototyping is a regulatory necessity.
After all, these are big, heavy machines that undergo immense stress and wear, and they must be safe for operators and bystanders alike. However, when simulation technology — namely finite element analysis (FEA) — emerged and grew in the 1990s, it ushered in a new design and testing paradigm, one where virtual prototyping played a larger role.
The industry is witnessing another paradigm shift today, yet this time, virtual prototyping is moving from a useful (but secondary) verification tool to a core capability and driving factor in design, testing and certification. It is worth exploring the past and present of virtual prototyping to understand how it has revolutionized heavy equipment design, and to look at where the technology may take the industry in the future.
Virtual prototyping emerges
Before the first virtual prototyping tools hit the market in the early 1990s, heavy equipment design was fairly predictable. Many designs had not changed significantly in years or even decades. Since designs had to all be physically tested, development could be slow, tedious and costly, which discouraged risk-taking.
Everyone wanted to make better machines at lower costs. But making machines stronger or more durable usually meant adding more weight in high-stress areas. FEA changed that. In the 90s, some trailblazing companies debuted machines that were stronger and more durable than ever, while also being lighter and more agile.
Now, thanks to digital tools, manufacturers could solve problems by doing more than just adding metal to troublesome areas — they could re-evaluate the entire design process. Companies that had traditionally only dabbled with FEA in validation and testing knew they would need to adopt it throughout the lifecycle, including in optimization and certification. Everyone knew quick and efficient adopters would have a concrete commercial advantage.
Adding more tools to the mix
Fast forward a few years and heavy equipment design looked much different than it did the decades prior. After the turn of the century, companies got better with virtual prototyping tools and embedded new capabilities like data analytics and bulk material simulation into their repertoire. Development was faster, costs were lower and innovation was on the rise.
For instance, the addition of bulk material simulation and data offered companies a simple, yet powerful proposition: the ability to optimize performance, lower costs and minimize the risks, time and headache associated with physical testing.
Other more sophisticated tools allowed designers to refine CAD designs before validation, reduce total CAD iterations, precisely simulate and analyze material flow, improve vehicles’ noise, vibration and harshness (NVH) properties and beyond.
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As compute technology matured and scaled, it improved the accuracy and power of bulk material simulations, giving teams the ability to simulate coal, mined ores, soils, fibres and grains with precision.
In all, by the end of the 2010s, the heavy equipment design landscape was a completely different place than the industry of past generations. Though physical testing was (and is) still a key feature, the design, optimization, validation and certification lifecycle was now a firmly simulation-driven affair.
As we approach 2030, the industry is poised to once again witness another revolution in virtual prototyping and design, but this time it’s driven by technologies like artificial intelligence (AI), digital twins and AI agents.
AI and beyond
The convergence of AI, data analytics and simulation — together with more efficient, more powerful high-performance computing (HPC) and cloud resources — will once again reshape the heavy equipment industry. Not just buzzwords or concepts for the future, modern tools and platforms are already driving this digital transformation.
For instance, once trained, AI-powered geometric deep learning tools can deliver predictions up to 1,000 times faster than traditional solver simulations, slashing simulation runtimes from hours to seconds and what-if studies from months to days. Moreover, these tools can learn from a company’s historical data – years and decades’ worth – without the limits of parametric studies.
Other AI capabilities are also poised to make their mark. Today’s data analytics platforms can seamlessly weave in the best functionalities of AI operationalization tools to create natural language-accessible knowledge graphs that span an organization’s entire data estate. These knowledge graphs allow users of all skill levels and specialties to map, visualize and analyze relationships within previously disparate data.
Knowledge graphs also enable effective, diverse AI agents. These agents can optimize workflows, improve customer experience and enhance productivity, scalability and decision-making in supply chain operations, fleet monitoring and beyond.
Moreover, digital twin platforms can combine AI-powered simulation and Internet of Things (IoT) capabilities to maximize equipment performance and slash costs. Thanks to reduced order models (ROMs), these digital twins deliver real-time predictions and insights that help companies minimize downtime and warranty expenses.
These capabilities are just the start. The pace of innovation in data, AI and digital twins has been staggering and will only continue to increase. As more organizations and their users become proficient in these tools’ use and deployment, their impact will compound.
The path to a more efficient industry
Though the adoption and growth of new technology always presents new challenges — for individuals, organizations and the industry at large — history has demonstrated that virtual prototyping has ushered in a better, faster, more efficient heavy equipment landscape. Teams can solve more complex problems in interesting ways; they can reduce waste, risks and costs; they can make, simulate and optimize more interesting designs; and they can experiment with a freedom that yesteryear’s physical prototyping-driven landscape simply did not allow.
And best of all, today’s virtual prototyping tools help teams see the big picture in one go. Rather than simulating mere components and systems, teams can now simulate entire systems, analyzing electronic, mechanical and thermal performance all in a single workflow and technology environment.
Virtual prototyping will continue to foster the creation of stronger, lighter, more affordable and more sustainable heavy equipment. The industry has come a long way in just a few decades – it is exciting to envision how much more progress will be made in the decades to come.
