AI in Design and Manufacturing: Autodesk’s Big Bet
Autodesk, a world leader in 3D design and engineering software, is making a big bet on artificial intelligence (AI) with the launch of Autodesk AI. The company envisions using AI to create and edit 3D models in real time using natural language and semantic controls. With its acquisition of BlankAI, Autodesk plans to debut this AI technology in its automotive design studio, allowing designers to generate new concepts based on historical design work. The company also plans to give users the ability to refine the AI algorithms using their own proprietary data, potentially leading to personalized machine learning. While this technology will initially be available in Autodesk’s automotive portfolio, the ultimate goal is to integrate it into its Fusion industry cloud.
Transforming PCB Design with Machine Learning: Zuken’s Long-Term Roadmap
Zuken, a global provider of software and consulting services for electrical and electronic design, is embracing machine learning in the PCB design process. The company’s Autonomous Intelligent Place and Route (AIPR) software, launched last year, uses AI to optimize layout design and reduce clashes. Zuken’s long-term roadmap includes the development of a Basic Brain, which enhances the user experience by routing designs utilizing machine learning techniques. The company also plans to offer a tool for customers to apply machine learning to their own library of designs, leading to the creation of a Dynamic Brain. However, the release of the Dynamic Brain is not expected in the near future, as Zuken focuses on perfecting the Basic Brain and ensuring data security and privacy for its customers.
Accelerating Innovation with AI-Powered Simulation: Ansys SimAI
Ansys, a leading provider of engineering simulation software, is harnessing the power of AI to revolutionize simulation. Ansys SimAI, a cloud-enabled platform, eliminates the need for parametrizing geometry by allowing users to train the software using simulation results. Users can upload their data, select the variables of interest, and let the software learn the correlations between the topology and the outcomes. The trained model can then be used to make predictions for new geometries. To ensure security, Ansys relies on Amazon Web Services (AWS) infrastructure, which is widely used by security-sensitive organizations. The incorporation of cloud computing has become a crucial part of Ansys’s strategy and offerings.
Personalized Machine Learning in Engineering: MathWorks’ MATLAB and Simulink
MathWorks, the developer of MATLAB and Simulink software, recognizes the trend towards personalized machine learning in engineering. MATLAB allows users to import their own engineering data and train models through a graphical user interface. Users can refine algorithms to work for specific scenarios or conditions, providing a more effective solution for their enterprise’s needs. MathWorks has also developed the MATLAB AI Chat Playground, trained on ChatGPT, to allow users to query the software using natural language. However, the tool is still experimental and evolving.
The Dilemma of Data in AI Training: Challenges and Opportunities
The reliable training of AI algorithms in design and manufacturing relies on having a large and diverse sample data pool. While larger companies may have access to a sufficient number of models for training, midsize manufacturers may struggle to reach a critical mass of data. John McEleney, former CEO of SolidWorks and co-founder of Onshape, highlights the importance of incorporating design assistant-type suggestions in AI training. However, he also acknowledges the challenge of protecting proprietary data while benefiting from AI training. The balance between accessing others’ data for more reliable algorithms and safeguarding competitive advantage poses a dilemma in the AI era.
Analyst comment
Positive news: The integration of AI technology in design and manufacturing by industry leaders Autodesk, Zuken, Ansys, and MathWorks showcases the potential for increased efficiency, optimization, and personalized solutions. This development is likely to drive innovation and enhance the user experience, leading to positive market growth and opportunities for these companies. However, the dilemma of data access and protection poses challenges that need to be addressed for the widespread adoption of AI in the industry.
