Paul Slebodnick

Microstructural inhomogeneities and sea water corrosion in laser-deposited Ti–6Al–4V alloy matrix/carbide particulate composite surfaces

Metal matrix/carbide particulate composite surface layers were produced on Ti–6Al–4V alloy samples by injecting metal carbide particles into laser-melted surfaces followed by rapid solidification. Hard, wear-resistant surfaces were produced on a strong alloy which normally has poor wear resistance. The corrosion behaviour of the composite surface was evaluated after a months exposure to flowing sea water. A variety of solidification products was found in the laser-deposited surface layers, but corrosion was observed only in the carbide particulate phase in the WC-injected sample. No corrosion was observed in the TiC-injected sample nor in the Ti–6Al–4V base alloy. Corrosion in the WC-injected sample was related to the formation of a narrow interphase zone surrounding the particulate phase and a thin reaction zone on the surface of the particulate phase during solidification. The titanium-rich interphase zone formed a galvanic couple with the WC particulate. Crevice-type corrosion initiated at the interface between the two phases and proceeded into the particulate phase assisted by the reaction zone. Electrochemical test results revealed a high corrosion rate for the WC-injected sample and almost none for both the TiC-injected sample and the Ti–6A1–4V base alloy, confirming the microstructural observations.

Fusion of color, grayscale, and edge-detection algorithms for the accurate assessment of corrosion in shipboard tank and void imagery

Over the last several years, the Naval Research Laboratory has developed video based systems for inspecting tanks (ballast, potable water, fuel, etc.) and other voids on ships. Using these systems, approximately 15 to 30 images of the coated surfaces of the tank or void being inspected are collected. A corrosion detection algorithm analyzes the collected imagery. The corrosion detection algorithm output is the percent coatings damage in the tank being inspected. The corrosion detection algorithm uses four independent algorithms that each separately assesses the coatings damage in each analyzed image. The independent algorithm results from each image are fused with other available information to develop a single coatings damage value for each of the analyzed images. The damage values for all of the images analyzed are next aggregated in order to develop a single coatings damage value for the complete tank or void being inspected. The results from this Corrosion Detection Algorithm have been extensively compared to the results of human performed inspections over the last two years.

Effect of algorithm fusion and aggregation methodologies on the performance of a corrosion detection algorithm

Over the last several years, the Naval Research Laboratory has been developing corrosion detection algorithms for assessing coatings conditions in tank and voids on US Navy Ships. The corrosion detection algorithm is based on four independent algorithms; two edge detection algorithms, a color algorithm and a grayscale algorithm. Of these four algorithms, the color algorithm is the key algorithm and to some extent drives overall algorithm performance. The four independent algorithm results are fused with other features to first generate an image level assessment of coatings damage. The image level results are next aggregated across a tank or void image set to generate a single coatings damage value for the tank or void being inspected. The color algorithm, algorithm fusion methodology and aggregation algorithm components are key to the overall performance of the corrosion detection algorithm. This paper will describe modifications that have been made in these three algorithm components to increase the corrosion detection algorithm’s overall operating range, to improve the algorithm’s ability to assess low coatings damage and to improve the accuracy of coatings damage classification at both the individual image as well as at the whole tank level.

Multiple algorithm fusion in a corrosion detection algorithm used with shipboard tank and void inspection systems

Over the last several years, the Naval Research Laboratory has developed video based systems for inspecting tanks (ballast, potable water, fuel, etc.) and other voids on ships. Over this past year, we have extensively utilized the Insertable Stalk Inspection System (ISIS) to perform inspections of shipboard tanks and voids. This system collects between 15 and 30 images of the tank or void being inspected as well as a video archive of the complete inspection process. A corrosion detection algorithm analyzes the collected imagery. The corrosion detection algorithm output is the percent coatings damage in the tank being inspected. The corrosion detection algorithm consists of four independent algorithms that each separately assesses the coatings damage in each of the images that are analyzed. The algorithm results are fused to attain a single coatings damage value for each of the analyzed images. The damage values for each of the images are next aggregated in order to develop a single coatings damage value for the tank being inspected. This paper concentrates on the methods used to fuse the results from the four independent algorithms that assess corrosion damage at the individual image level as well as the methods used to aggregate the results from multiple images to attain a single coatings damage level. Results from both calibration tests and double blind testing are provided in the paper to demonstrate the advantages of the video inspection systems and the corrosion detection algorithm.

Generation and fusion of multiple image representations in automatic corrosion detection algorithms used with shipboard tank video inspection systems

Coatings damage in shipboard tanks is presently assessed using Certified Coatings Inspectors. Prior to a coatings inspector entering a tank, the tank must be emptied and certified gas free. These requirements combined with the limited number of certified coatings inspectors available at shipyards and Naval Bases significantly increases the cost and the logistical requirements associated with performing shipboard tank inspections. There is additionally significant variation in damage assessments made by different inspectors. To overcome these difficulties, the Naval Research Laboratory has developed two video inspection systems that obviate requirements for both certifying tanks gas free and for emptying the tank prior to performing an inspection. These systems also obviate requirements for inspector presence during tank inspections. The Naval Research Laboratory has also developed an automatic corrosion detection algorithm. The corrosion detection algorithm currently employs two independent algorithms that individually assess the tank coatings damage. The independent damage assessments are than fused to attain a single coatings damage value. In testing performed to date, it has been shown that the corrosion detection algorithm significantly reduces the effect of inspector-to-inspector variability and provides an accurate assessment of tank coatings damage. This in turn makes it significantly easier to prioritize ship maintenance.