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The purpose of this paper is to investigate the thermo-mechanical behavior of clutch facing materials using image processing techniques. Infrared thermography and digital image correlation were used to analyze the mechanical behavior of clutch facing material under tensile loads. For specimens under tension loads, Infrared thermography clearly showed the thermo-elastic coupling as well as thermal dissipations due to the microcracking state of damaged clutch facing specimens. During an up to failure tensile test, localized thermal effects were observed at the macro-crack location before it appeared. Strain fields at the surface of specimens under tensile loadings were determined using the digital image correlation (DIC) technique. These experiments showed that the strain fields early became inhomogeneous. Strains concentrated in multiple localization zones which highlighted the role of the fibers in transmitting the internal forces.

Transient thermography non-destructive evaluation methods are being used in aerospace industry to inspect flaws and damages for various composite materials. The purpose of this paper is to establish a set of guidelines for testing Carbon Fiber Reinforced Panels (CFRP) panels using infrared thermography. These guidelines insure that the inspection process is efficient and effective. Samples with simulated defects were made and modeled using a finite element program. Heat will be applied to the models and the temperature profiles analyzed. Along with changing the heat and time, different post-processing techniques were used to improve the method in determining defects in the sample. Once this has been optimized, actual CFRP panels with the same simulated defects were experimentally tested using the conditions from the analytical model. The analytical and experimental data was compared to insure that the testing process has been optimized. A standardized process was developed for evaluating the CFRP panels using infrared thermography.

The purpose of this paper is to discuss the influence of Diamond–Like Carbon (DLC) coatings on the fatigue behavior of Spinal implant rod. DLC exists in seven different forms of amorphous carbon materials that display some of the unique properties of diamond. They are usually applied as coatings to other materials that could benefit from some of unique diamond properties. In this work, we compared bending fatigue behavior between Stainless Ti6Al4V and DLC Coating Spinal implant rod. The positive effect has been observed by fatigue tests carried out at ASTM Standard D790. Fatigue life and endurance limit of spinal implant rod have been remarkably increased by means of thin DLC coatings. The improvement of fatigue characteristics of spinal implant rod by DLC coatings has been ascribed to the sealing of external defects which retards crack initiation.

This research developed a reliable intelligent non-destructive evaluation (NDE) expert system for Carbon Fiber Reinforced Panels (CFRP) panels based on infrared thermography testing (IRT) and post processing by means of fuzzy expert system technique. Data features and NDE expert knowledge are seamlessly combined in the intelligent system to provide the best possible diagnosis of the potential defects and problems. As a result, this research help ensure CFRP panels’ integrity and reliability. Specimens with simulated defects were evaluated to demonstrate the usefulness of the intelligent IRT NDE expert system in NDE inspection. The testing data pattern corresponding to feature and quantification of defects were found. This fuzzy expert system not only eliminates human errors in defect detection but also functions as NDE experts. In addition, fuzzy expert system improves the defect detection by incorporating fuzzy expert rules to remove noises and to measure defect size more accurately. In the future, the expert system model could be continuously updated and modified to quantify the size and distribution of defects. The system developed here can be adapted and applied to build an intelligent NDE expert system for better quality control as well as automatic defect and porosity detection in CFRP production process.

Spiral concentrators have been widely used in coal preparation plants in Illinois and elsewhere to clean 1 mm ×150 μm particle size coal fraction. The major factors which have made spiral concentrator so popular include its low capital and operating cost, no chemical reagent or dense medium requirement, and the ease of operation/maintenance. Spirals are capable of providing excellent clean coal recovery although at a relatively high ash content. Like any other water-only separation process, spirals are also susceptible to continuously fluctuating feed quality and solids content in the feed slurry, which are quite common in a plant. The main objective of the project is to develop an inexpensive control system for spiral to automatically adjust the splitter position with fluctuating feed characteristics to maintain the desired effective separation specific gravity (density cut-point).

An input design system identification (IDSI) method is outlined in this chapter based on input/output data gathered from random excitation of a system so as to excite all modes. The data set is then used to compose a new set of more focused input data, from which the system is excited again and identified. In this chapter, the input design method is used for the system identification of an ultrasonic sensor pair (transmitter–receiver) so as to obtain an accurate model for it. This model is essential for the analysis of a 3D position estimation system that uses ultrasonic transducers. A single transmitter is attached to the point of interest, and its position is triangulated based on signals received at multiple receivers fixed in a 3D environment. An accurate model for the response of the ultrasonic receivers is essential in the eventual optimization of the entire system. This chapter only presents results for a single pair (called the system in this chapter), but the results will be applicable for the entire 3D system. Results are given that show the comparison of the actual output signal of the system and the output of the model obtained from the IDSI method as well as the identifiability indicator and an identified state-space model. It is shown that the IDSI improves system identification even in the presence of excessive noise.