This experimental work investigates the emission, performance, and combustion study of Pongamia biodiesel fuel with the addition of titanium oxide (TiO₂) in four-stroke, single-cylinder, water-cooled, compression-ignition (CI) engine. The TiO₂ nanoparticle was included with Pongamia biodiesel (PBD100) fuel at the different PPM of 50 and 100. These experimental results proved that biodiesel with TiO₂ nanoparticle has improved combustion when compared to biodiesel fuel. The emissions of HC, the smoke of biodiesel with 100 ppm TiO₂ nanoparticle are lower than biodiesel fuel by 2.1%, 2.7% at full load. NO_x and CO emissions are decreased by 3.8% and 1.9%, respectively compared to biodiesel. As per the performance fronts, specific fuel consumption improved with improvement in brake thermal efficiency for Pongamia biodiesel with 50 and 100 ppm TiO₂ nanoparticle. The TiO₂ nanoparticle inclusion at different ppm significantly increases the peak pressure and the net heat release rate values for Pongamia biodiesel. Hence, the Pongamia biodiesel with TiO₂ nanoadditives is an appropriate substitute fuel for diesel engine.

In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before the actual machining process. It helps to avoid the actual machining cost and material cost. Experimental trails are planned based on Taguchi analysis, and L₂₇ orthogonal array was chosen. Speed, feed, and approach angle of drill were considered as controlling factors, and cutting force and surface roughness were considered as responses. The feed forward neural network (FFNN) was used to develop a predictive model. The prediction capability was validated with a predictive model developed by Taguchi analysis. Furthermore, ANOVA (analysis of variance) analysis was done to find out the most influence factor on the responses.