IPSUSDr. Jorge F. dos Santos

GKSS - Forschungszentrum, Geesthacht

Institute of Materials Research

WMP – Solid State Joining Processes

Virtual Institute

Improving Performance and Productivity

of Integral Structures through

Fundamental Understanding of

Metallurgical Reactions in Metallic Joints

FSWProcesses

Materials AlLi / AlMgSc ODS Steels

Knowledge

Gap

Viscous flow and

diffusion processes in

high speed FSW and

joining by forming

Precipitation

Phenomena in

FSW of Al and Mg

Alloys

Methodology Process Simulation

Material Flow Visualisation

by Computer Tomography

In-Situ monitoring of metallurgical phenomena

using synchrotron-based diffraction techniques

Phase analysis with TEM X-Ray Diffraction

Mechanical TestingMicrostructure Analysis

Coextrusion

Artificial

Neural Networks

6060 / AZ31

Methodology for improved

productivity in high speed FSW

and hybrid extrusion profiles

Methods & procedures for

microstructure control of FSW in

Al, Mg and advanced steels

Methodology and hardware for in-situ monitoring of

metallurgical phenomena using synchrotron-based

diffraction techniques

Guidelines for design of

Al and Mg alloys with

superior weldability

Demonstrator: hybrid

extrusion profile / high speed

FSW welded component

Improved productivity in

high speed FSW and

hybrid extruded profiles

Recrystallisation

Kinetics in FSW of

Al and Mg Alloys

Phase

transformation

in FSW of steels

TWIP Steels

Motivation

Strategic Research Topics

1. In-situ synchrotron-based diffraction techniques

2. Non-Equilibrium Precipitation and Recrystallisation

Phenomena in Metallic Joints

AlLi Alloy (2198-T8)

AlMgSc

Automotive: 6082 (6181), 5457

3. Phase Transformation and Phase Formation in FSW of

Advanced Steels

Twinning Induced Plasticity Steels (TWIP)

Oxide Dispersion Strengthened Steels (ODS)

4. Coextrusion of Hybrid Al-Mg Profiles

5. FSW Process Modelling for High Production Rates

6. Artificial Neural Network

Objectives

1. To develop the methodology and hardware for in-situ

monitoring of non-equilibrium metallurgical phenomena using

synchrotron-based diffraction techniques,

2. To describe the kinetics and phenomena of precipitation and

recrystallisation in FSW and coextrusion of Al and Mg alloys

3. To describe the kinetics of phase transformation and

formation and their influence of the joint properties of friction

stir welded TWIP and ODS steels

4. To develop the methodology for coextrusion of hybrid Al-Mg

profiles

5. To develop a FSW process model for high production rates

6. To implement the use of artificial neural networks to establish

a generalised computer model for process-properties

relationships

Further Aims

1. To disseminate the work in VI-IPSUS to the public to develop and hence

establish a Centre of Expertise for In-Situ Monitoring of Physical

Phenomena in Production Processes as a medium to long-term

perspective.

2. Qualification of Post-Docs (by assigning the responsibilities of

technical work and organisation alongside of the respective experts

from HGF centres and universities)

3. Training of young scientists by dedicated trainee programmes by

adequate combination of sub-tasks to be conducted at both HGF-

centres and universities

4. Acquisition of third party funding by using the network of expertise

developed via the VI-IPSUS and hence offering a team of experts for

solving complex industrial problems.

5. Dissemination of scientific results in workshops, conferences,

publications etc. and hence contributing for enhancement of existing

scientific knowledge in the fields of the covered areas by the VI-IPSUS.

Results of analysis and testing of “as welded” an coextruded samples

(Post-mortem Results)

In-situ Diffraction

Experiments

WP2

Assembly and Commissioning of

the In-situ Experimental Equipment

WP2

Preliminary In-situ

Experiments

WP2

Processes

FSW and Coextrusion

WP1

Artificial Neural

Network

WP6

Process

Modelling

WP5

Recrystallisation and

Precipitation

WP3

Phase

Transformation

WP4

Microstructural

Analysis

WP3/WP4

Mechanical

Testing

WP1

Flow

Visualisation

WP1

Ex-situ X-Ray

Diffraction

WP4

Expertise Centre for

In-situ Monitoring of

Physical Phenomena in

Production Processes

Deliverables

and

Achievements

Work Programme: General Strategy

WP1 – Processes and Characterisation (GKSS)

Task 1.1 – Development of FSW Process Parameters

(GKSS, RUB, MPIE, FZK)

Task 1.2 – Development of Coextrusion Parameters

(TUB, GKSS)

Task 1.3 – Mechanical Testing (GKSS; MPIE; FZK)

WP2 – In-Situ Diffraction Experiments (GKSS)

Task 2.1 – Design and Fabrication of the FlexiStir

Unit (GKSS)

Task 2.2 – Installation and Commissioning of the

Experimental Set-Up (GKSS)

Task 2.3 – In-Situ Diffraction Experiments (GKSS;

TUB, MPIE, FZK)

WP3 – Precipitation and Recrystallisation (TUB)

Task 3.1 – Analysis of In-Situ Diffraction and

Tomography Experiments (TUB, GKSS)

Task 3.2 – Coextrusion Model (TUB, CU)

Task 3.3 – Microstructure Analysis (RUB, TUB,

GKSS)

Task 3.4 – Analysis of Precipitation and

Recrystallisation in Coextrusion (TUB,

GKSS, RUB)

Task 3.5 – Precipitation and Recrystallisation

Phenomena in FSW (GKSS, RUB)

WP4 – Processes Deformation Mechanisms and

Phase Transformations in Steels (RUB)

Task 4.1 – Development of FSW Process

Parameters (MPIE, FZK, GKSS)

Task 4.2 – Tomography Experiments on Similar and

Dissimilar Steel Joints Parameters

(GKSS, MPIE, FZK, CU)

Task 4.3 – Microstructure Analysis (RUB, MPIE,

FZK)

WP5 – Processes Modelling (CU)

Task 5.1 – Optimisation of the Existing FSW Model

to Higher Welding Speeds (CU)

Task 5.2 – Material Flow Experiments (GKSS; FZK,

MPIE, CU)

Task 5.3 – Application of the Model to Steels (CU)

Task 5.4 – Validation of the new Model (CU,

GKSS,MPIE, FZK)

WP6 – Artificial Neural Networks (GKSS)

Task 6.1 – Gathering of Experimental Data (GKSS,

CU)

Task 6.2 – Modelling Data with Neural Network

(GKSS)

Task 6.3 – Validation of the Model (GKSS, CU RUB)

WP7 – Management

Work Programme: General Strategy

WP1 – Processes and Characterisation

- Material procurement

- ODS: FZK

- TWIP: MPIE

- Al and Mg Alloys: GKSS

- Al / Mg Profiles (coextrusion): TUB

- Base material characterisation

- Study on the welding metallurgy of the selected base materials

- Gleeble Experiments

- Initial development of FSW process parameters (GKSS Gantry + Fixed Gap Bobbin Tool)

- Development of FSW Process Parameters with the FlexiStir

- Welding of specimens for material flow visualisation

- Welding of specimens for microstructural analysis and mechanical testing

- Development of Coextrusion Procedures for similar (Al/Al and Mg/Mg) and dissimilar

configurations

- Development of high speed FSW procedures for Al and Mg alloys

- Welding of specimens for material flow visualisation, microstructural analysis and

mechanical testing

- High temperature testing (input data for modelling)

Work Programme: Detailed Activities

Work Programme: Detailed Activities

FSW with a fixed gap bobbin tool

WP2 – In-Situ Diffraction Experiments

- Design and Manufacturing of the FlexiStir Unit

- Commissioning of the FlexiStir Unit at GKSS/WMP

- Installation and Commissioning of the experimental set-up for in-situ diffraction experiments

at DESY

- Performing in-situ diffraction experiments

- Processing of acquired data

Work Programme: Detailed Activities

WP3 – Precipitation and Recrystallisation

- Microstructural analysis of base material (base material characterisation), Gleeble samples,

welded specimens and coextruded samples (post-mortem analysis) – Al and Mg alloys

- Analysis of the in-situ diffraction experiments

- Analysis of the -computer tomography

- Correlation of the in-situ experiments with post-mortem analysis (FSW)

- Correlation of the ex-situ experiments with post-mortem analysis (Coextrusion)

- Formulation of the precipitation and recrystallisation phenomena in FSW (Al and Mg alloys)

Work Programme: Detailed Activities

WP4 – Processes Deformation Mechanisms and Phase Transformations in Steels

- Development of FSW Process Parameters (GKSS-Gantry and fixed gap bobbin tool)

- Development of FSW Process Parameters with the FlexiStir

- Welding of specimens for material flow visualisation

- Welding of specimens for microstructural analysis and mechanical testing

- Tomography Experiments on Similar and Dissimilar Steel Joints Parameters

- Microstructure Analysis

WP5 – Processes Modelling

- Optimisation of the Existing FSW Model to Higher Welding Speeds

- Material Flow Experiments

- Application of the Model to Steels

- Validation of the new Model

WP6 – Artificial Neural Networks

- Gathering of Experimental Data

- Modelling Data with Neural Network

- Validation of the Model

WP1 – Processes and Characterisation

C.W. Olea (GKSS)

WP2 – In-Situ Diffraction Experiments

Dr. R. Martins (GKSS)

WP3 – Precipitation and Recrystallisation

B. Camin (TUB)

WP4 – Processes Deformation Mechanisms and Phase Transformations in

Steels

Dr. Christoph Somsen, RUB

WP5 – Processes Modelling

Dr. P. Colegrove

WP6 – Artificial Neural Networks

R. Zettler

WP7 – Management

G. Amancio

Work Programme: WP Leaders

Work Programme: Time Schedule

Year 1 Year 2 Year 3

Activities Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

WP1 Process Task 1.1 Develop. of FSW Procedures

Task 1.2 Develop. of Coextrusion Pro Task 1.3 Mechanical Testing

WP2 In-Situ Diffraction Experiments

Task 2.1 Design and Fabrication of the FlexStir unit

Task 2.2 Installation and Commissioning of the Exp. Setup

Task 2.3 In-situ Diffraction Experim.

WP3 Precipitation and Recrystallisation

Task 3.1 In-situ Diffraction and Tomography Experiments

Task 3.2 Coextrusion Model Task 3.3 Microstructure Analysis Task 3.4 Precipitation and Recrystallisation in Coextrusio n

Task 3.5 Precipitation and

Recrystallisation in FSW

WP4 Deformation Mechanisms and Phase Transformations

Task 4.1 Ex-situ Diffraction

Experiments on Similar and Dissimilar Steel Joints

Task 4.2 Tomography Experiments on Joints of Similar and Dissimilar Steel Joints

Task 4.3 Microstructure Analysis

Milestone Description Date

1 Development FSW concluded Month 3

2 Development of Coextrusion Month 12

3 FlexStir Availability Month 9

4 Begin In-Situ Diffraction Experiments Month 13

5 Optimised FSW Process Model Month 12

6 Modell Neural Network Month 6

WP5 Process Modelling Task 5.1 Optimisation of the Existing Model to Higher Welding Speeds

Task 5.2 Material Flow Experiments Task 5.3 Application of the Model to Steels

Task 5.4 Validation of the New Model

WP6 Artificial Neural Network Task 6.1 Gathering of Experimental Data

Task 6.2 Modelling Data with Neural Network

Task 6.3 Validation of the Model WP7 Management Task 7.1 Coordination and

Management of the Research

Task 7.2 Promotion of Young Scientists

Task 7.3 Dissemination Activities REPORTING PROJECT MEETINGS

6 Month Progress Report 12 Month Project Report

Year 1 Year 2 Year 3

Activities Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Work Programme: Time Schedule

Deliverable (D) / Milestone (M) Due date

Development of FSW procedures concluded (M) Month 3

Weldability Report (D) Month 6

FlexiStir availability (M) Month 9

WP1

Development of Coextrusion procedures concluded (M) Month 12

Methodology and hardware for in-situ monitoring of non-equilibrium metallurgical phenomena in productionprocesses using synchrotron based diffraction technique (D)

Month 12WP2

Begin in-situ diffraction experiments (M) Month 13

Procedure and interlayer selection for coextrusion of hybrid Al-Mg profiles (D)

Process model describing thermo-mechanical phenomena in coextrusion of hybrid Al-Mg profiles (D)

WP3

Time resolved description of precipitation and recrystallisation kinetics in FSW of Al and Mg alloys as well as indissimilar joints (D)

WP4 Time resolved description of phase formation and transformation in FSW of advanced steels and dissimilar jointsthereof (D)

Literature review (D) Month 6

Optimised FSW process model (M) Month 12

Process model describing thermo-mechanical phenomena in high speed FSW, which will support the selection ofstrategy and parameters for high productivity FSW (D)

Month 24

WP5

Process model for FSW of steel materials (D) Month 36

Model Artificial Neural Network (M) Month 6WP6

Artificial neural network for the dete rmination of op timal parameters based on experimentally determined complexprocess-properties relationship. (D)

Month 36

Deliverables and Milestones

Technology Demonstrators

1. coextruded hybrid Al-Mg profile

2. high speed friction stir welded tailored blanks

3. dissimilar Al-TWIP and Mg-TWIP steel joints

4. dissimilar ODS-Steel based joints