The benefits of AI, ML and simulation outweigh the investment costs

Vishwanath Rao, MD, Altair India

Vishwanath Rao, MD, Altair India, has been associated with the organization since October 1999. He joined the company as Sales Engineer for the West Indies region. Rao rose through the ranks to become a Director at Altair India in January 2015. Rao has been Managing Director and Country Director for Altair India and the Gulf Cooperation Council countries since January 2019.

What does this MoU with IIT Delhi Incubator Park mean for Altair?

We have worked with various incubation centers across the country. Altair has already signed MoUs with several incubation centers in Bangalore, Hyderabad and Chennai. We are also starting to see many tech startups emerging in the NCR region.

It is clear that many cutting-edge technological innovations occur in startups. So we’re not looking at revenue; we examine how we can contribute to the startup ecosystem by creating disruptive innovations. And if these innovations use our technology, it adds a lot of value for us by introducing it to the rest of the world.

You focus on automotive engineering, among other verticals. What innovation do you see in the automotive world?

The definition of a vehicle has changed dramatically from what was once a traditional mechanical product. Most vehicles today are a combination of electronic devices and software on wheels. This is where a lot of cutting-edge innovation is going to happen.

Traditional mechanics have reached a certain level of maturity. Innovation occurs in newer areas such as autonomous driving, parallel verification and connector technology. The use of data, the use of machine learning, the use of AI are aspects that will increase.

What are the three most critical points for automotive suppliers from a technical point of view?

Lightness is extremely important because making a vehicle light without compromising performance will help achieve higher range. If we can reduce the weight and increase the range, we automatically respond to the anxiety problem.

Second, the incorporation of increasingly aerodynamic elements into automotive designs. The same will make the wind resistance minimal and again help to increase the range. Look at any survey and we can see that range anxiety is the biggest barrier to EV adoption. Light weight and aerodynamics will also help petrol and diesel vehicles travel more miles.

Safety should be the priority on the list. OEMs and battery manufacturers should invest more to ensure the safety of batteries in all situations, whether it is a short circuit, a temperature increase or an accident. They also need to focus on how to better use the data they collect and create better products.

What’s next for automotive OEMs in AI, ML and simulation?

Simulation, historically, was more of a forensic tool. It was used to figure out what caused a failure. It then became a verification and validation tool. However, simulation has now become a design tool. OEMs are now infusing simulation to begin with a mathematically correct design. It helps OEMs reduce iterations between design and simulation.

AI and ML have many applications. These can be applied from the design phase to the after-sales service phase. AI and ML can help drive designs based on data and simulation.

The other use case for these three technologies together is that of digital twins. This technology can help OEMs and component manufacturers design and manufacture better products without having to test them in the real world. For example, data collected from the sensors of a car that has been on the road for five years can be used to create a digital twin of the same.

Simulations can then be run with AI and ML deployed to help build better components. These steps can help an automaker create better products.

At what stages of automotive design can these technologies be deployed?

The simulation should be deployed in the product development phase. This must be done before prototyping. AI and ML can be used throughout the product lifecycle.

Can AI and ML help improve automotive industry supply chains?

Yes absolutely! AI, ML and even simulation can help improve supply chains. For example, we can use data to calculate how much we will manufacture, and then the same data can help suppliers understand the type and quantity of raw materials and components they will need to supply.

All of this can be done in advance, say four to six months or more. Thus, simulation data can help a supplier, component manufacturer, or designer create better components in advance. They can then proactively contact OEMs about the refined quality of components.

There is a shortage of data in India. So how do you solve this problem?

Much of the data can be created and obtained from the test phase itself. A good number of test vehicles are running in India before the launch of the final version. These trials, from Kashmir to Kanyakumari, generate a lot of data.

Whether high-end, mid-range or low-end cars, all have transmitters. Many of them also have mobile apps. All of this can be used to collect and analyze data and create better products.

Wouldn’t investing in tools based on AI, ML and simulation increase the CAPEX of the automotive industry?

I think the benefits outweigh the cost. Sensors aren’t very expensive these days, so that’s one less thing to worry about for the industry. Then OEMs can also offer smart features to their consumers.

Then there are cost savings for OEMs. For example, suppose the frame or chassis weighs 10 kg and our tools help an OEM reduce it to 8 kg; they save 2 kg of raw material in terms of production volume and millions of vehicles. So we are talking about lakh kilos of reduced weight.


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