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  orange box General purpose flaw detection software finds cracks in turbine parts
The Challenge

Kelly Air Force Base contracted ACG to develop an automatic crack detection system to find cracks in turbine parts. This was being performed manually. An automated system to both detect cracks and update maintenance logs was an attractive alternative but other vendors had failed to provide a general purpose solution for a wide variety of turbine parts. 
ACG solution

Industrial processes often have to separate good parts from bad parts on the basis of  qualitative criteria. One has to select a criterion and then choose a threshold such that all the parts above the threshold will be classified as "bad", and all the parts below the threshold will be classified as "good".  However, such inspection tasks -- flaw detection, for example -- are difficult to automate because:

- A good criterion may need to be a combination of several measurements.
- It can be difficult to quantify inspection tasks that require some level of "judgment".
- Even with a good criterion one still must find the optimal value for the threshold.

The ACG Linear Classifier emulates some components of human judgment by combining criteria. The classifier computes an optimal linear combination of measurements so as to separate clearly the "good" and "bad" parts. To use the classifier the user sets up inspections to measure various part parameters (e.g. gray level, size, etc..) and then trains a representative sample of parts, telling the system whether each sample is good or bad. The classifier will then automatically determine the best combination of inspections, and suggest a threshold for pass/fail. Advanced features allow the user to view the training data on a 3-D plot to check the separation of data. 


The Linear Classifier software enables a teach by showing approach to defining qualitative inspection criteria. We have also successfully employed this technique to locate flaws in plastic housings and scratches on surfaces.