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July 2013
Software isolates, identifies porosity-related flaws in tooling from fuselage test data

Software and composites manufacturing data specialist NLign Analytics, a division of Etegent Technologies (Cincinnati, Ohio) helps identify and track product flaws caused by tooling porosity.

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Posted on: 7/1/2013
High-Performance Composites

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A screen shot from the software. Source: NLign Analytics

One of the challenges of manufacturing composite parts and structures from multiple tool sets is identifying and tracking product flaws caused by tooling porosity. This was the problem faced recently by an aerospace manufacturer that had good ultrasonic testing (UT) scan data and knowledgeable inspectors but no reliable way to use the data to drill back through the production process and find the source of porosity-caused flaws. The result was a costly scrap rate and inconsistent part quality in a fuselage component.

This scenario is one of many reasons why data management is emerging as a critical task. Composites production environments, across the board, are increasingly complex and demanding. To manage that complexity, software and composites manufacturing data specialist NLign Analytics, a division of Etegent Technologies (Cincinnati, Ohio) and author of NLign software, has inserted itself into this space and works with composites manufacturers to sort and organize valuable data.

In the case of the tooling porosity challenge in the fuselage part, Etegent and its customer used the UT data to sort through the flaws. NLign automatically aggregated and aligned hundreds of individual UT scans with inspection annotations and a large file of production and other inspection data for several successive production fuselage components. These data were then placed on a 3-D geometric model of the fuselage and associated tooling. The user then created a tube-shaped spatial filter along a tool seam to filter out all indications except those within the spatial filter’s cylindrical boundaries. Finally, the user created another filter to display only porosity indications within the previously created spatial filter. When this filter was applied, a growing pattern of porosity problems became apparent on the 3-D model.

Fortunately, in this case, the data from the analysis showed that most of the product flaws were associated with porosity problems along one tool seam of one particular tool set. Physical inspection of the seam verified the analysis. The seals were repaired, the porosity problems substantially decreased and so did the scrap rate.

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