Massimiliano (Max) Moruzzi is co-founder and CEO of Xaba (Xaba.ai), a startup focused on intelligent automation to enable sustainable manufacturing. Moruzzi has an extensive 15-year career as lead scientist and business innovator in sustainable materials and disruptive manufacturing process automation, including work for Autodesk.
As the lead scientist and director of business development for Magestic Inc., he was responsible for revolutionizing the business models of major OEMs in the aerospace (Boeing, Airbus, Lockheed Martin) and automotive (FCA, GM, FORD, Lamborghini, Ferrari) industries.
AI is revolutionizing the field of mechanical CAD by helping designers and engineers automate routine tasks and optimize designs for better performance. By using AI algorithms, engineers can generate detailed 3D models with complex features in a fraction of the time it would take to do it manually. It can also be used to analyze data from multiple sources and identify patterns that may not be visible to the human eye allowing engineers to make better decisions about their designs and optimize them for performance or cost-effectiveness.
“When we talk about artificial intelligence or generative design, in mechanical CAD, we're talking about an ecosystem of different mathematical operations,” says Moruzzi. “It's not just machine or deep learning or algorithms. It is creating a boundary for your shape and allows you to develop it in a comprehensive and dynamic design space. These rules ensure that if your object moves outward or inward, it respects the dimensions and manufacturability as well as traditional math.”
He continues, “Previously, when someone was dragging a mouse over the digital space to create a design, they had a preconceived idea of the outcome. But the shape they created can't understand any of the constraints necessary to exist. For example, will a specific polymer work in the way it is drawn in a printable testing machine? Without AI, there is no way to know. Now, I can put the object into these next generation of design spaces where AI understands the constraints it needs to make a functional design.”
As AI presently stands, it is not a replacement for mechanical engineers. It can’t create designs without prompts or input: “What you have is a companion. You can bounce your ideas off of AI, or it can inspire you to develop a new way of seeing things. It’s not replacing any jobs, nor is it a magic wand,” says Moruzzi. “You have a multi-dimensional window that you didn't have before. Until now, the window you were looking through was a single layer.”
While AI can be a powerful tool in streamlining the design process, it is important to remember that it is not a substitute for human creativity and expertise. AI algorithms can identify patterns and make recommendations based on large data sets but cannot replace the human touch. Designers and engineers must provide the right input and prompts to the AI algorithms to produce their desired results.
Also, many AI algorithms have limitations and may need help to handle certain design situations or problems requiring a more nuanced solution. “People are still necessary to instill beauty and experience, not just how good a design is. AI can help you build a building, but if I want to have a magical experience every time there is a sunset or sunrise or when it's raining, then that is where the human touch comes in,” he shares.
Adding AI to mechanical CAD software has tremendous benefits. Integrating machine learning and generative design into platforms adds a new dimension to engineering: “One primary benefit is that you can test the entire design space, not just a single point,” says Moruzzi. “You can test patterns that have never existed or approach your project from a different direction. Because of the complexity that shapes carry, with all the attributes that can be analyzed and many variations, it is impossible to do this kind of analysis with the current system without AI. With the right mathematical tools, you can augment computation to move into a design space with significantly more value.”
“When I was working with NASA, we were discussing a spaceship we designed for a mission into Europa, one of Jupiter's moons. NASA posed a problem in a meeting about a multifunctional shape we had designed. Someone from the committee asked, ‘What about if we change the material? Or what about if we change the shape?’” recalls Moruzzi.
“With current tools, the design team would have to go back and recompile a new scenario and return maybe two weeks to a month later with an answer. Now with AI, which has intelligent constraints where you can see the entire design space, you can enter a meeting like that with intelligence baked into the design, change the variables, and get an immediate response. You are going from a digital drafting to a decision-making tool.”
The future of CAD AI in mechanical engineering is vast: “I can see that at some point in the future, we can capture new boundaries such as beauty or performance. Today, it is difficult to ask a system to make something beautiful or functional. What we're building in AI is the language that, in essence, will drop the barrier to describe something multifunctionality and have it built. I'm expecting that there will be a lot of convergence in that direction,” says Moruzzi.
“Right now, the engineering model is still very much building an object with that single scope. Then assemble them one on top of another. It’s not very efficient. With potential AI developments, we can describe constraints that are very difficult for us right now,” he adds. “And it won't just be beauty. We will be able to constrain things like efficiency or sustainability that will help our planet. We will unlock knowledge in physics that is impossible to know today.”
Moruzzi’s company Xaba.ai is making advances in the 3D printing space: “Xaba is developing the first AI machine for 3D printing. Currently, 3D printing is seen as a toy for rapid prototyping. This is largely due to the fact that the CAD software doesn't offer any visibility into the outcome. We are developing a machine learning model that captures the physics of the material so the user doesn't have to worry about the polymer they are using. The CAD AI will adjust automatically, lowering the barrier completely for designing on this platform. We hope to eliminate slicing, and instead, it can just tell me if my design will succeed or not,” he explains.
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