Data to be processed

The aiCAM^stir project investigates Artificial Intelligence based Computer Aided Manufacturing of Friction Stir Welds

Data to be processed in the aiCAM^stir project are parameters, variables, boundary values and quality.

Key data to be processed

Data to be processed in the aiCAMstir project are parameters, variables, boundary values and quality.

According to digitalisation and data processing experts, the following categorisation of key data could be considered:

Parameters	Variables	Boundry Values	Quality
Values that you set	Values that result during welding	Values that affect the results	Quality
Rotation speed Welding speed Tool design	Tool temperature Clamping forces	Workpiece materials Workpiece cleanliness Tool materials and coatings Temperature of the workpieces Temperature of the fixture Geometry of the set-up Spindle tilt angle	Tensile strength Elongation Visual appearance Fatigue strength Voids (pores) Weld depth Lack of penetration Bend test results Inclusions Helium-tightness
Tool position Tool forces Spindle torque
	Gap between the parts Tool wear

The categorisation depends on the concept and complexity of the machine. Modern FSW machines provide close-loop force and position control algorithms, which complicate the distinction between parameters and variables. Some machines use the temperature as a parameter that can be set, e.g. to change the parameters if the tool gets too hot.

Please feel free to improve the classification above or to propose an alternative classification. Or use the discussion page for discussing changes.

CAM and FSW

• Computer Aided Manufacturing (CAM) includes data monitoring, data processing and data visualisation
• Friction Stir Welding (FSW) is a special process, and its quality can only be determined statistically

Benefits

• Simplify the data exchange
• Reduce the reaction time
• Automate the documentation
• Provide more attractive products and services

Traps

• CAM has no value by itself
• Humans need to be involved
• Raw data aren't providing information
• Visual management is required

The two approaches to data collection and processing

Bottom Up

• Collect and store as much data as possible at all times
• Use Data Mining to process the data according to specific needs
  • Advantages: Infrastructure will be installed. Data transfer is standardised
  • Disadvantages: There might be no short-term benefit. Creation and storage of data garbage^(WP)

Top Down

• Specific objectives
• Data collection is related to the needs
  • Advantage: Very good cost-benefit-ratio. Simple entry and fast progress
  • Disadvantages: Risk of insular solutions. Data might not be sufficient for the next project

Methods from concept to model

Academic approach

If you want to create a model of a friction stir welding you can progress academically. This is for instance useful if you are not sure about the types of data to be collected and their availability and quality.

• Advantages: neutral, analytic and powerful
• Disadvantages: large effort ist required

An ten-step example is as follows:

1. Define the process and the objective of the model
2. Select the data to be collected
3. Determine the data field
4. Find the data source
5. Create an experimental plan (e.b. by design of experiments)
6. Generate data
7. Select a suitable model and build it
8. Train the model
9. Evaluate the model
10. Assess the model: Is it okay?

Pragmatic approach

The pragmatic approach follows the conventional development procedures. It makes use of expicit or intuitive expert knowledge. If large numbers of experts are involved this is occasionally called swarm intelligence. Their know-how needs to be transferred into the data processing system.

• Advantages: Useful concepts can be developed in a short time frame. Already known correlations can be used. Quick validation.
• Disadvanatages: not fully neutral. Depends on individual expertise. Risk of tunnel vision.

Visualisation

• Spider diagrams are easy to understand
• KPIs (key performance indicators) should be used
  • Present operating and idle time, brutto capacity, number of rejected parts and overall efficiency etc.
• Data monitoring should be applied
  • Create chart of the production rate, freak values, number of rejected parts, reasons for rejects including metallographic samples etc
• Report
  • Trend of the reject rate
  • Monthly, quarterly or yearly production
  • Performance development