This paper deals with the machinability of nickel-based superalloys using abrasive water jet machining process. The machining studies were carried out with three different parameters such as water jet pressure, traverse speed of jet nozzle, and standoff distance at three different levels. The performances of the process parameters are evaluated by measuring difference in kerf width, kerf wall inclination, and material removal rate (MRR). Further, the surface morphology and material removal mechanisms are analyzed through scanning electron microscope (SEM) images. It is found that water jet pressure is the most influencing factor related to surface morphology and surface quality.

The filler materials are reinforced along with natural fibers in the composite to improve the quality and property of the component materials based on the requirements and its applications. In this paper, hybrid natural fiber composites were developed with and without filler materials as reinforcement. The developed hybrid natural fiber composites are machined using abrasive water jet cutting process with three different cutting parameters. The influences of cutting parameters are evaluated with respect to the kerf wall inclination, material removal rate, and surface roughness. The surface morphology was also studied to infer the basic mechanism involved during composite machining. The hybrid fiber composite with filler has proved that it can produce good engineering component without delamination and fiber pullouts during machining.

In Software Quality Assurance (SQA), predicting defect-prone software modules is essential for ensuring software reliability and consistency. This task is commonly achieved through Machine Learning (ML) techniques, but improving model performance typically incurs significant computational costs. These high computational costs and uncertain payoffs make most Software engineering researchers reluctant to optimize ML models. This creates a need for novel techniques that can achieve near optimal performance of hyperparameter settings while maintaining the computational efficiency of default settings. To address this, we employed five ML models, Decision Tree, Ranger, Random Forest, Support Vector Machine, and k-nearest Neighbors and optimized their parameters using the random search technique. Our experiments covered six diverse Software Fault Prediction (SFP) datasets, encompassing a wide range of software features, application domains, and defect patterns, to evaluate the approach’s generalizability and effectiveness.. Moreover, the Permutation Feature Importance (PFI)-based model-agnostic method was employed to identify the top ten features critical for model accuracy and efficiency. These selected features were used to retrain the ML models without hyperparameter (default settings) to determine whether similar performance could be achieved at low computational cost. The results show an average accuracy improvement of 77.39% and a 92.02% reduction in computational cost. The most important case attained a 99.25% accuracy improvement and a 96.77% cost reduction. Such results clearly show that the PFI-based feature selection is capable of high performance at a fraction of computational cost, offering an efficient solution for software engineers to optimize ML models.

The efficiency of industry depends upon the working conditions of the equipment and components used in the industrial process. The biggest problems faced by the industries are the problems of erosion and corrosion. The harmful effects of corrosion will lead to material loss, which results from the degradation of the equipment. The degradation of the equipment will cause the breakdown of the plant; moreover, it is a threat to the safety of people, and also from the point of conservation, it can cause the exploitation of available resources. The cost of replacing equipment increases the expense, and it can also result in the temporary shutdown of the plant. The protection of surfaces from the adverse effects of corrosion and erosion-corrosion is a matter of great concern in most industrial applications. Advancements in technology provides a wide range of techniques to overcome adverse conditions. The selection of appropriate technology must be from the viewpoint of their interaction with the environment. This review paper addresses the adverse effects of erosion-corrosion in the present scenario.