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In this investigation, the Nickel (Ni) – Chromium (Cr) composite deposits was developed from conductive type nickel plating bath through electrodeposition technique. The Cr particles concentration in bath were taken in three different proportions such as 10 g/l,20 g/l and 30 g/l and got deposited on the Ni plate. The design of experiment technique, L27 orthogonal array of Taguchi method was employed for conducting experiments with different combination of parameters and its level. The parameters taken into consideration were current density level, pH range, bath temperature, Cr concentration in bath and stirrer speed. The experiment includes the measurement of micro hardness, area fraction of Cr, mass of deposit and coating thickness of coated samples with variable input parameters. The main effects of process parameters and their influences on the responses were analyzed. The outcomes shows that Cr concentration in bath and pH range are the most significant parameters for better enhanced responses measured. Microstructure examinations were conducted to analyze Cr deposition in prepared Ni plates using optical metallurgical microscope, scanning electron microscope (SEM) micro images and Energy Dispersive X-Ray (EDX) spectroscopy studies. Furthermore, the decision-making procedure was carried out for maximization of multi-performance measured responses using grey relational analysis (GRA). The optimized plating parameters levels are found to be 4 A/dm2 for Current density, 2.5 for pH, 45˚C for bath temperature, 30 g/l for bath concentration and 300 rpm for agitation speed to have the maximum micro hardness of 1037 HV, 370 mg of mass of deposit, 42% of Cr particle area fractions and 112 µm of coating thickness. Subsequently the obtained optimal combination of process parameters was validated through confirmation experiment. The results shows that the confirmation test responses are well associated with predicted response values on the Ni-Cr coatings. [ABSTRACT FROM AUTHOR]

A back propagation neural network model was developed for the prediction of bead geometry (depth of penetration, height and width) in gas metal arc welded (GMAW) stainless steel plates. The plates are preheated before welding which enhances the weldment properties by reducing the cooling rates in the weld metal. The penetration of the weld has been taken into consideration for accessing the performance of the weld. The neural model developed was based on the experimental data. The experiments were conducted by intriguing different combination of five levels of welding parameters such as welding gun angle, welding velocity, welding current, shielding gas flow rate and wire feed rate. The welding experiments were conducted on AISI 202 stainless steel plates. The output of the developed model is compared with the experimental data; it is observed that the percentage error is less than 4% and correlation coefficient is found to be 0.985. Regression equation was developed between the input process parameters and bead geometry for doing optimisation. Multiple objectives constituted with maximum depth of penetration, minimum bead width and height within the bound of range of parameters and the optimisation was done based on the genetic algorithm using 'C' language. [ABSTRACT FROM AUTHOR]

Selection of geometrical and machining parameters has great influence in machining performance. Predictive modeling can be used in optimization and control of process parameters. This study focuses on the optimization and sensitivity analysis of machining parameters, and fine-tuning requirements to obtain better machining performance. A statistical prediction model was developed in terms of tool geometrical parameters such as rake angle, nose radius, and machining parameters such as cutting speed, feed rate, and depth of cut. Central composite response surface methodology with five parameters and five levels was used to create a mathematical model, and the adequacy of the model was checked using analysis of variance. The experiments were conducted on aluminum Al 6351 with high-speed steel end mill cutter. Vibration in terms of acceleration amplitude during end milling was measured with two accelerometers—one in tool holder (channel I) and other in workpiece fixture (channel II) respectively. Optimizations of process parameters were performed using genetic algorithm. Sensitivity analysis was performed using developed equations to identify the parameter exerting most influence on vibration amplitude. [ABSTRACT FROM PUBLISHER]