Spector for Composite Manufacturing

Problem

PROBLEM

Quality inspection, associated  defect reporting, MRB disposition, repair, and reinspection cause delay and creates excessive inventory 
In a typical composites manufacturing workflow inspection tends to be a bottleneck. It occurs because NDT machines, inspection equipment, and qualified personnel are shared resources in a facility among many production lines. When nonconformance is identified it must be reported. The inspector typically creates a sketch or an annotated photograph to provide the engineering the most exact description and context of the nonconformance.
The burden of defect reporting, associated MRB disposition, repair, and reinspection causes delay and creates excessive inventory. The manual nature of the work greatly limits the throughput and consistency of the issue analysis.
Translation of the defect location from the part to the drawing and then finding it again on a part during the reinspection is often a challenging task especially for the large parts (fairings, flaps, fuselage panels). Repair procedures tend to be complex, with references to specs that are unavailable on the shop floor.
Model Composite Manufacturing Workflow
Component Fabrication
Component Inspection & NDT
PASS
Component Fabrication
Final Inspection
PASS
Dispatch
FAIL
FAIL
Tag
Defect Reporting
MRB Disposition
Repair
Non-Confomance Manegemant
Solution

SOLUTION

Spector empowers shop floor  technicians with the spatial computing allowing to connect inspection records directly to the defect location
Marker coordinates in space are generated automatically. This provides the repair crew not only accurate defect location but also measurements of the size, area, and distance to other objects.
Pictures, videos, notes, and historical maintenance data are linked to the marker enabling stress-free engineering disposition. Availability of consistent historical data supports more sound trend analysis.
Depending on its location marker provides access to the relevant repair instructions and historical data. It becomes a spatial quality record storing pictures, notes, and sensor readings over time.
The algorithm helps to detect and classify surface defects while upon completing the repair the correctness is again confirmed via the automated system.
The algorithm helps to detect and classify surface defects while upon completing the repair the correctness is again confirmed via the automated system.