John McNamara, professor of civil engineering at NMSU, has received a $40,000 award from Sandia National Laboratories to develop methods to evaluate adhesives used in automotive metal joints.
The U.S. Federal Aviation Administration (FAA) Airworthiness Assurance Nondestructive Inspection Validation Center (AANC), operated by Sandia National Laboratories, provides the FAA, U.S. Department of Defense and U.S. Department of Energy with independent and quantitative evaluations of new and enhanced inspection techniques. Sandia’s primary mission is to ensure the United States nuclear arsenal is safe, secure and reliable. As a result, Sandia has extensive inspection, testing and manufacturing capabilities and expertise.
Adhesive joining is widely used in automotive production today. Currently, adhesives are primarily used for increased stiffness, noise and vibration dampening and sealing. The adhesive is usually used in combination with resistance spot welds or rivets. As adhesives are added to improve crash performance, the quality of these bonds becomes critical. At present, the quality control of adhesive joints relies on the robust control of the adhesive preparation and its application. However, there is no method available to test the overall quality of the joints other than destructive testing.
The main objective of this project is to develop ultrasonic methodologies and data analysis algorithms to identify characteristics of bonded joints, in support of the ongoing AANC project entitled “Nondestructive Inspection of Adhesive Bonds in Automotive Metal/Metal Joints.” This will be accomplished by identifying th e most promising ultrasonic and data processing techniques for adhesive bond inspection and characterization. Followed by an investigation of the possibility of using contact ultrasonics, air-coupled ultrasonics, or laser generated ultrasonics in adhesive bond inspection. The ultrasonic wave propagation parameters best suited and most sensitive to adhesive bond inspection will be determined. Structural/material models capable of representing experimentally determined bond characteristics, and predicting future behavior will be designed. The most appropriate signal processing algorithm for extracting the maximum amount of information from each ultrasonic inspection will be determined. The best pattern recognition algorithm to automatically process ultrasonic inspection data and provide recommendations regarding the characteristics of the adhesive bond will be determined. Finally, laboratory results with known bond specimens will be compared to results from unknown bond specimens to evaluate the robustness of the developed methodologies.