CuttingEdge 4.0

Facing edge-cracking in AHSS: towards zero-defect manufacturing through novel material characterisation and data driven analytics for process monitoring

CuttingEdge4.0 RFCS project holistically addresses the edge-cracking problem in AHSS considering fundamental knowledge related to material behaviour, predicting tools and industrial processes and incorporating Industry 4.0 data driven analytics based on Artificial Intelligence (AI) and Machine Learning (ML) expert systems.

The final aim is to transfer to the automotive industry tools and methodologies to predict edge-cracking in the early part design stages, detect edge-cracking defects and assure part quality during forming, boosting the applicability of AHSS-based automotive lightweight parts.

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New Methodologies

Experimental and digital-twins methodologies to predict edge-cracking in early part design stages

Data Driven Analytics

Industry 4.0 data driven analytics for process manufacturing

Objectives

Laboratory tests

Set conditions to accurately reproduce in laboratory tests the edge-crack resistance of AHSS.

Digital Twins

Develop phenomenologically-based digital twins for industrial cutting processes with the possibility to change process parameters and predict final product properties.

Fracture Toughness

Implement fracture toughness as a parameter to understand edge-cracking in AHSS as a failure criterion in FEM.

Predictive System

Develop a predictive edge quality system based on AI and ML techniques to be able to establish unknown key parameters correlations.

Research beyond the state of the art

Experimental predicting tools

Further exploration and research on the main parameters affecting HER (clearance, tool wear, radial strain gradient, tool stiffness, cutting speed) and optimisation of the method to better describe edge-cracking in AHSS.

Further understanding of edge-damage and crack propagation

Extension of the existing knowledge about edge-damage and fracture toughness on the overall resistance to the edge-cracking and provide a new experimental technique (EWF) to characterise the steel resistance to edge-cracking and predict the material behaviour in numerical simulation.

Novel edge-cracking modelling approach

Further explore the applicability of a new modelling approach coupling forming (trough FLC) and edge-damage and crack propagation to accurately predict edge-damage.

Expert systems and experimental data driven analytics for metal sheet forming

Apply data analytics to specifically tackle edge-cracking based on process parameters and material characterisation for the first time.

CONSORTIUM

Organisations from 7 different EU countries covering all the value chain, from steel manufacturers, component producers to car manufacturers.

CuttingEdge4.0 brings together industrial partners and research institutes to improve edge-cracking predictive tools using an industrial perspective to find affordable solutions.

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