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Composites are replacing more conventional materials due to their advantageous properties, such as high strength, hardness, low weight, and wear resistance. In this study, the stir casting method is used to create an Al7075/SiC aluminium matrix composite, and its dry sliding wear behaviour is examined. The EDX and SEM results both show that the silicon carbide is evenly distributed throughout the matrix. The dry sliding wear behaviour of the composites is investigated using the Taguchi L16 orthogonal array to reduce the number of experimental runs. Four key process parameters—reinforcement quantity (0%, 3%, 6%, and 9%), load (15N, 30N, 45N, and 60N), sliding velocity (0.75m/s), sliding distance (1.5m/s), and sliding distance (3m/s)—are evaluated across four levels to determine the best parameter combination for reducing wear rate. S/N ratios are best when the following conditions are met: 3 wt.% SiC reinforcement, 15 N load, 3 m/s sliding velocity, and 800 m sliding distance (as shown in the main effect graphic). Wear rate, frictional force, and coefficient of friction are all affected by the four process parameters, and their effects are often studied using analysis of variance (ANOVA). The Analysis of Variance (ANOVA) outcome indicated that the probability value associated with the applied load was below 0.05, signifying statistical significance.

This study emphasizes the criticality of measuring specific cutting energy in machining Hastelloy C276 for biomedical industry applications, offering valuable insights into machinability and facilitating the optimization of tool selection, cutting parameters, and process efficiency. The research employs artificial intelligence-assisted meta-models for cost-effective and accurate predictions of specific cutting energy consumption. Comparative analyses conducted on Hastelloy C276, utilizing a TiAlN-coated solid carbide insert across various media (dry, MQL, LN2, and MQL+LN2), reveal the superiority of hybrid LN2+MQL in reducing specific cutting energy consumption. Subsequently, the analysis of variance underscores the cutting speed as the most influential parameter as compared to other inputs. Finally, a statistical evaluation compares the Gene Expression Programming (GEP) model against the Artificial Neural Network (ANN), and Response Surface Methodology model, demonstrating the superior predictive performance of the GEP meta-model. The GEP model demonstrates validation results with an error range of 0.25%–1.52%, outperforming the ANN and RSM models, which exhibit an error range of 0.49%–8.33% and 2.68%–10.18%, respectively. This study suggests the potential integration of contemporary intelligent methodologies for sustainable superalloy machining in biomedical industry applications, providing a foundation for enhanced productivity and reduced environmental impact of surgical instrument and biomedical device machining. [ABSTRACT FROM AUTHOR]

Composites are versatile materials that can be tailored to meet the specific needs of various industries, including aerospace, automotive, and construction. This study focuses on investigating how the amount of titanium-di-boride (TiB2) particles (0%, 3%, 6%, and 9%) affects the physical, mechanical, microstructural, and fracture properties of Al7075/TiB2 composites produced using a liquid-state stir-casting process. The addition of TiB2 particles increases the hardness and tensile strength of the Al7075 alloy. This has been confirmed by X-ray diffraction analysis, which showed that these particles are dispersed throughout the aluminum matrix. The hardness increases from 60 HV to 90 HV and the tensile strength increases from 140 MPa to 211 MPa. The significance of increased wettability brought about by the addition of magnesium powder and the preheating of a ceramic reinforcement before pouring molten metal was revealed by scanning electron micrographs of the cast composites, which showed an even dispersal of reinforcements in the aluminum matrix, establishing the strong connection between the matrix and the reinforcement agents. Fracture surface scanning electron microscopy revealed that micro-void formation and nucleation in Al7075/TiB2 composites resulted in ductile material paves a way for manufacturing innovation. [ABSTRACT FROM AUTHOR]

Magnesium AZ91/AlN-based surface composites have been synthesized using the friction stir processing (FSP) technique. The influence of particle addition during FSP on microstructural and mechanical properties has been investigated. Composite plates of 5, 10, and 15 vol. % AlN were synthesized using two-pass FSP with an axial force of 10 KN, a tool travel speed of 40 mm/min, and a tool rotation speed of 1200 rpm. The AZ91/AlN surface composites were analyzed microscopically with the use of an optical microscope, an x-ray diffractometer, and a scanning electron microscope. The prepared composites were tested for hardness and tensile strength. Micrographs taken in the stir zone revealed a consistent distribution of AlN particles throughout the AZ91 matrix. The AZ91/AlN surface composites were tested for hardness and tensile strength, and the findings showed that the AlN particles improved the mechanical properties without significantly decreasing ductility. The fracture surfaces of the composites were studied, and the mechanisms of fracture were identified. [ABSTRACT FROM AUTHOR]

Detecting diseases is a vital and crucial step in maintaining healthy, high-yielding plants. The challenge of manually identifying infections is arduous as well. The proposed work is to diagnose plant leaf diseases and discuss their origins and remedies. Image processing is used to discover the infected leaf and provide remedial measures through a mobile robot application. The use of machine learning techniques allows for the detection of leaf diseases using the support vector machine model, the K nearest neighbor model, and the Naïve Bayes classification to categorize the sample leaves. In this paper, the Momordica charantia leaf and the common four diseases dataset are developed, and a classification model is developed to identify and categorize leaf curl, downy mildew, powdery mildew, and angular leaf spot. Based on the disease classification, appropriate chemical pesticides are sprayed by controlling the servo actuated valve in the proposed agriculture robot, which is controlled and validated. The result reveals that the proposed approach has an average accuracy of 82% in identifying the disease type that remains more prevalent in Momordica charantia leaves than other compared classification algorithms. [ABSTRACT FROM AUTHOR]

The present study is an integrated approach of experimental and simulation processes to investigate the influences of the flushing on the wear, textural feature, heat dissipation, tool-tip temperature, and elemental contents of the electrical discharge machining-tool. The general heat transfer equation in cylindrical coordinates is used to explain the thermal phenomenon through the tool, where the boundary conditions are influenced by convective interactions and dimensionless numbers. The inter-electrode flushing is considered a micro-channel flow, and the used model is conceptualized from "Hazen–Poiseuille observation." It is observed that the adopted model has a prediction error of 8.503% and can accurately explain the thermal consequences of the process. The study reveals that tool wear is influenced by flushing velocity and flushing pressure. The tool-tip temperature reduces with the Reynolds number, and effectual expelling of the debris can be ensued with a flushing of a higher Reynolds number (Re). However, the increment of Re beyond 4500 provides rapid heat dissipation, which produces extensive residual stress and creates cracks on the surface. [ABSTRACT FROM AUTHOR]