New experimental and modelling approaches to predict and avoid edge-cracking in the automotive industry

December 1, 2022 | Hybrid event – PW0 Czech Republic & Online

The CuttingEdge 4.0 project has developed new experimental and modelling approaches to predict and avoid edge-cracking in the automotive industry. In this workshop, we will present how the CuttingEdge 4.0 project has holistically addressed the edge-cracking problem in different AHSS grades by developing predicting tools, investigating the material response to cutting and forming and by incorporating Industry 4.0 data-driven analytics based on Artificial Intelligence and Machine Learning.

The event is targeted to professionals in the automotive industry and researchers willing to know more about novel tools and methodologies to predict edge-cracking in the early part design stages and assure part quality during forming.

1st December 2022
9.00 AM – 13.00 PM

Hybrid

PWO Czech Republic

Palackého 1261, 757 01 Valašské Meziříčí

Streaming

Workshop agenda & presentations

9.00 AM – 12.30 PM

9.00 – 9.10 – Opening Plenary: Welcome and project overview

Begoña Casas, Project Coordinator, Researcher at the Ceramic and Metallic Materials Unit, Eurecat

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Opening Plenary: Welcome and project overview

1 file(s) 1.84 MB
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9.10 – 9.40 – Lab tests methodologies to evaluate edge-cracking

Christian Walch, Head of Application Technologies, Voestalpine, and Matteo Ferrea, Researcher, Stellantis

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Lab Tests Methodologies to Evaluate Edge-cracking

1 file(s) 4.21 MB
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Formability Lab Test and Semi-industrial trials

1 file(s) 4.59 MB
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9.40 – 10.00 – Fracture toughness as a tool to address edge-cracking

David Frómeta, Head of the mechanical behaviour research line, Eurecat

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Fracture toughness as a tool to address edge-cracking

1 file(s) 5.96 MB
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10.00 – 10.20 –Digital twins for quality predictions of real industrial cutting processes

Olle Sandin, PhD Student, Luleå University of Technology

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Digital twins for quality predictions of real industrial cutting processes

1 file(s) 7.23 MB
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10.20 – 10.50 – Coffee Break

10.50 – 11.20 – In-line process monitoring

Martin Sláma, Project manager, Lead Engineer, PWO Czech Republic, and Martin Heuse, Simulation Team Leader, Faurecia

11.20 – 11.50 – Machine Learning techniques for Edge Crack identification

Jose Gonzalez, Researcher at the Applied Artificial Intelligence Unit, Eurecat

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Machine Learning techniques for Edge Crack Identification

1 file(s) 4.04 MB
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11.50 – 12.20 – Recommendations for the processing of cold and hot rolled AHSS

Eisso Atzema, Principal Scientist, Tata Steel

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Recommendations for the processing of cold and hot rolled AHSS

1 file(s) 4.38 MB
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12.20 – 12.30 –  Questions & Answers session

12.30 –  Closure