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In the aiCAM<sup>''stir''</sup> project we want to go one step further. We want to create a cloud, into which FSW operators can upload images and information on parameter settings during feasibility studies, prototyping, production ramp-up and series production, and get feedback about the weld quality and recommendations on optimising the parameters. In the final stage, such a system would be integrated into the FSW machine, and the machine would optimise the parameters itself within boundries set by the operator.
In the aiCAM<sup>''stir''</sup> project we want to go one step further. We want to create a cloud, into which FSW operators can upload images and information on parameter settings during feasibility studies, prototyping, production ramp-up and series production, and get feedback about the weld quality and recommendations on optimising the parameters. In the final stage, such a system would be integrated into the FSW machine, and the machine would optimise the parameters itself within boundries set by the operator.
The vision was explained for the first time in public during an [https://youtu.be/tD6perdrnGU?t=4224 on-line discussion with students of the University of Liège] on 19 April 2021. 


== The first welds ==
== The first welds ==

Revision as of 16:11, 22 April 2021

Three friction stir welds made during a training course in Munich
Three friction stir welds made during a training course in Munich
The roots of tree friction stir welds made during a training course in Munich. The start is at the right hand side, the stop is a the left hand side

The Vision of the aiCAMstir project was determined during a friction stir welding training course in Munich.

Some years ago, Stephan Kallee had worked on a project on making tailor welded blanks by friction stir welding. A new spindle, a new jig, two FSW experts and a new technician were available, who was experienced in operating milling machines but had not received in-depth training on parameter optimisation. Due to an unforeseen business trip, they worked on three different sites: in the lab, in the office and in a hotel. They could only communicate by mobile phone, sharing photos and information on the parameter settings. After a few iterations, optimised parameters could be found.

A few years later, he considered using neuro-networks for optimising FSW parameters. During a training course in Munich, the concept of using artificial intelligence in computer aided manufacturing was discussed in more detail and a project with very promising results was conducted.

In the aiCAMstir project we want to go one step further. We want to create a cloud, into which FSW operators can upload images and information on parameter settings during feasibility studies, prototyping, production ramp-up and series production, and get feedback about the weld quality and recommendations on optimising the parameters. In the final stage, such a system would be integrated into the FSW machine, and the machine would optimise the parameters itself within boundries set by the operator.

The vision was explained for the first time in public during an on-line discussion with students of the University of Liège on 19 April 2021.

The first welds

The first welds were made by a group of experts and trainees in Munich. The trainees could choose the parameters after discussions with the experts and found visually acceptable parameter settings after a few iterations. The parameter matrix investigated during the course is shown below:

Visual appearance of five friction stir welds made during a training course at Munich
Photo Rotation Speed Welding Speed Downward Force Commment
AiCAMstir AS-SWK TaM 000.001 5000 rpm 500mm-min © Stephan Kallee at AluStir, CC BY-SA 4.0.JPG 5000 rev/min 500 mm/min Not documented Too hot
AiCAMstir AS-SWK TaM 000.002 1700 rpm 500mm-min © Stephan Kallee at AluStir, CC BY-SA 4.0.JPG 1700 rev/min 500 mm/min Not documented Visually acceptable but too slow
AiCAMstir AS-SWK TaM 000.003 5000 rpm 1000mm-min 7.6kN © Stephan Kallee at AluStir, CC BY-SA 4.0.JPG 5000 rev/min 1000 mm/min 7.6kN Too hot
AiCAMstir AS-SWK TaM 000.004 3000 rpm 1000mm-min 9.2kN © Stephan Kallee at AluStir, CC BY-SA 4.0.JPG 3000 rev/min 1000 mm/min 9.2kN Too hot
AiCAMstir AS-SWK TaM 000.005 1300 rpm 1000mm-min 9.8kN © Stephan Kallee at AluStir, CC BY-SA 4.0.JPG 1300 rev/min 1000 mm/min 9.8kN Visually acceptable, best weld so far