Altair and L&T Technology Services Establish Digital Twin Center of Excellence to Accelerate Global Innovation
The CoE will help organizations learn the latest methodologies and technologies in the areas of:
- AI-powered engineering to transform products, systems, and processes
- Innovation labs to simulate new use cases
- Predictive maintenance
- Rapid product development to reduce cycle time for physical prototyping
- Hands-on training
“The strategic partnership with Altair is poised to revolutionize the digital twin technology landscape. By leveraging LTTS’ unparalleled cross-domain engineering expertise and Altair’s exceptional simulation and data analysis capabilities, we are set to redefine industry standards across segments such as mobility, sustainability and hi-tech,” said Abhishek Sinha, executive director and president of Medical, Smart World, and Functions, LTTS. “Together, we are committed to pioneering advancements that will shape the future of technological integration.”
LTTS has extensive experience in digital twin technology with multiple CoEs across its design centers. Its Digital Twin for Line Operations enhances performance through virtual commissioning, efficiency monitoring, predictive maintenance, and root cause analysis using real-time and historical data. It supports strategic digital transformations and offers modular implementation and role-based access. Additionally, LTTS has engineered a digital twin with over 54 machine-learning algorithms to automate oil rig operations. To read more, click here or refer to the e-book to explore the world of digital twin and LTTS’ role in the digital twin revolution.
LTTS has already utilized Altair’s leading total digital twin portfolio – covering solutions from both the Altair® HyperWorks® design and simulation platform and Altair® RapidMiner® data analytics and AI platform – for multiple diverse use cases, including:
- Motor, battery, and gear box systems
- Automotive electrical systems
- Heart attack and stroke risk prediction
- Data-driven automotive production line performance
- Data-driven wind power forecasting