NEURAL NETWORK ANALYSIS FOR DEPOSITION OF NICKEL-ALUMINIDE COATINGS ON STEEL BY PLASMA SPRAYING
Nickel-aluminides are used in thermal spray applications mostly as bond-coat materials, where their function is two-fold: to minimize the thermo-mechanical stresses at the substrate-coating interface arising out of thermal expansion mismatch of the metal substrate and ceramic top layer and secondly to promote coating adhesion. This paper describes the deposition of nickel aluminide coatings on mild steel substrates. A computational technique (ANN analysis) is used to predict the rate of coating deposition under various operational conditions. Prediction of deposition efficiency is significant as it gives an idea about the effectiveness of the spraying technique as well as about the coatability of the material under investigation. This technique involves database training to predict property parameter evolutions in process having large number of interdependent variables such as in the case of plasma spray coating deposition. This paper presents the database construction, implementation protocol and also the set of predicted results related to the deposition efficiency of nickel-aluminide coating. It is seen that the deposition rate depends largely on the plasma arc current, arc voltage etc. The neural network analysis gives an oversight on the parameter inter-dependencies and of the effect of the individual process variable on the efficiency of coating deposition.
Porosity Analysis of Plasma Sprayed Coating by Application of Soft Computing
The present piece of work describes the industrial wastes and low grade ores (fly ash + quartz + ilmenite, as the coating material), deposited on mild steel substrates. In many cases it is found that porosity is an important factor on the coating surface. Knowledge about the extent of these porosity imperfections is critical since they influence a wide range of spray coated properties and behaviors. To decrease the porosity by optimizing necessary operating parameters, artificial neural network (ANN) technique is used. The aim of this investigation is to find out appropriate input vectors in ANN model. ANN experimental results indicate that the projection network has good generalization capability to optimize the porosity.
A device for uranium series leaching from glass fiber in HEPA filter
For the disposal of a high efficiency particulate air (HEPA) glass filter into the environment, the glass fiber should be leached to lower its radioactive concentration to the clearance level. To derive an optimum method for the removal of uranium series from a HEPA glass fiber, five methods were applied in this study. That is, chemical leaching by a 4.0 M HNO3–0.1 M Ce(IV) solution, chemical leaching by a 5 wt% NaOH solution, chemical leaching by a 0.5 M H2O2–1.0 M Na2CO3 solution, chemical consecutive chemical leaching by a 4.0 M HNO3 solution, and repeated chemical leaching by a 4.0 M HNO3 solution were used to remove the uranium series. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after leaching for 5 h by the 4.0 M HNO3–0.1 M Ce(IV) solution were 2.1, 0.3, 1.1, and 1.2 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after leaching for 36 h by 4.0 M HNO3–0.1 M Ce(IV) solution were 76.9, 3.4, 63.7, and 71.9 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after leaching for 8 h by a 0.5 M H2O2–1.0 M Na2CO3 solution were 8.9, 0.0, 1.91, and 6.4 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after consecutive leaching for 8 h by the 4.0 M HNO3 solution were 2.08, 0.12, 1.55, and 2.0 Bq/g. The residual radioactivity concentrations of 238U, 235U, 226Ra, and 234Th in glass after three repetitions of leaching for 3 h by the 4.0 M HNO3 solution were 0.02, 0.02, 0.29, and 0.26 Bq/g. Meanwhile, the removal efficiencies of 238U, 235U, 226Ra, and 234Th from the waste solution after its precipitation–filtration treatment with NaOH and alum for reuse of the 4.0 M HNO3 waste solution were 100, 100, 93.3, and 100%.