Comparison of Machining Performances Using Multiple Regression Analysis and Group Method Data Handling Technique in Wire EDM of Stavax Material

AbstractWire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to estimation of machining performances using Multiple Regression Analysis (MRA) and Group Method Data Handling Technique (GMDH). Experimentation was performed as per Taguchi's L’16 orthogonal array for Stavax (modified AISI 420 steel) material. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18mm was used as an electrode. Four responses namely accuracy, surface roughness, volumetric material removal rate and electrode wear (EW) have been considered for each experiment. Estimation and comparison of responses was carried out using MRA and GMDH

EVALUATION OF FRACTURE TOUGHNESS BEHAVIOUR OF GLASS-BANANA FIBER REINFORCED EPOXY HYBRIDCOMPOSITES

The present work deal with fabrication and investigation of fracture toughness of banana reinforced with glass fiber as natural hybrid composite. Composites of different combinations with varied fiber content were prepared using hand lay-up technique using L-12 epoxyresin and K-6 hardener as reinforcing materials. Banana fiber with 15, 20, 25 and 30% were hybridized with 20, 15, 10 and 5% of glass fiber to form composites and compared with non-hybrid 35% glass and banana fiber composites. The fracture toughness (KIC) was investigated according to ASTM D5045 by single edge notch bending (SENB) technique, with SEM image confirmation. The non-hybridized 35% banana fiber showed highest KICvalue and 35% glass fiber showed the least. Whereas, the hybrid composite with fiber volume of 20% banana and 15% glass showed higher KICvalue when compared with other hybrid fractions. The results thus obtained signified the fracture toughness got improved in banana-glass hybrid composite with increased glass fiber content from 5%-15%, thus acting as a positive reinforcement in providing extra strength and smooth surface finish to the composite and at the same time the banana fiber imparted elasticity to the composite

Prediction of Responses for Simarouba Biodiesel based CRDI Engine using General Regression Neural Network

The evaluation of performance and emission of Common Rail Direct Injection (CRDI) engine fuelled by various biodiesel at different operating conditions is time consuming and expensive. This can be overcome by using prediction techniques like GRNN. The GRNN model is developed using ‘newgrnn’ function in Matlab R2019b software to predict the performance and emission responses of CRDI engine for simarouba biodiesel. A total of 27 experimental dataset of each biodiesel is used for development of model. Out of 27 experimental dataset, 21 datasets are selected randomly for training the model. The remaining 6 datasets are utilized for testing the GRNN model. In this study, 20 different values of spread parameters within the range 0.05 to 1 with step increment of 0.05 are chosen. As a result, 20 simulations are performed and the best predicted results are chosen based on least mean error. The optimum spread parameter for simarouba, pongamia and composite biodiesel GRNN model was found to be 0.1, 0.1 and 0.05 respectively. The Root Mean Square Error (RMSE) values of different responses are found to be acceptable. The results indicated that GRNN model for the prediction of engine responses yields good correlation with experimental values and are acceptable for new predictions