The plasma nutriding of tool and bearing steels

There is some evidence to suggest that nitriding of alloy steels, in particular high speed tool steels, under carefully controlled conditions might sharply increase rolling contact fatigue resistance. However, the subsurface shear stresses developed in aerospace bearing applications tend to occur at depths greater than the usual case depths currently produced by nitriding. Additionally, case development must be limited with certain materials due to case spalling and may not always be sufficient to achieve the current theoretical depths necessary to ensure that peak stresses occur within the case. It was the aim of' this work to establish suitable to overcome this problem by plasma nitriding. To assist this development a study has been made of prior hardening treatment, case development, residual stress and case cracking tendency. M2 in the underhardened, undertempered and fully hardened and tempered conditions all responded similarly to plasma nitriding - maximum surface hardening being achieved by plasma nitriding at 450°C. Case development varied linearly with increasing treatment temperature and also with the square root of the treatment time. Maximum surface hardness of M5O and Tl steels was achieved by plasma nitriding in 15% nitrogen/85% hydrogen and varied logarithmically with atmosphere nitrogen content. The case-cracking contact stress varied linearly with nitriding temperature for M2. Tl and M5O supported higher stresses after nitriding in low nitrogen plasma atmospheres. Unidirectional bending fatigue of M2 has been improved up to three times the strength of the fully hardened and tempered condition by plasma nitriding for 16hrs at 400°C. Fatigue strengths of Tl and M5O have been improved by up to 30% by plasma nitriding for 16hrs at 450°C in a 75% hydrogen/25% nitrogen atmosphere.

Predicting and diagnosing faults in wastewater treatment process by Bayesian Belief Networks

Diagnosing faults in wastewater treatment, like diagnosis of most problems, requires bi-directional plausible reasoning. This means that both predictive (from causes to symptoms) and diagnostic (from symptoms to causes) inferences have to be made, depending on the evidence available, in reasoning for the final diagnosis. The use of computer technology for the purpose of diagnosing faults in the wastewater process has been explored, and a rule-based expert system was initiated. It was found that such an approach has serious limitations in its ability to reason bi-directionally, which makes it unsuitable for diagnosing tasks under the conditions of uncertainty. The probabilistic approach known as Bayesian Belief Networks (BBNS) was then critically reviewed, and was found to be well-suited for diagnosis under uncertainty. The theory and application of BBNs are outlined. A full-scale BBN for the diagnosis of faults in a wastewater treatment plant based on the activated sludge system has been developed in this research. Results from the BBN show good agreement with the predictions of wastewater experts. It can be concluded that the BBNs are far superior to rule-based systems based on certainty factors in their ability to diagnose faults and predict systems in complex operating systems having inherently uncertain behaviour.