10 use cases of AI in manufacturing (Part 2)

Generative design

Generative design is a process that involves a program generating a number of outputs to meet specified criteria. Designers or engineers input design goals and parameters such as materials, manufacturing methods, and cost constraints into generative design software to explore design alternatives. The solution utilizes machine learning techniques to learn from each iteration what works and what doesn’t. Let’s have a look at this example from Autodesk:

The algorithm finds countless ways of designing a simple thing – e.g. a chair. You have to input the parameters: four legs, elevated seat, weight requirements, minimal materials, etc. Then, the algorithm generates a variety of options. The software is not there to replace humans, though. It’s another example of AI being an augmentation to human work. As described by Autodesk: “Computational design doesn’t replace human creativity—the program aids and accelerates the process, expanding the limits of design and imagination.”

Generative design is a way to explore ideas that could not be explored in any different way – just think about how much time it would take a real person to come up with a hundred different ways to design a chair. Artificial intelligence can do it in no time, letting the human expert choose from a wide range of options. Digital transformation like that can change the way a company delivers value to the customers and improve efficiency of processes.

Digital twins

A digital twin is a virtual representation of a factory, product, or service. The representation matches the physical attributes of its real-world counterpart through the use of sensors, cameras, and other data collection methods. In an article for Forbes, Bernard Marr writes about digital twins: “This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to head off problems before they even occur, prevent downtime, develop new opportunities and even plan for the future by using simulations.”

To make digital twins work, the first thing you have to do is integrating smart components that gather data about the real-time condition, status or position with physical items. The components are connected to a cloud-based system that received all the data and processes it. And why do we need technology like that?

Let’s look at NASA, who was one of the first organizations to adopt the technology. They needed a solution that would allow them to operate, maintain, and repair systems that were not in their physical proximity. John Vickers, NASA’s leading manufacturing expert and manager of NASA’s National Center for Advanced Manufacturing says: “The ultimate vision for the digital twin is to create, test and build our equipment in a virtual environment. Only when we get it to where it performs to our requirements do we physically manufacture it. We then want that physical build to tie back to its digital twin through sensors so that the digital twin contains all the information that we could have by inspecting the physical build.”

Source: Neoteric