AiCAMstir Kick-off Meeting, 29 July 2021: Difference between revisions
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The '''aiCAMstir Kick-off Meeting''' was held online on 29th July 2021 with 16 attendees. | The '''aiCAMstir Kick-off Meeting''' was held online on 29th July 2021 with 16 attendees. | ||
== Title slide == | |||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 01.jpeg |thumb|left|upright=3.2|alt=Title slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees.|Title slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 17 attendees]] | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 01.jpeg |thumb|left|upright=3.2|alt=Title slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees.|Title slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 17 attendees]] | ||
== Screenshot == | |||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 02.JPG|thumb|left|upright=3|alt=Photos of six attendees and a list of the 17 attendees.|Screenshot of the aiCAMstir Kick-off Meeting]] | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 02.JPG|thumb|left|upright=3|alt=Photos of six attendees and a list of the 17 attendees.|Screenshot of the aiCAMstir Kick-off Meeting]] | ||
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|} | |} | ||
== Agenda == | |||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 03.JPG|thumb|left|upright=3.2|alt=Agenda of the aiCAMstir Kick-off Meeting.|Agenda of the aiCAMstir Kick-off Meeting: | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 03.JPG|thumb|left|upright=3.2|alt=Agenda of the aiCAMstir Kick-off Meeting.|Agenda of the aiCAMstir Kick-off Meeting: | ||
• Welcome and introduction (2-3 sentences each) <br /> | • Welcome and introduction (2-3 sentences each) <br /> | ||
| Line 59: | Line 62: | ||
• Organisational comments and date of next meeting]] | • Organisational comments and date of next meeting]] | ||
== Computer Aided Manufacturing of friction stir welds - The vision == | |||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 04.JPG|thumb|left|upright=3.2|alt=Stephan Kallee being interviewed: "Parameter optimisation is often difficult during prototyping, production ramp-up and production. We want to create an open access cloud, where FSW users can upload and use data and information for machine learning."|Stephan Kallee commented: "Parameter optimisation is often difficult during prototyping, production ramp-up and production," and shared the vison: "We want to create an open access cloud, where FSW users can upload and use data and information for machine learning."]] | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 04.JPG|thumb|left|upright=3.2|alt=Stephan Kallee being interviewed: "Parameter optimisation is often difficult during prototyping, production ramp-up and production. We want to create an open access cloud, where FSW users can upload and use data and information for machine learning."|Stephan Kallee commented: "Parameter optimisation is often difficult during prototyping, production ramp-up and production," and shared the vison: "We want to create an open access cloud, where FSW users can upload and use data and information for machine learning."]] | ||
| Line 87: | Line 90: | ||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 09.JPG|thumb|left|upright=3.2|alt=Three concepts for improved FSW tools|Improved tool designs to be investigated in the aiCAMstir project]] | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 09.JPG|thumb|left|upright=3.2|alt=Three concepts for improved FSW tools|Improved tool designs to be investigated in the aiCAMstir project]] | ||
== Computional Fluid Dynamics == | |||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 10.JPG|thumb|left|upright=3.2|alt=|FSW Simulation using Computerised Fluid Dynamics (CFD) – Butt Weld <br /> | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 10.JPG|thumb|left|upright=3.2|alt=|FSW Simulation using Computerised Fluid Dynamics (CFD) – Butt Weld <br /> | ||
Torque Comparison: <br /> | Torque Comparison: <br /> | ||
| Line 109: | Line 112: | ||
[https://www.mdpi.com/2075-4701/10/7/872 www.mdpi.com/2075-4701/10/7/872]]] | [https://www.mdpi.com/2075-4701/10/7/872 www.mdpi.com/2075-4701/10/7/872]]] | ||
== Artificial Intelligence and machine learning algorithms == | |||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 12.JPG|thumb|left|upright=3.2|alt=|•The first step is the construction of a '''high-quality dataset''' of good and bad welding. This dataset may consist of experimental and/or numerical samples.<br /> | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 12.JPG|thumb|left|upright=3.2|alt=|•The first step is the construction of a '''high-quality dataset''' of good and bad welding. This dataset may consist of experimental and/or numerical samples.<br /> | ||
<br /> | <br /> | ||
| Line 127: | Line 131: | ||
• '''Deep Neural Networks''' may be used to extract more subtle patterns from images/videos or time series (e.g. Convolutional Neural Network - CNN).]] | • '''Deep Neural Networks''' may be used to extract more subtle patterns from images/videos or time series (e.g. Convolutional Neural Network - CNN).]] | ||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 14.JPG|thumb|left|upright=3.2|alt=|Predictions of FSW power: <br /> | == Analytical models == | ||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 14.JPG|thumb|left|upright=3.2|alt=|Predictions of [[FSW power]]: <br /> | |||
• Torque, 𝑡, needed to make a weld is unknown <br /> | • Torque, 𝑡, needed to make a weld is unknown <br /> | ||
• Power consumed by tool rotation is 𝑡𝜔 <br /> | • Power consumed by tool rotation is 𝑡𝜔 <br /> | ||
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]] | ]] | ||
== Friction Stir Welding == | |||
[[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 19.JPG|thumb|left|upright=3.2|alt=|PHYSICAL METALLURGY LABORATORY (LAMEF) | [[File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 19.JPG|thumb|left|upright=3.2|alt=|PHYSICAL METALLURGY LABORATORY (LAMEF) | ||
We are part of the Metallurgy Department, School of Engineering, Federal University of Rio Grande do Sul (UFRGS), Brazil. We have four professors, thirty engineers and one technician who together with PhD students, Masters and undergraduates compose a workgroup of around 200 people in different areas of expertise. We have an impressive infrastructure for the characterization of engineering materials, as well as performing mechanical tests in air and in corrosive media, numerical simulation (finite element) and non-destructive tests. <br /> | We are part of the Metallurgy Department, School of Engineering, Federal University of Rio Grande do Sul (UFRGS), Brazil. We have four professors, thirty engineers and one technician who together with PhD students, Masters and undergraduates compose a workgroup of around 200 people in different areas of expertise. We have an impressive infrastructure for the characterization of engineering materials, as well as performing mechanical tests in air and in corrosive media, numerical simulation (finite element) and non-destructive tests. <br /> | ||
| Line 194: | Line 200: | ||
• Recent publication on FSW: http://dx.doi.org/10.4322/2176-1523.20202450 ]] | • Recent publication on FSW: http://dx.doi.org/10.4322/2176-1523.20202450 ]] | ||
== Two companies – one team == | |||
[[File:Two companies - one team; Smart Industry Group and Latrock GmbH 01.jpg|thumb|left|upright=4|alt=Smart Industry Group: Ukrainian company specializing in developing software for PLC, SCADA-systems, and software solutions in various industries - Latrock GmbH: German company specializing in Data Science, Machine Learning, Web Development, DevOps, Embedded System, IoT and Mobile Applications. | [[File:Two companies - one team; Smart Industry Group and Latrock GmbH 01.jpg|thumb|left|upright=4|alt=Smart Industry Group: Ukrainian company specializing in developing software for PLC, SCADA-systems, and software solutions in various industries - Latrock GmbH: German company specializing in Data Science, Machine Learning, Web Development, DevOps, Embedded System, IoT and Mobile Applications. | ||
|Two companies - one team: Smart Industry Group and Latrock GmbH]] | |Two companies - one team: Smart Industry Group and Latrock GmbH]] | ||
Revision as of 16:14, 30 July 2021
The aiCAMstir Kick-off Meeting was held online on 29th July 2021 with 16 attendees.
Title slide
Screenshot
List of Attendees (alphabetically sorted by first name)
| Company | People | Status | Signed-up on |
|---|---|---|---|
| Please enter your company name | Azman Ismail - Ts. Dr. | Guest | |
| Please enter your company name | Breno Boretti Galizoni | Guest | |
| Please enter your company name | Erhard Buchmann | Guest | |
| Please enter your company name | Gokhan Tekin_Alcomet_R&D_Director (Konuk) | Guest | |
| LAMEF | Guilherme V. B. Lemos | R&D Institute | 2021-06-01 |
| Latrock GmbH | Heorhii Vdovychenko | Data science, machine learning etc | 2021-07-12 |
| Sabe Technology Ltd | Josselin Guillozet | Consultancy service provider | 2021-02-18 |
| Please enter your institute's name | Koen Faes | R&D Institute | 2021-04-14 |
| Please enter your company name | Laurie Da Silva | Guest | |
| FTS Engineering Answers Ltd | Mike Lewis | Consultancy service provider | 2021-01-14 |
| Please enter your company name | Mansoor, Bilal | Guest | |
| Please enter your company name | MJ Sefat | Guest | |
| TPO Maharashtra Institute of Technology Aurangabad | Please enter your name | Guest | |
| Please enter your institute's name | Renan Landell | R&D Institute | 2021-06-07 |
| Please enter your institute's name | Sandeep | R&D Institute | 2021-07-25 |
| Please enter your company name | Shivraman | Guest | |
| AluStir | Stephan Kallee | Consultancy service provider | 2021-01-14 |
Agenda
Agenda of the aiCAMstir Kick-off Meeting: • Welcome and introduction (2-3 sentences each)
• Computer Aided Manufacturing of friction stir welds - The vision (Stephan Kallee, AluStir)
• Computional Fluid Dynamics (Mike Lewis, FTS Engineering Answers Ltd)
• Artificial Intelligence and machine learning algorithms (Josselin Guillozet, Sabe Technology Ltd)
• Analytical models (Simon Smith, Transforming Stress Ltd)
• Friction Stir Welding (LAMEF/UFRGS)
• Two companies – one team (Smart Industry Group and Latrock GmbH)
• Needs and contributions of the attendees (3-5 sentences)
• Discussion
• Organisational comments and date of next meeting
• Computer Aided Manufacturing of friction stir welds - The vision (Stephan Kallee, AluStir)
• Computional Fluid Dynamics (Mike Lewis, FTS Engineering Answers Ltd)
• Artificial Intelligence and machine learning algorithms (Josselin Guillozet, Sabe Technology Ltd)
• Analytical models (Simon Smith, Transforming Stress Ltd)
• Friction Stir Welding (LAMEF/UFRGS)
• Two companies – one team (Smart Industry Group and Latrock GmbH)
• Needs and contributions of the attendees (3-5 sentences)
• Discussion
• Organisational comments and date of next meeting
Computer Aided Manufacturing of friction stir welds - The vision
Avoid:
• Wrong parameters
• Simple tools
• Inappropriate machines
• Chamfer, radius or taper on workpieces
• Insufficient clamping
Reasons for low-quality welds:
• Downward pressure is too low
• Welding speed is too fast or slow
• Rotation speed is too high or low
• Tool position is too high
• Tool rotates in the wrong direction
• Gap between the work pieces
• Wrong parameters
• Simple tools
• Inappropriate machines
• Chamfer, radius or taper on workpieces
• Insufficient clamping
Reasons for low-quality welds:
• Downward pressure is too low
• Welding speed is too fast or slow
• Rotation speed is too high or low
• Tool position is too high
• Tool rotates in the wrong direction
• Gap between the work pieces
Computional Fluid Dynamics
FSW Simulation using Computerised Fluid Dynamics (CFD) – Butt Weld
Torque Comparison:
• Test = 38 Nm
• CFD = 30 Nm
Heat Input Comparison:
• Test = 2000 W
• CFD = 1800 W
FTS is also involved in: • Standard Setting (energy Institute Subsea Guidelines)
• Joint Industry Projects
:• Multiphase FIV JIP
:• Multiphase FIV SIG
• Many trouble shooting projects
Torque Comparison:
• Test = 38 Nm
• CFD = 30 Nm
Heat Input Comparison:
• Test = 2000 W
• CFD = 1800 W
FTS is also involved in: • Standard Setting (energy Institute Subsea Guidelines)
• Joint Industry Projects
:• Multiphase FIV JIP
:• Multiphase FIV SIG
• Many trouble shooting projects
FSW Simulation using Computerised Fluid Dynamics (CFD) – Butt Weld
Comparison with Aldanonda Work for conventional thread at 1200 rpm and 250 mm/min www.mdpi.com/2075-4701/10/7/872
Comparison with Aldanonda Work for conventional thread at 1200 rpm and 250 mm/min www.mdpi.com/2075-4701/10/7/872
Artificial Intelligence and machine learning algorithms
•The first step is the construction of a high-quality dataset of good and bad welding. This dataset may consist of experimental and/or numerical samples.
•Machine learning models could then be trained to predict the right set of tool parameters to achieve a good welding (supervised learning)
• Input parameters (features) of the models may be:
::• Physical quantities and derivatives
::• Time series of these same physical quantities
::• Images/videos
• Output (target) may be a continuous variable like the torque (regression) or whether the welding is expected to be good or bad (classification)
•Machine learning models could then be trained to predict the right set of tool parameters to achieve a good welding (supervised learning)
• Input parameters (features) of the models may be:
::• Physical quantities and derivatives
::• Time series of these same physical quantities
::• Images/videos
• Output (target) may be a continuous variable like the torque (regression) or whether the welding is expected to be good or bad (classification)
• “Non-deep” learning models like Linear Regression, Support Vector Machine or Decision Trees may be directly used with well-designed input features. The model may be inspired by equations of the expected physics.
• Signal/Image processing and Computer Vision methods may help extract relevant features from images or videos.
• Deep Neural Networks may be used to extract more subtle patterns from images/videos or time series (e.g. Convolutional Neural Network - CNN).
• Signal/Image processing and Computer Vision methods may help extract relevant features from images or videos.
• Deep Neural Networks may be used to extract more subtle patterns from images/videos or time series (e.g. Convolutional Neural Network - CNN).
Analytical models
Predictions of FSW power:
• Torque, 𝑡, needed to make a weld is unknown
• Power consumed by tool rotation is 𝑡𝜔
• Can these be calculated?
• Torque, 𝑡, needed to make a weld is unknown
• Power consumed by tool rotation is 𝑡𝜔
• Can these be calculated?
Baseline variables for analysis
• Temperature, 𝑇 is a function of the unknown power, and:
::• Distance from tool axis, 𝑥
::• Travel speed, 𝑣
::• Thermal properties, conductivity, 𝑘 and diffusivity, 𝑎
::• Room temperature, 𝑇0
• Strength, 𝜎 is a function of the unknown temperature, and:
::• Room temperature strength, 𝜎𝑅𝑇
::• Material melting temperature, 𝑇𝑀
• Torque, 𝑡 is a function of the unknown material strength near the tool, and:
::• The radius of the tool, 𝑅
• FSW power is equal to the unknown torque, 𝑡 times the rotation speed, ω
• Temperature, 𝑇 is a function of the unknown power, and:
::• Distance from tool axis, 𝑥
::• Travel speed, 𝑣
::• Thermal properties, conductivity, 𝑘 and diffusivity, 𝑎
::• Room temperature, 𝑇0
• Strength, 𝜎 is a function of the unknown temperature, and:
::• Room temperature strength, 𝜎𝑅𝑇
::• Material melting temperature, 𝑇𝑀
• Torque, 𝑡 is a function of the unknown material strength near the tool, and:
::• The radius of the tool, 𝑅
• FSW power is equal to the unknown torque, 𝑡 times the rotation speed, ω
Assumed relationships:
• Temperature, 𝑇 predicted using a Rosenthal equation, assuming power, 𝑃
• Strength, 𝜎 assumed to be a known function of 𝑇 between the room
temperature value, 𝜎𝑅𝑇 and zero at the melting point, 𝑇𝑀
• Torque based upon the need to “yield” the material with strength, 𝜎
• Power straightforward function of torque, 𝑡 and rotation speed, ω
• Hence:
::• Four equations
::• Four unknowns (𝑇, 𝑡, 𝜎, 𝑃)
• Answer: FSW power based upon (𝑣, 𝜎𝑅𝑇, 𝑇𝑀, 𝑅, ω, 𝑘, 𝑎)
• Temperature, 𝑇 predicted using a Rosenthal equation, assuming power, 𝑃
• Strength, 𝜎 assumed to be a known function of 𝑇 between the room
temperature value, 𝜎𝑅𝑇 and zero at the melting point, 𝑇𝑀
• Torque based upon the need to “yield” the material with strength, 𝜎
• Power straightforward function of torque, 𝑡 and rotation speed, ω
• Hence:
::• Four equations
::• Four unknowns (𝑇, 𝑡, 𝜎, 𝑃)
• Answer: FSW power based upon (𝑣, 𝜎𝑅𝑇, 𝑇𝑀, 𝑅, ω, 𝑘, 𝑎)
Example: • Thick plate Rosenthal gives: Power 𝑃
Friction Stir Welding
File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 19.JPG
PHYSICAL METALLURGY LABORATORY (LAMEF) We are part of the Metallurgy Department, School of Engineering, Federal University of Rio Grande do Sul (UFRGS), Brazil. We have four professors, thirty engineers and one technician who together with PhD students, Masters and undergraduates compose a workgroup of around 200 people in different areas of expertise. We have an impressive infrastructure for the characterization of engineering materials, as well as performing mechanical tests in air and in corrosive media, numerical simulation (finite element) and non-destructive tests.
• Please have a look at https://vimeo.com/261297853
• Visit our website on https://www.ufrgs.br/lamef/english/index.html
• Please have a look at https://vimeo.com/261297853
• Visit our website on https://www.ufrgs.br/lamef/english/index.html
File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 22.JPG
Friction stir welding: weld characterization
• Mechanical properties
::• Bending tests
::• Tensile tests
•Metallurgical characterisation
::• Tool wear analysis
• Mechanical properties
::• Bending tests
::• Tensile tests
•Metallurgical characterisation
::• Tool wear analysis
File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 25 .JPG
FSW: Numerical Simulation https://youtu.be/d0_as7Nf_Q0
File:AiCAMstir Kick-off Meeting, 29th July 2021 - Slide 27.JPG
Friction-based welding processes at LAMEF/UFRGS:
• Friction hydro-pillar processing: https://youtu.be/j--FMNFIYVI
• Linear friction welding: https://youtu.be/j3pm_rTK1GE
• Friction welding of pipes: https://youtu.be/DoNgR33GDnA
• Recent publication on FSW: http://dx.doi.org/10.4322/2176-1523.20202450
• Friction hydro-pillar processing: https://youtu.be/j--FMNFIYVI
• Linear friction welding: https://youtu.be/j3pm_rTK1GE
• Friction welding of pipes: https://youtu.be/DoNgR33GDnA
• Recent publication on FSW: http://dx.doi.org/10.4322/2176-1523.20202450
Two companies – one team
Contacts:
• Smart Industry Group, Kharkiv, Poltavsky Shlyakh str., 123, Phone: +38 (067) 765 27 40 and +38 (067) 573 79 99, E-mail: ceo@sig-automation.com, www.sig-automation.com
• Latrock GmbH, Darmstädter Landstr. 116, 60598 Frankfurt am Main, Phone: +49 6128 8600121, E-mail: contact@latrock.com www.latrock.com
• Smart Industry Group, Kharkiv, Poltavsky Shlyakh str., 123, Phone: +38 (067) 765 27 40 and +38 (067) 573 79 99, E-mail: ceo@sig-automation.com, www.sig-automation.com
• Latrock GmbH, Darmstädter Landstr. 116, 60598 Frankfurt am Main, Phone: +49 6128 8600121, E-mail: contact@latrock.com www.latrock.com