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lated compromise between inspection quality and throughput. A typical expectation for ADR (low


Fig 4. Unique filters, such as the edge conversion filter, are used for image recognition, depending on the application.


the ADR system tend to be much more successful with the technology than those that view it as a simple plug-and-play solution. Different markets and applications


have a wide range of requirements, but these current ADR systems can typically detect casting flaw sizes down to 0.1 sq.mm with a minimal depth of approximately 3% of the material thickness at an average 2-5% false reject rate. Of course, these values are strongly dependent on the application. Size/geometry, complex- ity, surface roughness, and whether the casting is finished or unfinished all have an effect on the inspectability of a part.


Te detectable flaw size depends


on the focal spot size, detector resolution and magnification–not the software. Critical for the results (especially the false reject rate) is the material and the geometry of the inspection parts. It is important to note, without a part-specific appli- cation it is difficult to estimate the quality of the ADR. However, in every application the


overall goal is the same as manual operator interpretation: To have zero false accepts. In order to assure this with an automatic system, falsely rejecting some parts is all part of risk management. Once the system is proven to be equivalent to the approved methods for never accept- ing a bad part, the system can be tuned to keep the false rejects at a minimum or even zero in some cases. No matter what mode the ADR


60 | MODERN CASTING May 2016


software is operating in, the results will depend on: • Precise part fixing and/or manipulation.


• Part complexity and design, includ- ing surface roughness and produc- tion consistency.


• Optimal image quality. Specific and well-designed software


algorithms for image processing and defect recognition, as required by each application.


Maintaining software to account


for production changes such as tooling wear, material variances and casting differences.


As an example, a common speci-


fication for defects in cast aluminum automotive parts is 0.5 mm. Te image acquisition and evaluation process is optimized for a high throughput and low false reject rates. Tis is a calcu-


false reject rate and high throughput) for automotive aluminum castings is 4% or lower. Tis application highly depends on geometry, part configura- tion and especially surface rough- ness. To help with the inspection, the software is capable of recognizing regular structures. A regular structure is defined as a reproducible geometric structure within a certain tolerance. Tese structures are taught to be accepted by the ADR software. After optimizing the imaging, and teaching the regions of interest with the appro- priate filters, a false reject rate of even 2% is achievable in many applications. At the most basic level, image


processing for ADR involves several steps. All are equally important for the overall process. Acquiring the image is the first and most important step and is cru- cial for obtaining the best results. A technique previously used for film or even digital radiography might not be optimal for an ADR implementation. Selecting the correct imaging chain with the right calibration and spatial resolution is critical. Choosing the correct magnification and integration time will define the detectability of the smallest defects. All of the details regarding focal spot size, energy, detector type, pixel pitch, scintillator material, geometry, and integration time must be optimized for these types of critical inspections. Filtering the images is equally


important because it allows the soft- ware to take advantage of the 16-bit images and find the smallest details. Te filters are specific to the inspec- tion task(s) and are designed to detect specific features (Fig. 2). Many defects that are difficult or even unable to be seen by the human eye can be auto- matically identified. After filtering, the images are


further processed for visualization and classification purposes (Fig. 3). Filtering and processing also


Fig. 5. Typical flaw classification options


includes eliminating edge artifacts and pseudo-defects that cause false rejects. To avoid this, unique filters are applied depending on the part


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