Simulation pioneer leverages decades of expertise and generative AI to slash vehicle weight, accelerate development, and drive efficiency in the EV era.
12 Jun 2025 | 1018 Views | By Shahkar Abidi & Ketan Thakkar
In the rapidly evolving automotive landscape, where sustainability and efficiency are paramount, companies are seeking innovative solutions to shed vehicle weight. Vishwanath Rao, MD of Altair India, a global leader in simulation, high-performance computing (HPC), and artificial intelligence (AI), recently highlighted his company's future vision for light-weighting and design innovation ...
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In the rapidly evolving automotive landscape, where sustainability and efficiency are paramount, companies are seeking innovative solutions to shed vehicle weight. Vishwanath Rao, MD of Altair India, a global leader in simulation, high-performance computing (HPC), and artificial intelligence (AI), recently highlighted his company's future vision for light-weighting and design innovation during a panel discussion organized by Autocar Professional. Altair, a 40-year-old company recently acquired by Siemens and now part of Siemens' digital industry software, boasts a vast global customer base of approximately 15,000, with over 1,500 in India, a third of whom are in the auto industry.
Altair's journey in lightweighting began in the aerospace industry where every gram saved significantly impacts efficiency and load capacity. Rao cited their extensive involvement in the Airbus A380 program, where an optimization center with Airbus in Europe focused on reducing weight across the aircraft. "Just on the aircraft wing rib itself we were able to reduce almost about 500 kilos," he stated. The principles developed for aerospace were swiftly adopted by the automotive sector, where saving even 100 grams on a single part, multiplied by millions of parts, translates to substantial cost savings. While initial drivers for automotive lightweighting were cost, efficiency, and performance, the focus has more recently shifted to sustainability, net zero, and environmental impact.
The Dual Approach to Weight Reduction:
Rao outlined two primary strategies for achieving significant weight reduction in vehicles. The first being the material replacement: This involves substituting traditional materials with newer, lighter, stronger, and more durable alternatives. Examples include high steel alloys, aluminum, magnesium, titanium, carbon-reinforced fiber, or glass-reinforced fiber. Rao considers this the "more easier way of addressing the weight problem". The second way is by making optimal material placement: This advanced approach focuses on ensuring material is used only where absolutely necessary. Rao emphasized that "Having more material does not mean you have a better design". The objective is to use the minimum amount of material while delivering superior performance in terms of structural integrity, life, durability, noise reduction, and occupant safety.
Pioneering Generative Design with OptiStruct:
Altair has been at the forefront of generative design for 25 years, a methodology Rao suggested could also be called "generative AI design" today. This technology, specifically named OptiStruct, is widely used by automotive companies globally. The process involves several key steps. The first one being identifying the available area for the component or system design. The next step is distinguishing between fixed points (e.g., where components connect) and areas where design freedom exists. The third step is specifying the forces and environmental factors acting on the component. Lastly, the step involves defining constraints and objectives, which in other words means setting goals like minimum weight while adhering to limits on stress, frequency, or part's life.
The software then autonomously generates a conceptual design by tracing the load path, retaining material only where needed, and effectively removing material from unnecessary areas. This is described as a "material removal technique". Crucially, manufacturing constraints for processes like casting, forging, extrusion, plastic molding, or 3D printing can be imposed, allowing the software to recommend a manufacturable design from the outset.
Driving Sustainability and Electrification through Design:
As per the top executive, material recyclability depends on the chosen material and Altair significantly contributes to sustainability by facilitating the creation of the lightest possible designs. This reduces material consumption, enhances efficiencies, decreases fuel consumption, and lowers emissions.
Electrification has opened new avenues for design innovation. Altair developed a unique three-stage process, dubbed 1-2-3-C (Concept 1, 2, and 3), to create entirely new architectures for Electric Vehicle (EV) Body-in-White. Unlike traditional Internal Combustion Engine (ICE) vehicle bodies, EV bodies have different load distributions and packaging requirements. This technology enables the development of the among the lightest in the world EV architectures, which are also modular and adaptable for various vehicle variants like sedans, SUVs, and hatchbacks. Major automakers, including Mercedes, Ferrari, Porsche, JLR, have already deployed this technique.
The Fourth Paradigm: Infusing AI for Data-Driven Design:
Vishwanath Rao emphasized Altair's ongoing infusion of AI capabilities into their software, signaling a shift to the fourth paradigm of design – one that is data-driven. For instance, companies these days possess decades of simulation data, which Altair is now utilizing to build machine learning models for predictions. This promises to "accelerate design decision making by a factor of thousand", allowing tasks that once took days to be completed in "minutes to seconds".
Furthermore, with vehicles increasingly equipped with hundreds of sensors transmitting data to the cloud, AI models can derive insights into vehicle performance, operating conditions, driver behavior, and facilitate the creation of new variants and improved designs. It also aids in predicting critical failures and understanding battery health. Moreover, OEMs' historical testing data, typically used for final agency sign-offs, is also being leveraged to build AI models. These models can predict the likelihood of failure, when a part might fail, and its lifespan after design or material changes, potentially reducing the need for new physical tests.
Rao underlined the broad applicability of AI, stating that its use cases extend beyond design and development in the auto industry, finding relevance wherever data exists, such as in sales, marketing, production, manufacturing, supply chain, and finance.