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With the enhancement in science and technology, necessity of complex shapes in manufacturing industries have become essential for more versatile applications. This leads to the demand for lightweight and durable materials for applications in aerospace, defense, automotive, as well as sports and thermal management. Wire electric discharge machining (WEDM) is an extensively utilized process that is used for the exact and indented shaped components of all materials that are electrically conductive. This technique is suitable in practically all industrial sectors owing to its widespread application. The present investigation explores WEDM for LM6/fly ash composites to optimize different process variables for attaining performance measures in terms of maximum material removal rate (MRR) and minimum surface roughness (SR). Taguchi’s L27 OA design of experiments, grey relational analysis, and analysis of variance (ANOVA) were employed to optimize SR and MRR. It has been noted from ANOVA that reinforcement (R) percentage and pulse on time are the most influential aspects for Grey Relational Grade (GRG) with their contributions of 28.22 and 18.18%, respectively. It is found that the best process variables for achieving the highest MRR and lowest SR simultaneously during the machining of the composite are gap voltage of 30 V, pulse on time of 10 µs, pulse off time of 2 µs, wire feed of 8 m/min, and R of 9%. The predicted GRG is 0.84, and the experimental GRG value is 0.86. The validation experiments at the optimized setting show close agreement between predicted and experimental values. The morphological study by optical microscopy revealed a homogenous distribution of reinforcement in the matrix which enhances the composite’s hardness and decreases the density.

Using a cutting-edge net-shape manufacturing technique called Additive Layer Manufacturing (ALM), highly complex components that are not achievable with conventional wrought and cast methods can be produced. As a result, the aerospace sector is paying closer attention to using this technology to fabricate superalloys based on nickel to develop the holistic gas turbine. Because of this, there is an increasing need for the mechanical characterisation of such material. Conventional mechanical testing is hampered by the limited availability of material that has been processed, especially given the large number of process factors that need to be assessed. Thus, the present study focuses on manufacturing CM247LC Ni-based superalloy with exceptional mechanical characteristics by laser powder bed fusion (L-PBF). This study evaluates the effect of input process variables such as laser power, scan speed, hatch distance and volumetric energy density on the mechanical performance of the LPBF CM247LC superalloy. The maximum value of as-built tensile strength obtained in the study is 997.81 MPa. Plotting Pearson's heatmap and the Feature importance (F-test) was used in the data analysis to examine the impact of input parameters on tensile strength. The accuracy of the tensile strength data classification by machine learning algorithms, such as k-nearest neighbours, Naïve Baiyes, Support vector machine, XGBoost, AdaBoost, Decision tree, Random forest, and logistic regression algorithms, was 92.5%, 83.75%, 83%, 85%, 87.5%, 90%, 91.25%, and 77.5%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing. [ABSTRACT FROM AUTHOR]

This article details the existing wearable assistive devices that could mimic a human's active range of motion and aid individuals in recovering from stroke. The survey has identified several risk factors associated with musculoskeletal pain, including physical factors such as engaging in high-intensity exercises, experiencing trauma, aging, dizziness, accidents, and damage from the regular wear and tear of daily activities. These physical risk factors impact vital body parts such as the cervical spine, spinal cord, ankle, elbow, and others, leading to dysfunction, a decrease in the range of motion, and diminished coordination ability, and also influencing the ability to perform the activities of daily living (ADL), such as speaking, breathing and other neurological responses. An individual with these musculoskeletal disorders requires therapies to regain and restore the natural movement. These therapies require an experienced physician to treat the patient, which makes the process expensive and unreliable because the physician might not repeat the same procedure accurately due to fatigue. These reasons motivated researchers to develop and control robotics-based wearable assistive devices for various musculoskeletal disorders, with economical and accessible solutions to aid, mimic, and reinstate the natural active range of motion. Recently, advancements in wearable sensor technologies have been explored in healthcare by integrating machine-learning (ML) and artificial intelligence (AI) techniques to analyze the data and predict the required setting for the user. This review provides a comprehensive discussion on the importance of personalized wearable devices in pre- and post-clinical settings and aids in the recovery process. [ABSTRACT FROM AUTHOR]

An engine bracket is one of the most critical components of the engine used for mounting and supporting the engine in the vehicles. Today, the automobile industry requires lightweight components, which will reduce the car's overall weight when fitted into the vehicle. Topology optimization is a technique with the help of which the surface of a component is optimized to get the required shape for having reduced weight. The weight is reduced by optimizing the material on the surface of the details. In this paper, the work done is the application of topology optimization on the surface of the engine bracket arm. Then the optimized model is tested computationally using realistic conditions. Bi-directional evolutionary structural optimization is used as a technique for topology optimization. With the help of the BESO method, the material optimization is done, and then the weights are compared with the original component. A new algorithm is developed using MATLAB codes. The sensitivity ratio is considered using the von Mises strength as a critical parameter for the BESO method for optimization. The optimized bracket model is then assembled with the hub of the component, and then the assembly is simulated for verification using standard conditions. A comparison of weight reduction is there using topology optimization.

The present design of a set of worm gears used in a soot blower produced by a certain manufacturer has an efficiency of 68.8%. A soot blower is one of the most critical components in industrial applications for removing the large amounts of soot generated by boilers and is required to be operational 24×7. The energy consumption of the soot blower depends on its working efficiency and ultimately the design of its set of worm gears. This paper focuses mainly on the design and analysis of available industrial worm-gear sets used in soot blowers. The theoretical, experimental, and finite-element analysis approaches are validated for the stability of the worm gear set under typical input conditions. This paper also describes an analytical design of experiments (DOE) approach to identify the most significant factor for performance (efficiency) improvement and suggests some design improvements for the worm gear set using the profile modification approach. These ensure the efficiency improvement of the current industrial design of the set of worm gears used in a soot blower. The analytical DOE approach helped identify that the number of worm wheel teeth (Z2) and gear module (m) are the two most significant factors affecting performance. Accordingly, based on the improved design, the final efficiency increased from 68.8% to 74.6% (~8.5% increment), resulting in lower power consumption during industrial application. [ABSTRACT FROM AUTHOR]

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes. [ABSTRACT FROM AUTHOR]

Introduction: This study presents a comprehensive design and analysis of a 300L steel fuel tank intended for heavy off-road vehicles. The design process integrates numerical simulations and experimental investigations to optimize the tank's performance and durability under various operating conditions. Methods: The design methodology involves CAD model optimization, numerical analysis setup, and experimental validation. CAD model optimization simplifies the tank geometry while retaining structural integrity. Numerical analysis setup includes defining boundary conditions, meshing strategies, and simulation parameters. Experimental validation entails testing the tank under dynamic loading conditions to assess its structural response. Results: Numerical simulations reveal insights into stress distribution, deformation behavior, and fluid dynamics within the tank. Experimental tests confirm the numerical predictions and provide valuable data for model validation. Key results include stress concentrations in critical areas, deformation patterns under different loading conditions, and fluid flow characteristics. Discussion: The integrated approach combining numerical simulations and experimental tests offers a comprehensive understanding of the fuel tank's behavior. Findings highlight areas for design improvement, such as reinforcement of stress-prone regions and optimization of fluid flow dynamics. The study contributes to enhancing the performance, reliability, and safety of fuel tanks for heavy off-road vehicles. [ABSTRACT FROM AUTHOR]

Nowadays, research has shown the emergence of the 3D printing method for printing a functionalized component. Graphene nanomaterial has an enormous conducting property that can compete with conducting materials like copper and silicon. This paper describes the electrical conductivity investigation of 3D-printed graphene nanomaterial in extrusion-based 3D printing methods. In extrusion, two different approaches of the 3D printing method were used to print the graphene-based structure: the fused deposition modeling (FDM) method and the direct ink writing (DIW) method. Both printing methods follow the two printing processes and select material forms. Selection of testing was made to analyze the characterization variations in the printed material, such as XRD, TGA, viscosity, Raman shift, and Scanning Electron Microscopy analyses, which shows the changes of effect in the conductivity due to various parameter differences in both the printing methods. A four-point probe technique was used to analyze the electrical conductivity of the two different methods. These analysis results prove that the characterization variations differ in the FDM and DIW printed models. [ABSTRACT FROM AUTHOR]

A highly advanced thermo-electric machining technique called wire electrical discharge machining (WEDM) can effectively produce parts with varying hardness or complicated designs that have sharp edges and are very difficult to machine using standard machining procedures. This useful technology for the WEDM operation depends on the typical EDM sparking phenomena and makes use of the commonly used non-contact material removal approach. Since its inception, WEDM has developed from a simple approach for creating tools and grown to an outstanding option for creating micro-scale components having the greatest degree of dimensional precision and surface finish characteristics. The WEDM method has endured over time as an efficient and affordable machining alternative that can meet the stringent operating specifications enforced by rapid manufacturing cycles and increasing expense demands. The possibility of wire damage and bent, nevertheless, has severely hindered the process' maximum potential and decreased the precision as well as effectiveness of the WEDM process. The article examines the wide range of investigations that have been done; from the WEDM through the EDM process' spin-offs. It describes WEDM investigation that required variables optimization and an assessment of the many influences on machining efficiency and accuracy. Additionally, the research emphasizes adaptive monitoring and control of the process while examining the viability of multiple approaches to control for achieving the ideal machining parameters. Numerous industrial WEDM applications are described with the advancement of hybrid machining techniques. The paper's conclusion examines these advancements and identifies potential directions for subsequent WEDM research. The investigation on WEDM of metal matrix composites (MMCs) is also reviewed; along with the impacts of various cutting variables like wire feed rate (F), voltage (V), wire tension (WT), and dielectric flow rate on cutting processes outcomes like material removal rate (MRR), kerf width (Kw) and surface roughness (SR). In the present article, future directions for WEDM research were also suggested. [ABSTRACT FROM AUTHOR]

The abrasive characteristics of LM6 alloys are difficult to machine, so designing a technology that allows for effective machining is essential. This paper aims to evaluate the effect of process variables, namely feed rate, spindle speed and drill material, towards the responses like Thrust force, Surface roughness and burr height when drilling of LM6 alloy. LM6 aluminium alloy was fabricated by the stir casting process. Experiments were conducted using L9 orthogonal array in a Vertical Machining Centre coupled with a dynamometer for measuring thrust force. Surface roughness was found by Surface roughness tester and burr height was measured using Vision Measuring System. The findings show that the created model can accurately estimate the thrust force (TF), surface roughness (SR) and burr height (BH) in LM6 alloy drilling within the parameters examined.

This study performed a steady-state numerical analysis to understand the temperature in different heat sink configurations for LED applications. Seven heat sink configurations named R, H-6, H-8, H-10, C, C3, and C3E3 were considered. Parameters like input power, number of fins, heat sink configuration were varied, and their influence on LED temperature distribution, heat sink thermal resistance and thermal interface material temperature were studied. The results showed that the temperature distribution of the H-6 heat sink decreased by 46.30% compared with the Cheat sink for an input power of 16 W. The result of the H-6 heat sink shows that the heat sink thermal resistance was decreased by 73.91% compared with the Cheat sink at 16 W. The lowest interface material temperature of 54.11 °C was achieved by the H-6 heat sink when the input power was used 16 W. The H-6 heat sink exhibited better performance due to more surface area with several fins than other heat sinks.

Fiber-reinforced additive manufacturing components have been used in various industrial applications in recent years, including in the production of aerospace, automobile, and biomedical components. Compared to conventional methods, additive manufacturing (AM) methods can be used to obtainin lighter parts with superior mechanical properties with lower setup costs and the ability to design more complex parts. Additionally, the fabrication of onyx composites using the conventional method can result in delamination, which is a significant issue during composite machining. To address these shortcomings, the fabrication of onyx composites via additive manufacturing with the Mark forged 3D-composite printer was considered. Machinability tests were conducted using abrasive water jet machining (AWJM) with various drilling diameters, traverse speeds, and abrasive mass flow rates. These parameters were optimized using Taguchi analysis and then validated using the Genetic algorithm (GA) and the Moth Flame Optimization algorithm (MFO). The surface morphology (Dmax) and the roughness of the drilled holes were determined using a vision measuring machine with 2D software (MITUTOYO v5.0) and a contact-type surface roughness tester. Confirmation testing demonstrated that the predicted values werenearly identical to the experimental standards. During the drilling of an onyx polymer composite, regression models, genetic algorithms and the Moth-Flame Optimization algorithm were used to estimate the response surface of delamination damage and surface roughness. [ABSTRACT FROM AUTHOR]