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.
Mathematical and Neural Network models for prediction of wear of mild steel coated with Inconel 718 - A Comparative Study
Many wear models can be found in the literature to predict the type and amount of wear but there is no general wear model which can be adopted for all wear problems due to the complex and dynamic nature of the phenomenon. In the present work, an attempt has been made to study the influence of wear parameters like applied load, speed and sliding distance on the dry sliding wear of mild steel coated with Inconel 718 using pin on disc apparatus and build Mathematical and Neural Network models. A plan of experiments based on Taguchi's orthogonal array was developed to acquire data in a controlled way for different sliding distances, under different loads, and different speeds. The experimental results were used to develop mathematical model using MINITAB 13 and ANN model using MATLAB7 successfully. The validity of the modeling approaches is demonstrated by comparing the predicted results with experimental results. The predictions of these models show a good agreement with experimental values.
"Flying Triangulation" - a new optical 3D sensor enabling the acquisition of surfaces by freehand motion
In order to obtain complete 3D surface information of an object it is often necessary to acquire many partial 3D views. For this purpose, the sensor has to be moved (e.g. for the acquisition of sculptures or rooms). Further, in many cases a motion of the object relative to the sensor is unintended but inevitable (e.g. for medical applications such as intraoral measurements of teeth). Nearly all existing optical 3D sensors either lack an easy handling or require a standstill during the acquisition of a single partial 3D view, since usually multiple camera shots have to be taken for this purpose. This makes such measurements elaborate or even impossible.