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Prediction of forming limit diagrams using machine learning

Measuring forming limit diagrams (FLDs) is a time consuming and expensive process. Machine learning (ML) methods are a promising route to predict FLD of aluminium alloys. In the present work, we developed a machine learning (ML) based tool to establish the relationships between alloy composition / thermomechanical processing route to the material's FLD.

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Call for Abstracts: Numisheet 2020 mini symposium on “Challenges and Opportunities in Forming Aluminum”

The NUMISHEET conference series is the most significant international conference on the area of the numerical simulation of sheet metal forming processes. Within Numisheet 2020, we are organizing a mini symposium on “Challenges and Opportunities in Forming Aluminum”.

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Novelis and Georgia Tech Establish Novelis Innovation Hub

Atlanta-based Novelis Inc. and Georgia Tech have announced a new collaboration to establish the Novelis Innovation Hub at Georgia Tech. The company has committed $2.5 million to initiate research, faculty, student, and educational program support. The collaboration will promote basic and translational research, innovative business models, and related educational endeavors at Georgia Tech and will serve as a cross-functional hub connecting Novelis’ technical and business innovators with Georgia Tech’s students and faculty.

 

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R&D Team Manager, Metallurgy @ Novelis Inc.

This position is strictly for experienced candidates and is located at our Global R&T Centre in Kennesaw GA.

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Opportunity for Researcher - Finite Element Modeling at the Novelis Global Research and Technology Center in Kennesaw, Georgia

Please apply online at http://www.novelis.com/en-us/Pages/Careers.aspx

Select USA in the "Country" field and click on "Submit". On the next page, select "Research and Development" in the "Job" field and click on "Search for Jobs". The position is listed as "Researcher-Finite Element Modeling"

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Multi time scale simulations for wear prediction in micro-gears

Reliability of micro-gears is known to be adversely affected by wear. In this work we report a strategy to predict local wear with the aim of predicting their effective life span. For the prediction of local wear we start from the relevant model experiments, choice of a suitable wear model and identification of the wear coefficient from these experiments. This wear model is then implemented in an efficient finite element based scheme to predict local wear.

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Compositional patterning in coherent and dislocated alloy nanocrystals

Variations in the distribution of the alloy components can significantly influence the electronic properties

of self-organized alloy nanocrystals. Using a combination of finite element and quadratic programming

optimization methods, we have developed an efficient numerical technique to compute the equilibrium

composition profiles in coherent and dislocated nanocrystals. We show that the variations in composition

profiles arise due to the competition between chemical mixing effects and the relaxation of composition-

dependent mismatch strain as well as strain due to dislocations. We find that the composition profiles

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Presentation Tips

I am attaching a pdf of "How to give successful oral and poster presentations" by J.W. Niemantsverdriet of the Eindhoven University of Technology which I had found on the internet some years ago. It has some very useful tips.

V. Hegadekatte

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