|NDT.net - October 2002, Vol. 7 No.10|
This paper describes the requirements, the structure, software setup, inspection procedure and economical advantages of fully automatic X-ray inspection systems for aluminium wheels in an industrial environment.
In the automotive industry aluminium wheels are safety relevant parts which have to be inspected by X-ray.
Most machines use a visual inspection by an operator. In the future, most of the inspection- systems will be designed as fully automatic inspection systems with an ADR (Automatic Defect Recognition) software (1).
Wheel customers now require these fully automatic inspection systems, because the defect recognition is more reliable than visual inspection. When comparing a fully automatic wheel inspection system with visual inspection, the automatic testing is usually much more economical.
The following regard and examples are done with the YXLON MU231 X-ray system with Automatic Inspector (AI) image processing. This solution is used more than any other worldwide for X-ray wheel inspection.
The inspection systems are mostly integrated as inline-systems in a foundry. Most requirements of an inspection system are based on detection of defects and on the inline - integration in an industrial environment.
The main components of an inspection system are (see fig. 1 and 2)
|Fig 1: MU231 X-ray Inspection System||Fig 2: MU231 X-ray Inspection System with Image Processing System|
After a short training (approx. 5 days), a new operator will be able to setup a new inspection program with the AI software quickly and reliably.
The setup requires the following Steps:
Step 1: Teaching the Vision Software for wheel-type recognition (see fig. 3).
|Fig 3: Image of the Vision System with a marked valve hole.|
Step 2: Sampling X-ray images of different views of the wheel with the AI to teach variations.
Step 3: Drawing the Regions Of Interest (ROIs) in one X-ray image (see fig. 4 a). With ROIs one has full control and can inspect every part of the image with different settings.
|Fig 4 a c: X-ray images with ROIs, different views.|
Step 4: Teach the system to place the ROIs on sampled images (see fig. 4 b, c)
Step 5: Training of the Neural Network in the AI (no action of the operator required)
Step 6: Entering the test specification (can be different for every ROI!)
Step 7: Testing the new inspection program in production
For the Steps 1,2 and 7, a complete X-ray system with image processing is required. Steps 3 to 6 can be continued at an offline system (PC with the image processing software; e.g., placed in an office). During the work on the offline system, the complete X-ray system can be used for production.
The inline - inspection of the wheel has no influence on the cycle time; it is working parallel to the mechanical positionning and image sampling. To get enough processing power, the hardware is using a Host-PC with several Slaves-PCs (see fig. 2).
The main Steps of the inspection are:
Step 1: Substracting the incoming image (see fig. 5) from a filtered image to receive a background (see fig. 6) image showing differences in material (like defects)
|Fig 5: Incoming Image.||Fig 6: Background Image.|
Step 2: Binarization - Distinguish noise from defects and reports for each defect the
|Fig 7: Image after Binarization.|
The Binarization can use a different classification for each ROI. The classification can be setup to fulfill standards of ASTM.
Step 3: Detection of large areas of missing material by comparing of the incoming image and reference image.
Step 4: Before giving the final result to the PLC, the AI will verify the defects with special algorithms.
With a system like AI its also possible to setup a mould recognition by X-ray.
The wheel-manufacturers install the X-ray system close to the casting process, because they want to sort out the rejects as soon as possible (to save costs in the following processes like machine processing) and to recognize and to solve casting-problems.
|Fig 8: Optimizing the casting process.|
If the actual tested images and statistic informations are displayed near the mould, the moulder can correct the quality of the wheels (see fig. 8).
An (very simplified) economical comparison of visual and a fully automatic inspection system with the following parameter
shows that theres a return of investment in less than one year (see fig. 9). There are additional financial advantages by optimizing the casting process with a system like AI.
|Fig 9: Costs for automatic and visual inspection.|
Comparing the fully automatic X-ray inspection of aluminium wheels with the visual
inspection, the main advantages of the automatic inspection are:
In the future, the fully automatic inspection will replace the visual inspection. The number of wheel customers (automotive industry) is increasing who demand automatic inspection in foundries.
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