AiCAMstir Kick-off Meeting, 29 July 2021

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The aiCAMstir Kick-off Meeting was held online on 29th July 2021 with 16 attendees.

Link to the video

Recording of the aiCAMstir Kick-off Meeting (64 min)

Title slide

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

Photos of six attendees and a list of the 17 attendees.
Screenshot of the aiCAMstir Kick-off Meeting. Please click here to see the recording of the aiCAMstir Kick-off Meeting (64 min)]

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
Maharashtra Institute of Technology, Aurangabad Sandeep Pankade 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.
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 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 he shared his vison: "We want to create an open access cloud, where FSW users can upload and use data and information for machine learning."


aiCAMstir: artificial intelligence (machine learning) in Computer Aided Manufacturing of friction stir welds - The friction stir weld made at 1300 rev/min and 1000 mm/m welding speed shows the visually best results so far.
Three friction stir welds made at 5000 rev/min, 3000 rev/min and 1300 rev/min at 1000 mm/m welding speed. The weld with the lowest rotation speed has the best visusal apprearance.
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
Quality depends on Parameters, Variables and Boundary values
FSW: Data to be processed: Input data and output data. The quality depends on parameters, variables and boundary values
Main challenges: hooking, thinning and Rremnant joint line
Hooking, thinning and remant joint lines are typical challenges if conventinal FSW butt welding tools are used for Lap welding
Three concepts for improved FSW tools
Improved tool designs to be investigated in the aiCAMstir project

Computional Fluid Dynamics

FSW Simulation using Computional 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
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

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)
• “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).

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?
The FSW circle:
• Torque, 𝒕, Function of strength
• FSW Power, 𝑷, Function of torque
• Temperature, 𝑻, Function of power
• Strength, 𝝈, Function of temperature
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
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)
Example:
• Thick plate Rosenthal gives: Power 𝑃

Friction Stir Welding

Eight introductory slides were presented by Laboratório de Metalurgia Física (LAMEF):

Slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees
Friction stir welding (LAMEF/UFRGS)
Slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees
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

Friction stir welding

Slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees
Friction stir welding (FSW): FSW machine (4000 rpm, 70 kN, 2000 mm) and process.

Friction stir welding: weld characterization:

Slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees
FSW: Weld characterisation
• Mechanical properties
&nbps; • Bending tests
&nbps; • Tensile tests
• Metallurgical characterisation
&nbps; • Tool wear analysis

FSW: Numerical Simulation

Slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees
FSW: Numerical Simulation: https://youtu.be/d0_as7Nf_Q0

Friction-based welding processes at LAMEF/UFRGS

Slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees
*Friction-based welding processes at LAMEF/UFRGS:
&nbps; • Friction Stir Welding (FSW): https://youtu.be/mYYn7O_w06s &nbps; • Friction hydro-pillar processing: https://youtu.be/j--FMNFIYVI
&nbps; • Linear friction welding: https://youtu.be/j3pm_rTK1GE
&nbps; • Friction welding of pipes: https://youtu.be/DoNgR33GDnA
* Recent publication on FSW of AA 5083: http://dx.doi.org/10.4322/2176-1523.20202450
Slide of the aiCAMstir Kick-off Meeting, which was held online on 29th July 2021 with 16 attendees
Universidade Federal do Rio Grande do Sul – UFRGSF, ProEng - Unidade EMBRAPII, Laboratório de Metalurgia Física – LAME

Two companies – one team

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
Computer vision, Machine learning, Data science - Cloud-based platforms - IoT - Embedded systems - Desktop, mobile and Web-applications - Industrial automation
Our Competencies
Web, Data Science, Embedded Systems, DevOps, Mobile, Industrial Automation
Relaed Technologies
AI coach in a mobile app which analyses fitness exercises and provides statistic.
Key projects: Kerebra
Technologies: С++, Rust, Golang, Python, TensorFlow, Flutter, Firebase, PostgreSQL - Platform: Google Cloud Platform, Kubernetes, Android, iOS, Web
Key projects: Insolar
Smart Industry Group, Kharkiv, Poltavsky Shlyakh str., 123 - Latrock GmbH, Darmstädter Landstr. 116, 60598 Frankfurt am Main
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

Date of next meeting

The next meeting will be held as shown on Upcoming events. Please contact stephan.kallee@alustir.com if you want to participate in this meeting and/or the aiCAMstir project