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structure (Fig. 4). In addition to detecting defects


or anomalies, the defect recognition software must also classify the fl aws. Flaws fall under many diff erent cat- egories and types, and each applica- tion has diff erent requirements for accepting or rejecting the parts, for example (Fig. 5): • Single flaw. • Flaw distance. • Flaw density. • Porosity. • String flaw. • Inclusion. Once the entire image processing


and classifi cation has been completed, an “accept” or “reject” decision/recom- mendation is provided by the system. Limiting the defect recognition to an “assisted” mode will aid in identify- ing false rejects and confi rming real defects, but the fi nal goal (even in the most critical applications) still can be fully automatic processing. It may take


Fig. 6. Example of ADR being utilized in Aerospace


some time and eff ort to create the cor- rect algorithms, correlate the required data, prove the statistical approach required and then process the data needed to make this step, but the tech- nology exists to make it possible. In an existing investment cast part


for an aerospace application, a test piece was designed and constructed with a range of small drilled holes. T e inspection criterion is that 0.1-mm


defects must be detected with a deci- sion threshold of 0.2mm. The “magenta” indications in


Figure 6 identify the holes detected at 0.2 mm and above. These defects could either be rejected automati- cally or flagged to be reviewed in assisted mode. T e “green” indications identify the holes detected smaller than 0.2 mm, down to the required 1 mm size.


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