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.
Prediction and Analysis of Deposition Efficiency of Plasma Spray Coating Using Artificial Intelligence Method
Modern industrial technologies call for the development of novel materials with improved surface properties, lower costs and environmentally suitable processes. Plasma spray coating process has become a subject of intense research which attempts to create functional layers on the surface is obviously the most economical way to provide high per- formance to machinery and industrial equipments. The present work aims at developing and studying the industrial wastes (Flay-ash, Quartz and illmenite composite mixture) as the coating material, which is to be deposited on Mild Steel and Copper substrates. To study and evaluate Coating deposition efficiency, artificial neural network analysis (ANN) technique is used. By this quality control technique, it is sufficient to describe approximation complex of in- ter-relationships of operating parameters in atmospheric plasma spray process. ANN technique helps in saving time and resources for experimental trials. The aim of this work is to outline a procedure for selecting an appropriate input vec- tors in ANN coating efficiency models, based on statistical pre-processing of the experimental data set. This methodology can provide deep understanding of various co-relationships across multiple scales of length and time, which could be essential for improvement of product and process performance. The deposition efficiency of coatings has a strong dependence on input power level, particle size of the feed material, powder feed rate and torch to substrate distance. ANN experimental results indicate that the projection network has good generalization capability to optimize the deposition efficiency, when an appropriate size of training set and network is utilized.
The effect of molybdenum addition on properties of iron aluminides
The effect of the addition of up to 10 pct molybdenum on several metallurgical properties of Fe-28Al (at. pct) to which 1 pct TiB2 was added for grain refinement has been studied. It was determined that the addition of molybdenum results in a decrease in grain size, an increase in the recrystallization temperature, and an increase in the DO3 to B2 ordering transformation temperature. The solubility limit of molybdenum in the matrix of the base alloy was found to be about 6 pct. At this concentration, another 1 pct is dissolved in the TiB2 precipitates. Tensile strengths were increased slightly by adding up to 2 pct Mo, but ductility decreased, even though grain sizes were reduced. The fracture mode in tension did not change with addition of molybdenum up to 2 pct.