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Automating the Optical Inspection of X Ray Ceramic Matrices with NI-IMAQ and LabVIEW 7 Express

Author(s):

Anand Krishnan, Soliton Technologies Private Limited; Ganesh Devaraj, PhD, Soliton Technologies Private Limited; Anish Mathews, Soliton Technologies Private Limited

Industry:

Life Science

Product:

Data Acquisition, LabVIEW, Signal Conditioning, Vision

The Challenge:

Developing a powerful automated optical inspection (AOI) system for detecting, quantifying, and classifying surface defects on x ray ceramic matrices.

The Solution:

Building a highly reliable, capable AOI system using image processing tools from National Instruments.


image
A Typical Vision Assistant Screen

Updating a Manual Process
Identifying visual defects in the x ray scintillator matrix is critical to the image quality produced in medical imaging devices. Previously, a group of trained operators inspected the surface by viewing the unit under inspection (UUI) under a microscope. This manual inspection made defect identification and classification rather subjective, which necessitated multiple rounds of inspection. As a result, the customer wanted to automate this process with a system that would:

  • Identify and classify various surface defects such as spots, cracks, voids, grind-marks, dark bars, and chip-offs
  • Devise and record a quantitative measure for the severity of these defects
  • Provide the capability to tune the image processing parameters to modify the defect classification thresholds
  • Incorporate software architecture to enable the vision algorithm developer to tweak algorithms, run a regression test, and update software from a remote site upon finding new types of defects


The customer required system:

  • Flexibility – The system should be able to inspect various scintillator ceramic matrix element models.
  • Reliability – The system had to meet strict inspection requirements for reliability and repeatability.
  • Networking – The system should permit configuration changes and statistical reviews on the inspection results from any PC within the customer network.
  • Compactness and Ergonomics – The system should efficiently utilize valuable manufacturing floor space with ease of use.


For this application, we selected a 1280 by 960 pixel Sony FireWire digital camera, which had many programmable features. We could configure more than 12 parameters, such shutter speed and filter selection, from the application software. Using NI LabVIEW, we had the flexibility to configure the defect classification criteria, set up automatic e-mail alarms, and transfer data through FTP.

NI-IMAQ and LabVIEW Minimize System Challenges

The inspection station consisted of an enclosed imaging chamber and a fixture for the UUI, which a pneumatic slide moved in and out of the chamber. We built the fixture modularly, such that, for different matrix types, the operator could change the holder in less than a minute. We controlled the pneumatics and lighting with digital lines, and manually loaded the UUI. The pneumatic slide took the UUI into the imaging chamber. The system imaged and processed the UUI and displayed the results onscreen. After inspection, the operator unloaded the UUI and placed it into the appropriate bin.

The Sony FireWire camera provided features that could capture the optimal processing image. The NI-IMAQ for IEEE 1394 driver provided easy access to all of the camera parameters and simplified the setup processes. By obtaining optimal image settings, we could minimize the image processing challenges. This application required the different defect type identification and classification on a variety of surfaces. We used the NI Vision Assistant, with its extensive libraries, in this project prototype phase. NI Vision Assistant provided an optimal prototyping environment to identify the key image processing parameters that best differentiated each kind of defect. For instance, we realized that a combination of the dispersion extent in a UUI-face discolored area (parameter 1) and edge straightness detected in the discoloration area (parameter 2) could help classify one particular kind of defect from another. In the design phase, we also studied the trained inspection personnel experiences and applied that knowledge to develop algorithms to mimic the skilled inspector.


We witnessed the unifying nature of LabVIEW as the software platform. During the prototype stage, the system developed image processing algorithms that were easily translated into production-ready application software. Furthermore, with LabVIEW, we developed a user-friendly HMI; interacted with the digital controls for sequencing the inspection process; generated reports; provided SPC analysis tools; generated e-mails; and provided FTP capability. Using the LabVIEW e-mail and FTP capabilities, we added the remote debugging and update feature to the design.


Virtual Instrumentation Provides Outstanding Results
We built an error-proof inspection system using the latest in virtual instrumentation and machine vision technology. Custom-built adaptable algorithms ensured reliable inspection irrespective of the variations in the components. The system helped the customer reliably control quality at early production stages. The wealth of data generated by the system provided feedback to improve the manufacturing process and quality. We are presently looking at upgrading the system to run on the NI Compact Vision System to provide further reliability and compactness.

Using NI drivers, we seamlessly integrated the various pneumatic slide controls, lighting, and image acquisition from the camera with the image processing routines. We completed and validated all of the image processing work for various ceramic matrices models within a short 16 weeks, which would have been impossible without the excellent NI vision development tools and LabVIEW.

For more information, contact:
Anand Krishnan
Senior Project Engineer
Soliton Technologies Private Ltd.
Blue Moon Icon, 420 6th B Cross, 20th Main,
Koramangla 6th Block,
Bangalore – 95, India
Tel: +91 (80) 2550 4677
Fax: +91 (80) 2550 4692
E-mail: kanand@solitontech.com