ManufacturingPredictive Analytics

Glass Futures builds AI-driven digital twin to reinvent glass manufacturing

Glass Futures· United KingdomNVIDIA RTX Pro 6000 GPU · NVIDIA PhysicsNeMo

Glass Futures, based in St Helens, England, built an AI-driven digital twin of its first-in-the-world multi-fuel pilot glass furnace, developed with NVIDIA and the University of Liverpool's Virtual Engineering Centre as part of the AI-GLASS project (£1.5m Innovate UK/Made Smarter UK funding). Using NVIDIA RTX Pro 6000 GPUs and NVIDIA PhysicsNeMo, the digital twin combines real pilot-line sensor data (16 sensor points) with physics-informed neural networks to run over 11,530 calculations simultaneously, accurately predicting glass output and letting manufacturers test new fuels, electric heating and low-carbon alternatives like hydrogen and biofuels before trying them on the real furnace.

Overview

Glass Futures, based in St Helens, England, built an AI-driven digital twin of its first-in-the-world multi-fuel pilot glass furnace, developed with NVIDIA and the University of Liverpool's Virtual Engineering Centre as part of the AI-GLASS project (£1.5m Innovate UK/Made Smarter UK funding). Using NVIDIA RTX Pro 6000 GPUs and NVIDIA PhysicsNeMo, the digital twin combines real pilot-line sensor data (16 sensor points) with physics-informed neural networks to run over 11,530 calculations simultaneously, accurately predicting glass output and letting manufacturers test new fuels, electric heating and low-carbon alternatives like hydrogen and biofuels before trying them on the real furnace.

This entry has 12 published fields tied to exact passages in an immutable source capture.

Inspect the highlighted source

The challenge

Predicting how changes such as temperature, pressure, density, new fuels or heating methods would affect real furnace output required slow, costly physical experimentation, and complex phenomena like flame shape were difficult to describe to a computer.

The solution

Glass Futures, working with NVIDIA and the University of Liverpool's Virtual Engineering Centre on the AI-GLASS project, built a physics-informed AI digital twin of its multi-fuel pilot furnace using NVIDIA RTX Pro 6000 GPUs and NVIDIA PhysicsNeMo, combining real pilot-line sensor data from 16 sensor points with the laws of physics to predict glass output.

Predictive AnalyticsDigital Twins & Simulation

Reported business value

The digital twin can process over 11,530 calculations at once to accurately predict glass output, letting manufacturers test new fuels, electric heating, bubbling and other techniques virtually before trying them on a real furnace, removing barriers to experimentation and accelerating adoption of low-carbon alternatives such as hydrogen and biofuels.

Sources

Open any source and check the claim yourself — that is the point of the register.

This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)

Related entries

Other manufacturing entries in the register.

All entries
ManufacturingMachine LearningPublic Cloud

ArcelorMittal Enhances Steel Production Through Digital Innovation

ArcelorMittal partnered with IBM Consulting and Infosys to modernize operations through AI, cloud technology and SAP S/4HANA migration. At ArcelorMittal Eisenhüttenstadt, machine learning was introduced to predict and prevent surface defects on automotive steel sheets. At the Hamburg wire rod plant, AI optimized the trimming process by analyzing historical production data to determine optimal cutting points, reducing trim scrap by 20% and contributing to energy savings and lower CO2 emissions. ArcelorMittal also deployed a bio-inspired Ant Colony Optimization algorithm to calculate optimal production schedules, reducing downtime and material waste. At AM/NS India, IBM Consulting used IBM Rapid Move for SAP S/4HANA to migrate data and applications from outdated platforms to a single SAP instance across locations in Dubai, Indonesia and India.

96/100HighPrimary source
ArcelorMittalMachine learning (defect prediction) · Ant Colony Optimization algorithm · SAP S/4HANA +1
ManufacturingComputer VisionUnknown

Michelin runs 200+ AI use cases across manufacturing, supply chain and innovation

French tire manufacturer Michelin has more than 200 AI use cases in production, led by group chief data and AI officer Ambica Rajagopal. Its in-house IRIS system, protected by over 20 patents, partially automates end-of-line visual tire defect inspection to improve inspector efficiency and workplace ergonomics while operators retain final accountability. Machine learning forecasting tools improve demand forecast accuracy and proactively detect stock shortages in the supply chain. Michelin scans the startup ecosystem and uses tools including Databricks and Dataiku, and has partnerships with Microsoft and Rockwell Automation to codevelop AI solutions. The company reports AI-project ROI exceeding €50 million per year, growing 30-40% annually for three consecutive years, governed by an internal data office and responsible-AI principles (people-centric, explainable, accountable).

92/100HighPrimary source
Michelin· FranceDatabricks · Dataiku
ManufacturingGenerative AIPublic Cloud

Schneider Electric fast-tracks innovation with Azure OpenAI Service

Schneider Electric bases customer-facing AI solutions on Azure OpenAI Service within Microsoft Cloud for Manufacturing. Its EcoStruxure Microgrid Advisor uses Azure OpenAI Service and Azure IoT for dynamic control of facility energy performance, EcoStruxure Resource Advisor Copilot helps customers manage energy usage, and the company is developing a PLC code generation copilot to automate programmable logic controller programming for manufacturing robots and IoT devices.

96/100HighPrimary source
Schneider Electric· FranceAzure OpenAI Service · Azure Machine Learning · Azure IoT +1
ManufacturingLarge Language ModelsUnknown

Foxconn Develops Physical AI-Enabled Smart Factories With Digital Twins

Foxconn (Hon Hai Technology Group) uses physically accurate digital twins integrating NVIDIA Omniverse libraries and OpenUSD to design, deploy, and manage high-volume production facilities, including those producing NVIDIA GB200 Grace Blackwell Superchip systems. Its Fii Omniverse Digital Twin (FODT) platform creates virtual replicas of factories, enabling simulation-driven design, real-time monitoring, and optimized operations. Using NVIDIA PhysicsNeMo AI models, Foxconn achieves 150x faster computational fluid dynamics simulations for thermal analysis (minutes vs. hours). Standardized OpenUSD-based digital twin assets enable rapid migration of entire production lines between global factories (e.g., Taiwan to Mexico). Robot workcells and AGV logistics are simulated in FODT before physical deployment: complex robotic tasks such as screw tightening and cable insertion are simulated and refined with NVIDIA Isaac Sim, Isaac Lab, FoundationPose models, and NVIDIA cuMotion, while AGV path is optimized by connecting simulations with Material Control Systems and NVIDIA cuOpt. Foxconn also built video analytics AI agents with NVIDIA Metropolis and the NVIDIA AI Blueprint for video search and summarization to monitor factory floors, and developed FoxBrain, an AI platform powered by NVIDIA NeMo trained in four weeks, described as Taiwan's first large language model with advanced reasoning capabilities. Leo Guo, General Manager of Fii Robotic Group, said the company believes it can cut factory setup and planning time by about 50%.

92/100HighPrimary source
Foxconn (Hon Hai Technology Group)NVIDIA Omniverse · OpenUSD · NVIDIA PhysicsNeMo +5

Was this helpful?

Your feedback helps us improve our use case database