Nondestructive Testing and Evaluation Research Laboratory

NONDESTRUCTIVE TESTING AND EVALUATION RESEARCH LABORATORY

University of Illinois at Urbana Champaign

Department of General Engineering

104 South Mathews

Urbana, Illinois 61801

Telephone: 217/333-1228

Fax: 217/244-5705

WWW:http://www.ge.uiuc.edu/facilities/ndt.html

Henrique L.M. dos Reis

Currently, the Nondestructive Testing and Evaluation (NDT&E) Research Laboratory, under the direction of Professor Henrique Reis, supports research work in the following areas: ultrasonics (immersion, air-coupled, and water-squirt), laser-driven-ultrasonics and interferometry, acoustic emission, acousto-ultrasonics, impact-echo, microfocus radioscopy, ultrasonic and x-ray computed tomography, electromagnetics, eddy currents, and holography. The main research areas are in the development of advanced sensors, on-line process control and materials characterization, life-cycle management of materials and structures, and artificial intelligence towards application in signal analysis and imaging processing. Special emphasis is placed on the development and optimization of those nondestructive evaluation techniques which lend themselves to intelligent manufacturing and construction processes; evaluation/characterization of advanced materials and structures such as polymer and ceramic composites, concrete, and wood and wood composites; non-contact evaluation of materials and structures in unique environments, such as high temperature and radiation; and advanced sensors for industry application. Research is aimed at a systems approach in the field of nondestructive testing and evaluation, focusing on real engineering problems in both real-time process monitoring and control and life cycle management of components which arise from a wide spectrum of applications.

Although traditionally nondestructive techniques have been used almost exclusively for detection of macroscopic defects (mostly cracks) in structural components after they have been in service, it has become increasingly evident that it is both practical and cost effective to expand the role of nondestructive evaluation to include all aspects of materials production and application and to introduce it much earlier in the manufacturing and construction cycle. In this NDT&E Laboratory, efforts are being directed at developing and perfecting techniques which are capable of monitoring and controlling materials production processes, manufacturing and construction processes, and the amount and rate of degradation during the materials in-service life. Using advanced sensors, along with signal/data processing techniques, information on the processing conditions and the properties and characteristics of the materials being processed can be continuously generated. Real-time process monitoring for more effective and efficient real-time process control and improved product quality, safety, and reliability becomes then a practical reality.

Current projects at the Nondestructive Testing and Evaluation (NDT&E) Laboratory include: NDT&E of adhesive bond strength in safety glass laminates (i.e., windshield), NDT&E of protecting coatings, cold-heading machine condition monitoring, NDT&E of airport pavements, and inspection of new and used tire casings before retreading. Along with remote sensing, neural networks have been successfully used during signal processing using multilayer perceptron networks for real-time monitoring and other applications. Neural networks have the advantage of reducing operator dependence and subjectivity while making real-time, process monitoring a reality in cases where only statistical of-line test sampling was feasible. Clearly, many other future applications will benefit from the use of neural networks and it is expected that their use will continue to increase to meet the challenges of future research projects. For example, ultrasonic and x-ray computed tomography, liquid penetrant and magnetic particle inspection all require image processing thus providing excellent opportunities for future application of neural networks.

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