The first-of-its-kind collaboration, say the companies, will leverage data analytics and AI to drive transformative business value by reducing development time and costs, and improving product performance and quality. In particular, the collaboration will address structural analysis and testing, bringing together vast amounts of historical product and in-service data from disparate sources to unlock new AI-driven engineering use cases to drive significant business value.
“As a pioneer of the convergence of data analytics and engineering, it’s a natural fit for Altair to collaborate with Rolls-Royce Germany, an organization that sees the power of technology and the potential of AI to unlock game-changing business value,” says James R. Scapa, founder and chief executive officer, Altair. “The demand for easy-to-use, low/no code, yet flexible AI and machine learning tools has never been greater.”
The collaboration will address a wide variety of use cases, including applying data science to the vast amounts of engineering testing data, which can lead to a significantly reduced number of sensors needed. This single use case alone, say the companies, has the potential to reduce recurring costs by millions of Euros.
“We share a common vision on the convergence of AI and engineering to drive significant positive business outcomes,” says Dr. Peter Wehle, Head of Innovation and R&T, Rolls-Royce Deutschland. “Altair has unique domain expertise and best-in-class, low-code data analytics technology. This collaboration will enable us to bridge the gap between engineering and data science, and empower our engineers to truly be engineers, focused on extracting the benefits of machine learning and AI from our data.”
Initially in the collaboration, Rolls-Royce will leverage Altair Knowledge Works – a collaborative end-to-end data analytics platform – to enable engineers to apply machine learning (ML) methods utilizing simulation data, test data, manufacturing data, and operational data. Knowledge Works is designed so users can easily and efficiently access disparate data sources and formats in a low code/no code environment, transform the data, use it to build ML models, and share knowledge discovery across the business.
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