Results and DiscussionIdentifying metal surface defects requires technologies that can

detect surface anomalies while maintaining the parts intact. We present a comparative analysis of three vision systems to predict defects on the surfaces of aluminum castings: a hyperspectral imaging system, thermal imager, and digital color camera have been used to inspect the surfaces. Hyperspectral imaging provides both spectral and spatial information. Each material produces specific spectral signatures which are also affected by surface texture. The thermal imager detects infrared radiation whereby hotspots can be investigated to identify possible trapped inclusions close to the surface, or other superficial defects. Finally, digital color images show apparent surface defects that can also be viewed with the naked eye. The surface defect locations are analyzed and predicted using the three systems, and verified by tensile testing. The final goal of the project is to determine the most effective vision technology to nondestructively detect defects.

Abstract

Introduction

Comparing Three Vision Systems for Metal Surface Defect DetectionShawn Robinson*, Ruby Mehrubeoglu**, Petru-Aurelian Simionescu**

Hyperspectral Optical Property Instrumentation (HOPI) LaboratorySchool of Engineering and Computing Sciences, Texas A&M University-Corpus Christi

*presenter, **mentor

Experimental Methods

Data Analysis Methods

Summary and Conclusion

Acknowledgement

References

Results and Discussion

Defect detection technologies have a wide range of variances and an even bigger range of applications. From X-rays to ultra sound, the most important goal of the technologies is to find the defects fast, accurately, and as inexpensive as possible. The biggest market for defect detection lies in the investigation of pipelines, aircraft wings, support beams, and roadways. In this experiment we will consider and compare 3 different vision technologies; Thermal, Hyperspectral and Digital Imagers. Thermal systems are most commonly used as night vision, or to identify moisture spots in housing structures, because of their acute ability to detect infrared wavelengths (heat), invisible to the human eye. This allows for the easy detection of hot spots, which are usually problem areas. Hyperspectral imaging systems are most commonly used in geographical scanning equipment. They can take images over a wide range of acute wavelengths and compile them together. They system computes a relative brightness for each wavelength for each pixel of data. Form this data you can deduce information like changes in elevation, change in texture, and changes in material. High definition digital Images allow for the complete capture of everything visible by the naked eye, with little to no distortion. This allows you to analyze color, shape, size, texture, and other obvious features accurately and make reasonable predictions about them.

Photo of bars A3 and A4 that have been through tensile testing and brokenFigure 2. Ground Truth

Digital Images -Lay each sample horizontally on the countertop.-Turn off overhead light and flash to reduce bright spots.-Keep the lens of the camera about a foot away from the sample for each measurement.-Take pictures of each side of each sample

Thermal Images Put samples in an oven and heat them up to a rage of 200-215 degrees F-After the samples reached their new equilibrium, take them out two at a time, quickly, so that excess heat doesn't escape the oven.-Put the hot samples on a sheet of black foam board the reduce noise cause by the hot samples exposure to external environment, causing background temperature change.-Take measurements of each side of each sample keeping the lens about 3 feet away from the samples.

Hyperspectral Images

-Lay one sample at a time on the stage and adjust till focused. Average width of each sample is about 13mm-Set system to reflection mode and set to scan in 330 micron increments.-Set scan length to 22mm and the integration time is 50ms. Adjust stage 0.5cm up when scanning sides.

Using the digital images we were able to visually identify the most significant defects in each sample. When looking for defects visually, you look for things like shrinkage, inclusions, impurities, crack chips and fissures. These types of defects act as stress risers, which means when a load is applied, they will affect that area most and cause breakage. On the thermal images the temperature rage was adjusted to a higher and more specific range to reduce noise. Aluminum has a high rate of thermal conductivity, which means it should be very easy to distribute heat throughout the sample. This makes identifying hot spot easier because they would become more apparent and obvious to it surrounding. Specific points were analyzed and picked as the hottest and most substantial hot spots on the samples.

Digital Images For sample A3, the prediction was dead on. The nick acted as a stress rise in the bar, kind like perforated tear on a bag of chip. However the prediction for A4 was incorrect, which leads the assumption that the defect was not visible or sub-surface.

Thermal Images Both of the thermal images were extremely accurate in its ability to predict the break point. The thermal images succeeded where the digital images had failed.

Hyperspectral images

These images were also to predict the break point accurately, although not as specific as the thermal images. Figure 5. a.(aeft) Dark area

background sample holder with Spectral profile of the region

Figure 5. b.(left) Pure metal region with minimal defect with spectral profile

Figure 5. c.(left) Suspect defect area with spectral profile of that region

The wavelength 880.223nm was selected cause it had a better average resolution. Then profiles were made in areas were the metal quality was exceptional, the background region of the sample holder, and a region where there were suspected defects. The profiles show a relative brightness to respective wavelength. Each of theses profiles were taken of an average of a 9x9 pixel area, to smoothen out the curve and reduce error while remaining specific the chosen area.

Digital images are a very good source for defect detection, but not good for prediction as to where it is going to break. They only accurate o the surface and leaves out internal data. These types of images are not very constant because they are highly subject to human error, due to factors like poor eyesight, misrepresentation, blurred or distorted images, and personal experience identifying defects.

Thermal Images are very accurate when it comes to identify defects and probable break points. It has the ability to class levels of risk based upon the temperature range. It requires little image processing, and does not depend on skill level or experience. The affected area is highlighted with a different color to precisely show the exact shape size and location of the defect/ break point. It is vey easy to ignore unwanted information and background noise.

Hyperspectral images have the highest accuracy when it comes to defect detection. They are not only able to show areas of defect, but can show areas of high quality as well. It is easy to ignore noise by changing the wavelength that is viewed. These images also have the ability to adjust the level of accuracy, depending a number of data collection procedures, as well as image processing. Although it is quite time consuming to collet data from hyperspectral system, there is a plethora of information you can deduce form it once acquired. Acquiring data this way is very sensitive and not flexible for various environments.

All three of these technologies are suitable for detecting defects. However this experiment suggest that thermal imaging is the best candidate out of the three.

Digital imaging is very good place to start identifying probable defect areas, but is subject to human error. Even though defects may be found there is no way to know which defect is most likely to cause failure other than assumptions and speculation.

Hyperspectral imaging is very costly and time consuming. Although you get more information with the possibility of higher accuracy, the equipment is too sensitive and not flexible enough to be deployed in different environment quickly. Also, image processing is time consuming and subject to human error.

Even though thermal imaging is more expensive than digital imaging, it is not nearly as costly as hyperspectral imaging. It can be easily deployed in various environments, with little data acquisition time and little image processing. It is exceptional in its ability to identify defects to an exact area with shape. Future work includes different image processing techniques, which may lead to better results from digital and hyperspectral imaging, including automated and manual processing, as well as different chart and graphing techniques.

This material is based upon work supported by the National Science Foundation und Grant No. 0960000

Figure 3.(above) On A3(left) a nick, and on A4(right) hairline fractures have been identified as stress risers, and possible break points

Results and Discussion

Figure 1. a. Digital camera, b. Flir Thermal Camera, c. Headwall Hyperspectral camera

Figure 4. a.(top) Thermal scan of the samples, b.(bottom) Temperature range adjusted to show hottest hot spots with point selected

M. Sharifzadeh, S. Alirezaee, R. Amirfattahi,  Detection of steel defect using the image processing algorithms, Edition of book, Isfahan, Iran: Proceedings ofthe 12 th IEEE International Multitopic Conference, 2008, p. 125-127.