![Illustration of the atomic composition of the fcc FeMnNiCoCr alloy. Various colors correspond to different elements: Fe (grey), Mn (purple), Ni (gold), Co (blue), Cr (green). Image: MCL](/fileadmin/_processed_/9/8/csm_Adamant_01_34957006e3.jpg)
Predictive Modeling of Strength in High Entropy Alloys
Computational design of alloys can reduce cost of advanced materials development
![© Ceratizit Austria GmbH](/fileadmin/_processed_/c/8/csm_SmartDrilling_01_ebb3271317.png)
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.
![Structural parts in aircraft that are manufactured by voestalpine Böhler Aerospace, image: voestalpine BÖHLER Aerospace](/fileadmin/_processed_/4/4/csm_MarTough_01_bc06e92b80.png)
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.
![Tara Winstead on Pexels](/fileadmin/_processed_/8/3/csm_Porgrammiersprachen01_47510619c4.jpg)
Use of Probabilistic Programming Languages for Material Models
Researchers at MCL and TU Wien use new programming languages to make material models more meaningful.
![Scanning acoustic microscopy image data used for testing on wafer-level. This region of a wafer contains approximately 800 TSVs (black dots), yellow squares indicate regions with defected TSVs; image: MCL](/fileadmin/_processed_/8/1/csm_Null-Fehler-Management_01_98da200833.png)
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.
![Architecture of the MCL material development platform ALPmat. Image: MCL](/fileadmin/_processed_/8/e/csm_Alpmat_1_5acfcfbc0c.png)
Software Platform for AI-based Material Development
At MCL existing material knowledge is combined with artificial intelligence to significantly accelerate material development.
![Sketch of the MCL Materials Accelerator Platform, compilation: MCL](/fileadmin/_processed_/b/4/csm_AI_Turbo_1_533faac6ef.jpg)
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.