GM is experimenting with design strategies for vehicles motivated by AI and cloud computing.

In the field of technology, artificial intelligence (AI) and self-driving cars are often discussed together. Though AI is being applied at a breakneck pace in a number of industries, the way it's being used in the automotive industry is currently a contentious subject. Every car maker and its parent company is striving to develop artificial intelligence and self-driving technology, and several tech companies and startups are pursuing the same goal.

While many people believe that personal, autonomous vehicles are the way of the future, AI and machine learning are being used in a variety of ways in the design and operation of vehicles.

General Motors, one of the largest global automaker, is taking a giant step forward towards automotive design by imagining a future of lighter, more powerful, and customizable vehicles. GM is experimenting with a variety of design strategies for vehicle components and parts that leverage generative design, which is motivated by artificial intelligence and cloud computing. The company can produce complex parts and special components for customized vehicles more sustainably and conveniently using additive manufacturing. Together, these innovations allow GM to provide customers with better results and more opportunities than ever before.

Since 2016, GM has introduced 14 new vehicle models with a combined mass reduction of over 5,000 lbs, or more than 350 pounds per vehicle, the majority of which is due to material and technology advances.

According to Fanatical Futurist, "If 3D printing and additive manufacturing were a door to the automotive future, the generative design would be the key to unlock it," says GM Director of Additive Design and Manufacturing, Kevin Quinn. "Generative design is a way for us to explore different design solutions for parts and components of our vehicles by using the cloud and artificial intelligence to combine the engineer and the computer. By getting them to work together, we can come up with part-design solutions that would be impossible to generate with either the computer or the engineer working on their own."

With this model, engineers establish component-design goals and constraints – including parameters such as materials, manufacturing methods, and budget – and then input them into generative design software. The software then uses an algorithm to analyze and evaluate dozens of design permutations and recommends an optimal solution.

It also added "Generative design paired with additive manufacturing can be completely disruptive to our industry," says Quinn, who adds that the auto industry historically has been impeded by the limitations of traditional manufacturing tools such as mills and injection moulds. For one, such tools can fabricate only very simple geometries.

Also, traditional tools are as expensive as they are inflexible, which makes experimentation cost-prohibitive. Generative design and additive manufacturing can support infinite design solutions with minimal capital investment.