Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel.  Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Signal-to-noise (S/N), the analysis of variance (ANOVA) was employed to find the optimal levels and to analyze the effect of electrochemical machining parameters on Ra and MRR. The surface roughness of the workpiece was decreased with the increase in current values and electrolyte concentration while causing an increase in material removal rate. The ability of the independent values to predict the dependent values (R2) were 87.5% and 96.3% for mean surface roughness and material removal rate, respectively.

Abrasive flow machining (AFM) is gaining wide spread application finishing process on difficult to reach surfaces in aviation, automobiles, and tooling industry. A multiple regression model is proposed by using SPSS to simulate and predict the surface roughness, and material removal for different machining conditions in (AFM) on aluminum alloys. Based upon the experimental data of the effects of AFM process parameters, e.g., length of stroke, extrusion pressure, number of cycles, percentage of abrasive concentration, and abrasive grain size. The mathematical models for Ra, and material removal are established to investigate the influence of AFM parameters. Conformation test results verify the effectiveness of these models and optimal parametric combination within the considered range. The statistical model could predict about 96.1%, and 99.38% accuracy.

The growing deployment efforts of 5G networks globally has led to the acceleration of the businesses/services' digital transformation. This growth has led to the need for new communication technologies that will promote this transformation. 6G is being proposed as the set of technologies and architectures that will achieve this target. Among the main use cases that have emerged for 5G networks and will continue to play a pivotal role in 6G networks is that of Intelligent Transportation Systems (ITSs). With all the projected benefits of developing and deploying efficient and effective ITSs comes a group of unique challenges that need to be addressed. One prominent challenge is ITS orchestration due to the various supporting technologies and heterogeneous networks used to offer the desired ITS applications/services. To that end, this paper focuses on the ITS orchestration challenge in detail by highlighting the related previous works from the literature and listing the lessons learned from current ITS deployment orchestration efforts. It also presents multiple potential data-driven research opportunities in which paradigms such as reinforcement learning and federated learning can be deployed to offer effective and efficient ITS orchestration.

Experimental investigation and optimization of machining parameters in electrical discharge machining (EDM) in terms adding particles Nano-reinforced among the various mechanical processes, the process of manufacturing in electrical discharge machines is one of the most effective and cost-efficient manufacturing processes in the manufacture of stainless steel. It has been dealt with in this article investigate each of operating parameters such as peak current (Ip), pulse on time (Pon) and pulse off time(Poff), insulating liquid with Nano powder (AL2O3) in EDM compounds AISI 304. In the present research work, the influences of certain process parameters on surface roughness(Ra) and material removal rate(MRR) were investigated on stainless steel carried out with powder mixture with particles size average of [5 nm]. Operating parameters are taking into consideration three factors based on the Taguchi method. The results from this work will be useful for manufacturing engineers to select appropriate set of process parameters to machine stainless steel.

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