Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM_2.5, PM₁₀, O₃, CO, SO₂, NO₂, CO₂) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R², RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

Hole quality in drilling is considered a precursor for reliable and secure component assembly, ensuring product integrity and functioning service life. This paper aims to evaluate the influence of the key process parameters on drilling performance. A series of drilling tests with new TiN-coated high speed steel (HSS) bits are performed, while thrust force and torque are measured with the aid of an in-house built force dynamometer. The effect of process mechanics on hole quality, e.g., dimensional accuracy, burr formation, surface finish, is evaluated in relation to drill-bit wear and chip formation mechanism. Experimental results indicate that the feedrate which dictates the uncut chip thickness and material removal rate is the most dominant factor, significantly impacting force and hole quality. For a given spindle speed range, maximum increase of axial force and torque is 44.94% and 47.65%, respectively, when feedrate increases from 0.04 mm/rev to 0.08 mm/rev. Stable, jerk-free cutting at feedrate of as low as 0.04 mm/rev is shown to result in hole dimensional error of less than 2%. A low feedrate along with high spindle speed may be preferred. The underlying tool wear mechanism and progression needs to be taken into account when drilling a large number of holes. The findings of the paper clearly signify the importance and choice of drilling parameters and provide guidelines for manufacturing industries to enhance a part’s dimensional integrity and productivity.

Non-conventional machining process for instance, wire electrical discharge machining (WEDM) of titanium alloys is gaining attention due to non-contact nature of this process. To deepen the understanding in this area, this study investigates surface generation, kerf width, discharge gap, material removal rate and wire degradation during WEDM of Ti6Al4V alloy. Pulse on time (4-10 μs), flushing pressure (7-18 MPa) and wire tension (800-1700 gf) were varied and resulting influences on output parameters were analysed. It was found that, machined surfaces consist of multi-layered recast layer with the presence of cracks, holes as well as traces of materials from electrode wire. The composition and roughness of the machined surface varies slightly with respect to machining condition without following any trend. In addition, deformation and morphology of deformed wire electrode after the WEDM process was also reported in this study.