KCG College of Technology

About: KCG College of Technology is a based out in . It is known for research contribution in the topics: Adsorption & Diesel fuel. The organization has 427 authors who have published 381 publications receiving 2193 citations.

Investigation of indentation damage resistance on normal and inclined plane of glass/epoxy composite laminates using acoustic emission monitoring

Abstract: This work relates to the investigation of indentation induced damage resistance in glass fiber reinforced polymer composite laminates under normal and inclined planes. Uni-directional [0] and cross...

Modelling and control of an anfis temperature controller for plastic extrusion process

Abstract: This paper develops a ANFIS controller design method for temperature control in plastic extrusion system. The plastic extrusion process control system uses first order transfer function. Plastic extrusion system is generally nonlinear and the temperature of the plastic extrusion system may vary over a wide range subjected to various disturbances. The plastic extrusion system compresses of couple effects, long delay time and large time constants. The system designed with three different control techniques to control temperature at different set point changes and as well as to control sudden input disturbances. To design a controller it must be first tuned to the system. The tuning synchronizes the controller to the controlled variable and make the process to work at its desired operating condition. The Software incorporates LabVIEW graphical programming language and Matlab toolbox to design temperature control in plastic extrusion system. In this research the control methods are simulated using simulink and results obtained through LabVIEW. Relatively ANFIS controller gives best performance than Fuzzy logic and PID controller. The results indicate that the proposed algorithm significantly improves the performance of the temperature control in plastic extrusion system.

Influence of different cooling methods on drill temperature in drilling GFRP

Abstract: Drilling of composite materials is difficult to carry out due to their anisotropic and non-homogenous structure and the high abrasiveness of their reinforced constituent. This results in high temperature during drilling, leading to rapid wear development in the cutting tool. The present work investigates the drilling of glass fibre-reinforced polymer composites with TiN/TiAlN-coated carbide drills on a computer numerical control (CNC) lathe for different cooling conditions. The objective of this work is to study the influence of different cooling conditions on drill bit temperature and drill bit wear. The signal to noise (S/N) ratio and analysis of variance (ANOVA) were employed to analyse the effect of cooling condition on drilling parameters. A linear polynomial model was developed using multiple regression analysis between drilling parameters and cooling condition with the drill bit temperature to represent the fitness characteristics. The workpiece made of GFRP is machined, and temperature is monitored during dry drilling, internal coolant and external coolant. The temperature of drill bit was measured by PFA Teflon-coated K (Chromega-Alomega) type thermocouples. From the analysis, it is evident that a linear multiple regression models developed to relate the cutting temperature with drilling parameters are with a minimum error of ±7 %. Internal coolant method reduces the average temperature by 76 % compared with dry drilling and 66 % compared with external coolant method. The reduction in tool temperature reduces the flank wear and increases the tool life by 43.75 % compared with dry drilling and 25 % compared with external coolant method.

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

Abstract: The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current. In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented. A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault. A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage. The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor. The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture. The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.