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Predictive Modeling of Strength in High Entropy Alloys
Computational design of alloys can reduce cost of advanced materials development
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Tool Condition Monitoring for Drilling Tools for Better Productivity and Process Reliability
Real-time diagnosis of damage via sensor data analyses during on-going production operations enables major increase in efficiency.
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Prediction of Decisive Material Properties of Aircraft Components
MCL is developing a model network that predicts spatially resolved fracture toughness and strength in the aircraft component.
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Use of Probabilistic Programming Languages for Material Models
Researchers at MCL and TU Wien use new programming languages to make material models more meaningful.
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Towards the Zero-defect Management of Through Silicon Vias (TSVs) in the Production Line
Advanced defect localization and classification of TSVs at Wafer Level Using Machine Learning Methods.
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Software Platform for AI-based Material Development
At MCL existing material knowledge is combined with artificial intelligence to significantly accelerate material development.

AI Turbo for the Development of Bainitic Steels
With the help of AI-supported models, the development time of sustainable high-performance steels is drastically reduced.