Manufacturers are using AI to build lightweight parts, reduce time to market, and rectify errors at the product conceptualization stage, when it’s least expensive.
The global coronavirus pandemic has brought into sharp focus the need for digitization and cost-cutting across manufacturing operations.
The design stage — where manufacturers decide on the materials, shape and size, and weight of components — impacts production and operational efficiency across the entire manufacturing value chain. (For example, the weight of an aircraft partition can impact how much an airline spends on jet fuel.)
In fact, design decisions determine roughly 70% of the manufacturing costs of a product, according to guidelines from the University of New Mexico.
In addition to being cost-conscious, manufacturers also need to continuously innovate, bringing new products and designs to market in order to stay competitive. But this is easier said than done: it can take 2 to 5 years to design a product like a car or an integrated circuit, and decades to discover entirely new types of materials, like glass.
Now, manufacturers are testing the use of AI to make this process more time- and cost-efficient, without compromising safety or structural integrity.
In part 1 of our series on AI in manufacturing, we look at promising AI use cases in manufacturing design and R&D, including:
- Generative design to create entirely new prototypes
- AI to accelerate circuit board design
- Machine learning approaches to discover next-gen glass and other materials