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This study aims to explore the potential of the engraving milling process to manufacture 3D structures and layouts like rotor wheels. An experimental study was carried out to manufacture a stepped rotor wheel from EN8 steel blank using a computer numeric control (CNC) engraving milling machine tool. The impact of variable parameters, namely rotational speed (SR), feed (f), and plunge feed (Pf), on maximum roughness depth (Rt) and material removal rate (MRR) was determined through a total of fourteen tests, each with two replications based on Box-Behnken design (BBD) of the response surface methodology (RSM). The best variable combination was found using desirability functional analysis (DFA) with goals of minimizing Rt and maximizing the MRR. The best values achieved at the optimal combination of variable parameters were 2.95 µm as Rt and 43.45 mm³/min as MRR. Micrographs of the rotor wheel blades and cutting tool supported the findings.

Aluminum is a light and soft material that is difficult to machine. It is the most produced non-ferrous metal and undergoes extensive machining for the development of a wide range of products. Advances in industry inspire the need to find sustainable ways of machining aluminum and its alloys using conventional machining processes. In the study reported in this paper, two sets of experiments were conducted to investigate the machinability of aluminum 7075 using a plain carbide tool under a dry environment, i.e., no lubrication. In the first set, four rough experiments were conducted where three important machining parameters, i.e., cutting speed CS (115-495 RPM), depth of cut DOC (0.8-1.5 mm), and Feed rate FR (0.08-0.2 mm/rev) have been varied at two levels each to check the behavior of responses or machinability indicators, i.e., surface roughness and tool wear, at machining parameters' highest and lowest values. Based on the results of the first set of experiments, the ranges and levels of parameters have been fixed in the second set for a detailed study of the machinability of aluminum. A total of nine experiments based upon Taguchi's robust design of experiment technique with orthogonal array L9 have been conducted where an additional machining parameter, i.e., machining time MT, has been introduced. The effect of machining parameters on tool wear and surface roughness has been studied in detail, and it is found that the dry machining of aluminum is possible without the early failure of the tool. Dry machining with low values of CS, DOC, FR, and medium MT is desirable for better machinability, i.e., minimum roughness and tool wear, an optimum combination of machining parameter cutting speed-115 RPM, depth of cut-0.8 mm, feed rate-0.12 mm/rev, and machining time-90 seconds. The findings of the present work will assist engineers and researchers in attaining quality, productivity, and sustainability while manufacturing parts and components from aluminum to be used in the automotive, defense, and aerospace sectors.

Isometric handgrip (IHG) exercise causes changes in heart rate variability (HRV) and hemodynamic responses with individual differences. We test the hypothesis that baseline measurements affect post-exercise outcomes.

Purpose: Human monkeypox (MPX) is a viral infection that transmits between individuals via direct contact with animals, bodily fluids, respiratory droplets, and contaminated objects like bedding. Traditional manual screening for the MPX infection is a time‐consuming process prone to human error. Therefore, a computer‐aided MPX screening approach utilizing skin lesion images to enhance clinical performance and alleviate the workload of healthcare providers is needed. The primary objective of this work is to devise an expert system that accurately classifies MPX images for the automatic detection of MPX subjects. Methods: This work presents a multi‐modal deep learning system through the fusion of convolutional neural network (CNN) and machine learning algorithms, which effectively and autonomously detect MPX‐infected subjects using skin lesion images. The proposed framework, termed MPXCN‐Net is developed by fusing deep features of three pre‐trained CNNs: MobileNetV2, DarkNet19, and ResNet18. Three classifiers—K‐nearest neighbour, support vector machine (SVM), and ensemble classifier—with various kernel functions, are used to identify infected patients. To validate the efficacy of our proposed system, we employ a publicly accessible MPX skin lesion dataset. Results: By amalgamating features extracted from all three CNNs and utilizing the medium Gaussian kernel of the SVM classifier, our proposed system achieves an outstanding average classification accuracy of 90.4%. Conclusions: Developed MPXCN‐Net is suitable for testing with a large diversified dataset before being used in clinical settings. [ABSTRACT FROM AUTHOR]

Metal matrix composites (MMCs) are essential materials in various industries due to superior properties, such as high strength-to-weight ratios, better corrosion resistance, improved wear resistance and adaptability, developed by continuous improvements in their fabrication methods. This helps to meet the growing demand for high-performance and sustainable products. The industries that stand to gain the most are automotive and aerospace, where MMCs are used for car parts, airplane frames, and jet engines that need to be strong and lightweight. Furthermore, MMCs are being extensively used in the biomedical industry for implants and medical equipment because of their suitable mechanical integrity and corrosion resistance. Applications in heavy construction, defense, and even space exploration are noteworthy. The advancements in fabrication of MMCs have revolutionized the composite industry with their improved mechanical, tribological, and metallurgical properties. This review article offers an introduction and thorough examination of the most recent advancements (mostly within the last five years) in fabrication methods of MMCs. The novelty and modernization in the traditional processes and advanced processes are covered, along with discussing the process parameters' effects on the microstructure and properties of the composites. The review focuses on features and prospective applications of MMCs that have been greatly improved and extended due to such advancements. The most recent methods for developing MMCs, including friction stir processing (FSP), ultrasonic-assisted stir casting, and additive manufacturing, are discussed. Artificial intelligence and machine learning interventions for composite manufacturing are also included in this review. This article aims to assist researchers and scholars and encourage them to conduct future research and pursue innovations to establish the field further. [ABSTRACT FROM AUTHOR]

This article presents a case study on improving process efficiency in a mining equipment part fabrication company. The company was facing issues concerning communication, organisation, and workflow processes. This study investigated that ineffective communication among departments was the major weakness which was responsible for the long lead or idle time. This lead time was a waste that affected the company’s productivity. A great amount of time was spent on non-value-added processes. The Kanban Centralised Communication System was implemented. Time study and value stream mapping were also used. A significant improvement in process efficiency from 34% to 85% was achieved by reducing lead time from 4200 minutes to 1680 minutes after streamlining the communication in the company using Kanban. [ABSTRACT FROM AUTHOR]

Lean methodologies, a system widely used in the manufacturing industry, have demonstrated numerous benefits and added significant value to the industry. In the same way that other industries have benefitted from the application of Lean techniques, the engineering projects sector can also realise significant improvements by integrating Lean principles into its processes and operations. The study aims to gain a comprehensive insight into the application of the Lean concept, with a focus on the South African engineering projects sector. The findings of a thorough literature review, focussing on Lean techniques, technology, and the projects sector, examined key variables that have an impact on the effective implementation of Lean methodologies within this industry. The variables studied are skills and expertise, active work methods, and leadership style. The study also explores the impact of digital technologies on Lean implementation, exploring how these technologies enhance the integration of Lean practices. The study includes descriptive statistical analysis, exploratory factor analysis, and a confirmatory factor analysis of the data. A significant relationship is found between variables influencing Lean implementation and the effectiveness of Lean practices within the South African engineering projects sector. [ABSTRACT FROM AUTHOR]