Desirability Approach Of Response Surface Methodology Research Articles

Comparative Analysis of Grey Relational Analysis Integrated with the Principal Component Analysis and Analytic Hierarchy Process for Multiobjective Optimization of Inclined Laser Percussion Drilling in Carbon Fiber Reinforced Composites

Multiobjective optimization (MOO) helps to achieve simultaneous improvement of more than one output characteristic in machining processes where complex interaction between the input parameter exists. This study focuses on the comparative analysis of design of experiment (DoE)-based grey relational analysis (GRA) combined with principal component analysis (PCA) and analytic hierarchy process (AHP). Experiments were conducted with millisecond (ms) duration pulsed Nd: YAG laser using the Box–Behnken design (BBD) approach of the response surface methodology (RSM) at three different levels of input parameters. The output parameters, i.e., hole circularity at top (HCT), hole circularity at bottom (HCB), and hole taper (HT), were determined for various input parameters like pulse current (I), pulse width ([Formula: see text]), gas pressure ([Formula: see text]), workpiece thickness ([Formula: see text]), and incidence angle ([Formula: see text]) during laser percussion inclined hole drilling (LPIHD) in the carbon fiber reinforced polymer (CFRP) of three different thickness, i.e., 1[Formula: see text]mm, 3[Formula: see text]mm, and 5[Formula: see text]mm at incidence angles of 0, 10, and 20 degrees. Multiobjective function based on RSM has been developed for GRA-PCA and GRA-AHP and further optimizations were performed using the desirability approach of RSM. The analysis revealed that the angle of incidence is the most significant factor for controlling the output parameters. Interaction of pulse current and thickness ([Formula: see text]) has a major impact on output responses. The GRA-PCA approach gives the average improvement of 2%, 9%, and 37%, respectively, for HCT, HCB, and HT, whereas in the case of GRA-AHP, the corresponding improvements are only 1%, 6%, and 11%. Therefore, the GRA-PCA approach is a more effective tool for the MOO of LPIHD in CFRP.

Experimental investigation of sliding wear behaviour of boron carbide and mica reinforced aluminium alloy hybrid metal matrix composites using Box-Behnken design

Abstract In this work, wear performance of hybrid aluminium metal matrix composite (AMMC) was studied owing to the influence of mica addition on dry sliding. Technique of Stir casting is used to prepare the hybrid composite with AA6061 as matrix phase, 10% boron carbide (B4C) as primary reinforcement and varying weight fractions of mica (3%, 4% and 5%). Box-Behnken design of Response surface methodology (RSM) is used to analyze the experiments. Pin-on-Disc (PoD) equipment is used to carry out the work for varying sliding speeds, axial load and mica percentage, at room temperature. Coefficient of Friction (CoF) and Friction Force (FF) was evaluated and optimized using desirability approach of RSM. For CoF, the most significant parameter is rotational speed, and for FF it is axial load. Scanning Electron Microscope (SEM) images of worn surface shows grove formation and roughness running in parallel with the sliding direction and abrasive wear mechanism is observed. Optimum condition arrived are; 3% reinforcement of mica, 695 rpm of rotational speed, 0.5 kgf of axial load and 3 min of sliding time with a CoF of 0.207 and FF of 5.47 N.

Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology

This paper studies the use of RSM (response surface methodology) to optimize the performance parameters and exhaust emissions of a SI (spark ignition) engine which operates with ethanol–gasoline blends of 5%, 7.5%, 10%, 12.5% and 15% called E5, E7.5, E10, E12.5 and E15. In the experiments, the engine ran at various speeds for each test fuel and 45 different conditions were constructed. In comparison with gasoline fuel, the brake power, engine torque, increased using ethanol blends, and the bsfc (brake specific fuel consumption) decreased as well. Moreover, the concentration of CO and HC in the exhaust pipe decreased by introducing ethanol blends, but CO2 and NOx emissions increased. Optimization of independent variables was performed using the desirability approach of the RSM (response surface methodology) with the goal of minimizing emissions and maximizing of performance parameters. The experiments were designed using a statistical tool known as DoE (design of experiments) based on RSM. Engine-operating parameters were optimized using the Desirability approach of RSM. The performance parameters for different biofuel–gasoline blends were found close to gasoline, and emission characteristics of the engine improved significantly. An engine speed of 3000 rpm and a blend of 10% bioethanol and 90% gasoline (E10) were found to be optimal values. The results of this study revealed that at optimal input parameters, the values of the brake power, Torque, bsfc, and CO, CO2, HC, NOx were found to be 35.26 (kW), 103.66 (Nm), 0.25 (kg/kW hr), 3.5 (%Vol.), 12.8 (%Vol.), 136.6 (ppm) and 1300 (ppm) respectively.