Super alloys offer excellent mechanical and chemical properties at elevated temperatures that make them an attractive choice for aerospace, automotive and chemical processing, and marine applications. These alloys are, however, difficult to machine due to their high strength at elevated temperatures, low thermal conductivity and work hardening. In this study, micro milling of Inconel 600 super alloy has been carried out and the effects of the key input parameters (cutting speed, feed rate, depth of cut) on response parameters (burr formation, surface roughness and tool wear), under various cooling conditions (dry, wet and cryogenic), have been analyzed. High speed micro milling (range up to 80,000 RPM) was carried out, while keeping the feed rate values below and above the cutting edge radius. The Taguchi design of experiments was used during this study. The results have been analyzed using SEM and 3D optical microscopy. Analysis of Variance (ANOVA) revealed that the best surface roughness values can be achieved under cryogenic machining condition with an overall contribution ratio of 28.69%. It was also revealed that cryogenic cooling resulted in the highest tool life with the contribution ratio of cooling conditions at 26.52%.

This research investigates the machinability of Inconel 718 under conventional machining speeds using three different tool coatings in comparison with uncoated tool during milling operation. Cutting speed, feed rate and depth of cut were selected as variable machining parameters to analyze output responses including surface roughness, burr formation and tool wear. It was found that uncoated and AlTiN coated tools resulted in lower tool wear than nACo and TiSiN coated tools. On the other hand, TiSiN coated tools resulted in highest surface roughness and burr formation. Among the three machining parameters, feed was identified as the most influential parameter affecting burr formation. Grey relational analysis identified the most optimal experimental run with a speed of 14 m/min, feed of 1 μm/tooth, and depth of cut of 70 μm using an AlTiN coated tool. ANOVA of the regression model identified the tool coating parameter as most effective, with a contribution ratio of 41.64%, whereas cutting speed and depth of cut were found to have contribution ratios of 18.82% and 8.10%, respectively. Experimental run at response surface optimized conditions resulted in reduced surface roughness and tool wear by 18% and 20%, respectively.

In this work, sugarcane vinasse combined with organic waste (food and wasted tea) was demonstrated to be an excellent source of biomethane synthesis from carbon-rich biowaste. The discarded tea trash might be successfully used to generate bioenergy. The uncertainties and costs associated with experimental testing were recommended to be decreased by the effective use of contemporary machine learning methods such as Gaussian process regression. The training hyperparameters are crucial in the construction of a robust ML-based model. To make the process autoregressive, the training hyperparameters were fine-tuned by employing the Bayesian approach. The value of R² was found to be greater during the model test phase by 0.72%, assisting in the avoidance of model overtraining. The mean squared error was 36.243 during the model training phase and 21.145 during the model testing phase. The mean absolute percentage error was found to be under 0.1%, which decreased to 0.085% throughout the model’s testing phase. The research demonstrated that a combination of wasted tea trash, sugarcane vinasse and food waste may be a viable source for biomethane generation. The contemporary methodology of the Bayesian approach for hyperparameters tuning for Gaussian process regression is an efficient method of model prediction despite the low correlation across data columns. It is possible to enhance the sustainability paradigm in the direction of energy security via the efficient usage of food and agroforestry waste.