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.
ANN Controlled Plasma Spray Process by using Industrial Waste
Plasma spray coating process has become a subject of intense research because it becomes an affordable solution for many industrial applications. The present work aims at developing and studying the industrial wastes (Flay-ash powder mixture) as the coating material, which is to be deposited on Mild Steel and Cupper substrates. To increase the coating deposition efficiency and to decrease the cost of coating process, one of the artificial intelligence (AI) method i.e. Artificial Neural Network (ANN) technique used. By this parameter optimization technique, it is sufficient to describe approximation complex inter-relations 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 vector in ANN coating efficiency models, based on statistical pre-processing of the data set. This methodology can provide deep understanding of various co-relationships across multiple parameter set, which could be essential for improvement of product and process performance. The aim of this article is to find optimum parameter for deposition efficiency. ANN experimental results indicate that the projection network has good generalization capability to optimize the deposition efficiency.
Slotted Waveguide Antenna With Two Radiation Nulls
A design of a linear array antenna having two radiation nulls and otherwise omnidirectional radiation pattern is reported. A new configuration of a waveguide broad wall longitudinal slot doublet with positive and negative offsets with respect to the waveguide center line is used to design the array. This type of doublet produces two radiation nulls. Each of the null directions can be shifted at least up to plusmn25deg by proper choice of slot offset. Resonance and radiation characteristics of the proposed doublet are also presented. Finally, one linear array antenna consisting of seven doublets has been fabricated. Measurements show that the 15 dB null widths are 14.2deg and the null depths are at least 24.8 dB. Maximum gain ripple in the omnidirectional region is 1.75 dB.