ONO Bunji Kyushu University, Faculty of Engineering, Research Associate, 工学部, 助手 (60224276)
GONDO Seigo Kyushu University, Faculty of Engineering, Research Associate, 工学部, 助手 (50037975)
SUGIMURA Joichi Kyushu University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (20187660)
|Budget Amount *help
¥10,200,000 (Direct Cost : ¥10,200,000)
Fiscal Year 1996 : ¥3,000,000 (Direct Cost : ¥3,000,000)
Fiscal Year 1995 : ¥700,000 (Direct Cost : ¥700,000)
Fiscal Year 1994 : ¥6,500,000 (Direct Cost : ¥6,500,000)
This project was aimed at developing a system which predicts conditions of sliding surfaces in machinery from wear debris contained in sampled oils. Techniques for analyzing microscopic images of wear debris with computers, and association of debris features with sliding conditions using artificial neural networks were established. The system was applied and tested with lubricated wear experiments of steels, and also applied to gear tests and some practical machines.
A computer controlled image analyzer was developed which not only captures and analyzes images on the microscope but is able to obtain images automatically. It can also conduct three-dimentional shape measurement of large debris. Several descriptors of shape and surface features of particles were introduced, including representative diameter, elongation, roundness and modified roundness, reflectivity, thickness, Fourier descriptors and sueface texture parameters. The optimum sample size for averaging there parameters was pr
oposed. Fourier descriptors were found to be less susceptible than other shape parameters to errors caused by image digitization.
Wear debris were shown to have different morphological and surface features depending on the stages of sliding, loads, sliding speeds, oils and additives, and sliding materials. Correlations of the debris features and the sliding conditions were analyzed and represented with artificial neural networks. The back-propagation net, Hopfield net, and Kohonen self-organizing map were introduced, among which the back-propagation nets were extensively used. It was shown that the neural nets learned debris features and associated conditions, and predicted conditions the debris came from as well as the coefficient of friction with only inputs of debris parameters. The system proved to be capable of detecting, from wear debris characteristics, changes from initial wear to steady-state wear, and further to flaking or scoring in gear tests.