Machining Techniques Research Articles

Mitigating Cloud Computing Virtualization Performance Problems with an Upgraded Logical Convergence Strategy

<p>In the domain of cloud computing and network resource virtualization, existing fusion techniques for containers and virtual machines suffer from high energy consumption, inflexible scheduling requirements, and suboptimal resource utilization. This study critically examined the current methods, accounted for the contemporary requirements, and developed a novel strategy aimed at maximizing resource utilization while minimizing energy consumption. Comprehensive experiments illustrate the superiority of our approach over state-of-the-art fusion strategies such as Kubernetes+Kubevirt and OpenStack+Kubernetes, demonstrating significant reductions in energy consumption, improved resource utilization, and enhanced system performance.</p> <p> </p>

Effects of machining parameters on Ni-Mn-Ga-based alloys for fabrication of multifunctional micro devices using femtosecond pulse width laser

Ni-Mn-Ga-based magnetic shape memory (MSM) alloy single crystals are known for their large magnetic-field-induced strain (MFIS). This quality makes them a promising material for use in micro actuators and devices. However, the manufacturing of single-crystal-based Ni-Mn-Ga micro actuators is challenging due to their high brittleness and other material properties – numerous machining techniques that are successfully used for the deep engraving of conventional engineering materials cannot be directly applied to Ni-Mn-Ga-based alloys. Nevertheless, previous studies have shown that a femtosecond pulse width laser (FPWL) can be successfully utilized for the defect-free micromachining various materials. This work studies the effects of different engraving parameters and introduces a novel scanning-based method for the deep micromachining of Ni-Mn-Ga-based MSM alloys with maximum surface quality. Results show that a 4-layer strategy with a 0.01 mm hatch distance provides excellent machining in terms of surface quality and dimensional accuracy. This study can be utilized within design stages to estimate minimum margin based on required machined depth and avoid defects that occur in the sample preparation stage. Additionally, evolution of structures generated by FPWL machining are characterized. The results highlight how FPWL can be considered a highly capable process for the micromachining and surface structuring of Ni-Mn-Ga-based single crystals for manufacturing multifunctional MSM microdevices.

Surface modification by electrical discharge machining: A systematic literature review and bibliometric analysis

In past few decades, the non-traditional machining techniques of electrical discharge machining (EDM) has been developed to machine materials that are difficult to cut. Additionally, the EDM method has also been employed to change the material's surface properties by applying a layer on the workpiece that is up to a few microns thick in order to improve the surface attributes. In past three decades, several numerous investigations on surface modification using EDM have been conducted globally. Present study provides a comprehensive review along with bibliometric investigation of the many approaches used to enhance the surface topology of the materials during the EDM process from 1993 to 2023. Firstly, a brief introduction to EDM-based different surface modification techniques such as electric discharge coating, surface texturing, surface alloying, and powder-mixed EDM has been given. All related documents were acquired from Scopus Core Collection and evaluated using a bibliometric analysis technique. The bibliometric analysis was examined based on the time distribution of articles, geography, top-cited documents, analysis of authors’ keywords, country-wise publications, etc. The result showed that India dominates this research area, followed by China, and there is a growing trend in the number of publications. Further, the influence of EDM parameters, dielectric fluid, tool, and work material etc. has also been discussed. The research work published related to topic has been summarized and resented in tabular format. This review offers interested scholars with a thorough overview of the significant achievements in the subject over the last few decades, including a description of the most relevant studies and their results. Moreover, potential areas for further investigation are emphasized with the goal of revitalize the interest of the worldwide scientific community in advancing this method.

Powder-Mixed Micro-Electro-Discharge Machining-Induced Surface Modification of Titanium Alloy for Antibacterial Properties

The powder-mixed electro-discharge machining (PM-EDM) technique has shown its advantages in forming surfaces and depositing elements on the machined surface. Moreover, using hydroxyapatite (HA) powder in PM-EDM enhances the biocompatibility of the implant’s surfaces. Ti-6Al-4V alloy has tremendous advantages in biocompatibility over other metallic biomaterials in bone replacement surgeries. However, the increasing demand for orthopedical implants is leading to a more significant number of implant surgeries, increasing the number of patients with failed implants. A significant portion of implant failures are due to bacterial inflammation. Despite that, there is a lack of current research investigating the antibacterial properties of Ti-6Al-4V alloys. This paper focuses on studying the performance of HA PMEDM on Ti-6Al-4V alloy and its effects on antibacterial properties. By changing the capacitance (1 nF, 10 nF and 100 nF), gap voltage (90 V, 100 V and 110 V) and HA powder concentration (0 g/L, 5 g/L and 10 g/L), machining performance metrics such as material removal rate (MRR), overcut, crater size and hardness were examined through the HA PM micro-EDM (PM-μ-EDM) technique. Furthermore, the surface roughness, contact angle, and antibacterial properties of HA PM micro-wire EDM (PM-μ-WEDM)-treated surfaces were evaluated. The antibacterial tests were conducted for Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Bacillus subtilis bacteria. The key results showed a correlation between the discharge energy and powder concentration with the antibacterial properties of the modified surfaces. The modified surfaces exhibited reduced biofilm formation under low discharge energy and a 0 g/L powder concentration, resulting in a 0.273 μm roughness. This pattern persisted with high discharge energy and a 10 g/L powder concentration, where the roughness measured 1.832 μm. Therefore, it is possible to optimize the antibacterial properties of the surface through its roughness.

Pengembangan Media Pembelajaran Berbasis Flip HTML 5 pada Mata Pelajaran Teknik Pemesinan Bubut di SMK Negeri 1 Padang

Flip HTML 5-based e-modules are a form of presenting material using smartphone and internet technology that can be used as an independent learning resource with a practical and easy-to-operate display. This study aims to analyze the development of learning media based on flip html 5 in lathe machining technique subjects at SMK Negeri 1 Padang. This type of research is development research or also known as R&D (Research and Development). This research uses the ADDIE development procedure. The data collection technique used a questionnaire to obtain information about the quality of the E-Module product developed in the form of a validation sheet. The instrument used by researchers in this development research is a validation guideline. From the results of the validation and practicality questionnaire, it can be concluded that the product based on Project Based Learning in the subject of Lathe Machining class XI Vocational High School is very feasible to use.

Recent Advances in Machining of Composite Materials by Electrical Discharge Machine

Composites are being used since decades and imparting excellent properties comparatively. It may be used in numerous industries because of its light weight and specific strength. Machinability of these materials is a concerned aspect. Conventional and The composites have been machined using unconventional machining techniques. Conventional methods are less suitable than non-conventional quoting the best surface finish and ability to machine complex parts. This article investigates the suitability of thermo-electric process for the machining of composites for higher surface quality and material removal. It includes the study of machining by die sinking, wire cut, powder mixed electric discharge machine in different matrix based composites along with the variation of reinforcement. Electric Discharge Machining (EDM) finds its suitability in machining of different metal matrix composites (MMC) more than the Polymer Matrix Composite (PMC) and Ceramic Matrix Composites (CMC). Variation in input parameters listed as Pulse duration, Voltage, Peak Current and Polarity is studied to obtain the optimum resulting parameters as Material Removal Rate (MRR), Surface Roughness (SR), Electrode Wear Rate (EWR) and Kerf Width. Material removal in PMC is 16% more in parallel fibre direction than with perpendicular. Low electrical conductance and high hardness of CMCs limits the use of EDM while natural ceramics are found more suitable for machining. Gap voltage, pulse on time and current are found most crucial in machining MMCs while quantifying material removal and surface roughness.

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study’s results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix’s evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.

Evaluating different methods for ranking inputs in the context of the performance assessment of decision making units: A machine learning approach

In the context of assessing the performance of decision-making units (companies, institutions, etc.), it is important to know the contribution or importance of each input to the generation of products and services in the production process. Identifying the degree of relevance of each input is a challenge from both an applied and a methodological point of view, especially within the field of non-parametric techniques, such as Data Envelopment Analysis (DEA), where the mathematical expression of the production function associated with the data generating process is not specified. This means that there is no specific coefficient to be estimated for each input, which makes it difficult to determine a ranking of importance of this type of variable compared to parametric methods, where a target function dependent on some parameters must be previously specified. Within this challenging context associated with the non-parametric approach to estimating technical efficiency, in this paper, we adapt several methods for identifying the importance of features used together with the Support Vector Machine technique in order to determine an importance ranking of the inputs in a productive process. The different adaptations developed in this article are computationally checked through a simulated experiment.

The Study of the Influence of the Tracing Speed on the Result of Surface Roughness Measurement Using the Tactile Method

This work concerns the problem of measuring surface roughness using the tactile method. The result of such a measurement is influenced by many factors, for example, the applied measurement parameters or changes in ambient conditions, e.g., temperature. The authors attempted to determine the influence of the tracing speed of the result of the profilometer tip on the roughness measurement of surfaces characterized by different types of irregularities. In the experiment, samples manufactured using various machining techniques were measured. Eleven selected roughness parameters were observed, including the most well-known ones, such as Ra, Rz, or Rt. The results obtained by the authors allowed them to determine which of the analyzed parameters are the most sensitive to changes in the tracing speed of the measuring tip, which is of great importance in the aspect of surface roughness measurements in industrial conditions. Owing to the obtained results, it is possible to determine in which cases it is possible to increase the speed of tip travel without reducing the reliability of the obtained results of surface roughness measurement.

Micromachining techniques for manufacturing high aspect ratio microelectrodes: A review

The advancement in micromanufacturing techniques has increased due to the demand for microtools used in biomedical engineering. The development of tailored micro-sized components has advanced significantly as manufacturing accuracy has improved. Although the machining of smaller dimensions is not new, difficulties arise when features are reduced to tens or hundreds of microns in size. Micro-machining technology is progressing towards the production of complex 3D parts. The creation of very precise equipment is essential to achieving productivity goals in terms of effectiveness, adaptability, robustness, and accuracy. Since even a minor modification can have a significant impact on machining performance, precise control over the machining variables is essential. This review paper describes the literature of subtractive micro manufacturing processes (traditional and advanced). In addition to subtractive processes, an overview of various micro machining techniques such as additive, mass containing and joining processes has been studied. In this paper, various micromachining techniques for fabricating high aspect ratio microelectrodes are studied and evaluated to find the suitable machining process to fabricate micro electrodes. The growing demand in the micro sector has attracted attention to the fabrication of high-aspect micro tools with diameters below 100 μm and high accuracy and precision.

A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis.

Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings. A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score. A PLT count <100×109/L independently increased the risk of death in sepsis patients (OR = 2.523; 95% CI: 1.084-5.872). The ML model, based on five platelet-related genes, demonstrated impressive area under the curve (AUC) values ranging from 0.5 to 0.795 across various validation platforms. On the GPL6947 platform, our ML model outperformed the APACHE II score with an AUC of 0.795 compared to 0.761. Additionally, by incorporating age, the model's performance was further improved to an AUC of 0.812. On the GPL4133 platform, the initial AUC of the machine learning model based on five platelet-related genes was 0.5. However, after including age, the AUC increased to 0.583. In comparison, the AUC of the APACHE II score was 0.604, and the AUC of the SOFA score was 0.542. Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.

Anomaly Diagnosis Method and Condition Assessment of Power Metering Device Based on SSD Algorithm

The advancement of anomaly diagnosis methods plays a crucial role in classifying and analyzing data, particularly in distinguishing between normal and abnormal patterns. This study explores the utilization of Support Vector Machine (SVM) techniques to facilitate the selection of pertinent data, thus enhancing the accuracy of anomaly detection. Furthermore, this research delves into the condition assessment process, which offers valuable insights into the real-time state of entities passing through power metering devices. Drawing upon a wealth of secondary data sources, this study employs the SSD algorithm to gain a comprehensive understanding of power metering devices and their interrelated aspects. The SSD algorithm, with its diverse anchor points, is revealed as a powerful tool for quantifying the energy flow passing through the power metering device. This approach not only aids in precise energy measurement but also provides essential insights into the functioning of power metering systems. By combining anomaly diagnosis, SVM techniques, and the SSD algorithm, this study contributes to a deeper comprehension of power metering devices' performance and their capacity to accurately measure energy. These insights have significant implications for improving the overall reliability and efficiency of power metering in various applications.

Laser Processing of Polyimide and Molybdenum Substrates for Extreme Environment Electronics

A desire in today’s electronic packaging technologies is to create more dense electronic packaging schemes. One method to achieve more dense electronic package integration is to use three-dimensional interconnects to minimize interconnect physical dimensions. Three-dimensional interconnect technologies, such as interposers, require a method of connecting electronics to electronic packages vertically. Thru silicon vias are a widely studied and explored vertical interconnect technology used in a variety of electronic packaging applications. Though silicon has high material hardness, the brittle nature of silicon substrates is a concern for robustness and reliability. These concerns only increase when the desired interconnect application is in extreme environments, such as cryogenic temperatures. A potential alternative to silicon in thru-via applications in extreme environments is molybdenum. Molybdenum is a commonly used material in many applications, such as automotive and aerospace, and has high material strength. Molybdenum is also a low coefficient of thermal expansion material, comparable to silicon, which in combination with high material strength lends to high reliability at extreme temperatures. A challenge of working with refractory metals such as molybdenum is that conventional CNC machining techniques require the use of machining coolants that may not be compatible with fabricated electronics and/or processes can become costly due to tool wear and the need for specialized equipment. Additionally, using molybdenum as a thru-via substrate requires the need for an electrically insulated layer, such as polyimide, within drilled molybdenum thru-holes to prevent the molybdenum substrate from shorting via connections. One potential solution to the above challenges is developing laser processing techniques to cut and drill molybdenum and polyimide. Laser processing is becoming a preferred method for inexpensive prototyping with high resolution and design versatility. This work seeks to meet the challenges described by developing laser cutting and drilling techniques for both molybdenum and polyimide for the use in cryogenic applications. This work will describe two laser processing configurations utilizing two different lasers to laser machine molybdenum and polyimide. This work will first describe the process strategies of laser cutting and drilling molybdenum as a stand-alone substrate. Aspects of laser processing on molybdenum, such as pattern repetitions and scanning speeds, will be described. Process parameter variations will be compared directly and evaluated. Additionally, post-laser processing steps, such as abrasive blasting, will be described and discussed. Next, the fabrication steps to achieve an insulating layer of polyimide in laser drilled holes as well as on each surface of the molybdenum will be detailed. Once polyimide has been cured, laser processing parameters to realize thru-holes in the polyimide-filled molybdenum drills will be presented. Adhesion of polyimide on the molybdenum substrate will be verified and demonstrated through the use of confocal scanning acoustic microscopy before and after polyimide laser processing. Cryogenic performance will be verified by completing confocal scanning acoustic microscopy after submersion in liquid nitrogen (~77 K) and compared with the pre-submersion microscopy images. Processes detailed in this work will open opportunities for future development of molybdenum-based interconnect structures for use in cryogenic applications. Acknowledgement We thank the Alabama Micro/Nano Science and Technology Center (AMNSTC) for providing access to fabrication and characterization facilities used in this work.

A Study to Investigate the Influence of Machining Parameters on Delamination in the Abrasive Waterjet Machining of Jute-Fiber-Reinforced Polymer Composites: An Integrated Taguchi and Response Surface Methodology (RSM) Optimization to Minimize Delamination

In recent years, abrasive waterjet machining has emerged as a promising machining technique for the machining of composites because of its non-thermal nature. In the present study, the effect of machining parameters on the quality of machining has been studied and the process parameters have been optimized to machine jute-fiber-reinforced polymer composites. The design of the experiment was used to predict the combination of the input parameters for L27 experiments. Taguchi and response surface methodology (RSM) techniques were employed to analyze the experimental data and identify the optimum combination of process parameters to achieve as little delamination as possible. The results indicate that an increase in the values of the parameters traverse speed and abrasive mass flow rate leads to an increase in the damage obtained. However, an increase in the value of the parameter stand-off distance minimizes the damage produced. To achieve minimum delamination, the optimum combination of input parameters is obtained through Taguchi and RSM. For the present experimental condition, to achieve minimum delamination, the parameter traverse speed should be set at 20 mm/min, the stand-off distance should be 4 mm, and the abrasive flow rate should be set at 0.25 kg/min. The results confirm that the optimum combination of parameters obtained through both approaches is similar. This investigation results indicated a significant improvement in the cutting quality with reduced damage, achieved through the optimized process parameters. For the considered range of parameters, graphs are plotted such that any intermediate values can be anticipated within the considered range without performing any further experiments. The present work signifies the effect of fiber orientation on delamination.

Comparison of Feedback Field-Weakening Techniques for Synchronous Machines with Permanent Magnets

In recent decades the market share of electrical cars has increased significantly, which has paved the way for the development of automotive electronics. Some of the most important parts of modern electrical vehicles are motor drives, which are used in car training and mechanization. Electrical drives are used in powertrains for traction, in air conditioning systems to cool cars and their parts, in doors for opening/closing as well as window movements, etc. The most popular motor type in electrical vehicles is synchronous motors with permanent magnets, which are compact and provide high torque. However, these motors require the development of control systems for proper operation. This system has to have the capacity to implement several state-of-the-art techniques, which can fully utilize motor potential, increase its efficiency, and decrease battery usage. One of these techniques is field-weakening, which overcomes speed limitations due to a lack of supply voltage and increases the motor’s speed operation range. This paper discusses the most popular approaches to field-weakening, including a new method proposed by the author. It considers both the pros and cons of each approach and provides recommendations for their usage. After that, this manuscript demonstrates the experimental results of each field-weakening technique obtained in the same motor drive, compares their performance, and discusses their strengths and weaknesses. Finally, the experimental part demonstrates that the proposed field-weakening approach demonstrates similar dynamics in load transients but provides 10 times less load to the microcontroller.

Determination of the actual deflection of the cutting tool ın turning by the Castigliano theorem

The turning method is a widely utilized technique in machining. The effect of machining parameters on the machining quality in turning is quite high. A longer tool overhang reduces tool rigidity, leading to increased tool deflection and subsequently, a decrease in machining quality. The tool overhang should be as short as possible in order to reduce tool deflection and eliminate the negative effects of vibration. Another critical factor affecting machining quality, as much as the tool overhang length is the distance between the main cutting edge and the tool holder axis. In the turning process, the alteration of the main cutting-edge position significantly influences the amount of tool deflection. When the main cutting edge is positioned farther from the tool holder axis, it generates a greater torsional moment effect, which negatively impacts machining quality. However, although the main cutting edge position of the turning tool has a great effect on the machining quality, it has not been adequately explored in the existing literature.This study examines how deflection values vary concerning the deviation of the main cutting edge from the tool holder axis using various methods and compares the results. In contrast to the commonly used deviation equation, this research applies the Castigliano theorem, which provides results much closer to actual tool deflection values. The outcomes derived from the Castigliano theorem are then compared with finite element analysis (FEM) and experimental studies. The proposed theoretical approach aligns well with the experimental results, highlighting the significance of considering torsional moments alongside bending moments as they both play a substantial role in tool deflection.

AI Driven Innovation in early Detection and Diagnosis of Brain Cancer

The continuous advancement of artificial intelligence (AI) has brought about a significant transformation in the healthcare sector, namely in the domain of early identification and diagnosis of intricate medical ailments. The present study investigates the use of artificial intelligence (AI) in the identification and diagnosis of brain tumors at an early stage. This research capitalizes on a combination of advanced methodologies such as Genetic Algorithms, Local Binary Patterns (LBP), Deep Learning-Based Segmentation, and Support Vector Machines (SVM) to achieve its objectives. Genetic algorithms are utilized in the context of feature selection to optimize the discriminative capability of input data. The use of Local Binary Patterns (LBP) presents a reliable method for doing texture analysis, hence improving the characterization of diseased areas in brain imaging. Deep learning-based segmentation approaches have demonstrated high efficiency in extracting tumor boundaries and accurately distinguishing them from healthy brain tissue, hence enabling precise localization. The Support Vector Machine (SVM) technique, which is highly effective in classification tasks, plays a crucial role in the diagnostic process by accurately distinguishing between benign and malignant tumour cases. The utilisation of an interdisciplinary method not only enhances the precision and dependability of brain tumor diagnosis, but also accelerates the procedure, therefore facilitating prompt intervention and potentially life-preserving therapeutic alternatives for individuals. The research highlights the significant capacity of AI-based approaches in revolutionizing the field of neuroimaging, emphasizing their crucial contribution to augmenting the abilities of healthcare practitioners in the essential undertaking of identifying and diagnosing brain tumors.

Nanoparticle Incorporation to Enhance Titanium Alloy Electric Discharge Machining Capabilities

The primary objective of this study was to examine the effects of the Powder-blended Micro Electric Discharge Machining (PMEDM) technique on micromachining applications, specifically when sea water is employed as the dielectric medium. Tiny apertures with a width of 200µm were punctured over Ti-6Al-4V plates. In the initial round of experimentation, the machining performance was evaluated by subjecting sea water to various process variables without the inclusion of any other ingredients. The effects of input variables, including electrode material, hole voltage, current, Pulse-on-time, and Duty factor, on the Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), Circularity Error (CE), and Taper Ratio (TR) were analysed by conducting tests in accordance with Taguchi's L18 plan design. The method employed to ascertain the optimal parametric configuration for multi-objective enhancement involved utilising the strategy of soliciting inclination based on similarity to an ideal arrangement. The present study aimed to investigate the effects of foreign materials on the dielectric-based miniature electrical discharge machining (EDM) process in sea water. This was achieved by utilising powders with varying weight concentrations and molecule sizes, including non-conductive (Al2O3), semi-conductive (SiC), and conductive (Al) powders. The experimental setup ensured that the variable boundaries were maintained at their ideal settings. The results indicate that the choice of tool has a notable influence on the functioning of micro EDM when sea water is employed as the dielectric, without the inclusion of extraneous particles. The performance metric of multi-objective execution in PMEDM is influenced by the conductivity of supplementary compounds. An 83.18 percent increase in Material Removal Rate (MRR) was observed when SiC added compounds were utilised in Powder Mixed Electrical Discharge Machining (PMEDM). Conversely, there was a drop in Tool Wear Rate (TWR) by 36.42 percent, Open Circuit voltage (OC) by 21.48 percent, Current Efficiency (CE) by 45.15 percent, and Tool Roughness (TR) by 22.87 percent.

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

In the field of machining different grades of alloys using wire electrical discharge machining (WEDM), developing efficient techniques has been a challenge. Typically, specific supervised learning and optimization (SLO) methods are created for individual alloys, resulting in a time-consuming process. To address this issue, a unified SLO approach capable of handling various grades of alloys with different compositions and physical characteristics is proposed. In this study, three stochastic optimization tools are combined with a multi-layer neural network to determine the optimal SLO strategy. Five different alloy grades are considered, with inputs consisting of six variables, including chemical composition, and physical properties. The neural network architecture used is 19-500-500-4, and it is combined with a gray wolf stochastic algorithm, proving to be the most effective SLO methodology. The results indicate that regardless of the alloy grade, the pulse on time significantly affects the material removal rate, kerf width, and thickness of the recast layer (RL). Surface roughness is primarily influenced by alloy properties such as density, specific heat, and elements like Cr, Mo, and Ni. Additionally, the recast layer is dependent on factors such as V, Ti, thermal conductivity, and thermal diffusivity of the materials. Validation experiments demonstrate that the mean squared error of all responses for each alloy remains within 5.0%. This unified SLO approach provides a promising solution for optimizing the machining process of different alloy grades, reducing the need for separate techniques for each material.

Heart sound classification using wavelet scattering transform and support vector machine

OBJECTIVE: A representation of the sound recordings that are associated with the movement of the entire cardiac structure is termed the Phonocardiogram (PCG) signal. In diagnosing such diverse diseases of the heart, PCG signals are helpful. Nevertheless, as recording PCG signals are prone to several surrounding noises and other disturbing signals, it is a complex task. Thus, prior to being wielded for advanced processing, the PCG signal needs to be denoised. This work proposes an improved heart sound classification by utilizing two-stage Low pass filtering and Wavelet Threshold (WT) technique with subsequent Feature Extraction (FE) using Wavelet Scatter Transform and further classification utilizing the Cubic Polynomial Support Vector Machine (SVM) technique for CVD. METHOD: A computer-aided diagnosis system for CVD detection centered on PCG signal analysis is offered in this work. Initially, by heavily filtering the signal, the raw PCG signals obtained using the database were pre-processed. Then, to remove redundant information and noise, it is denoised via the WT technique. From the denoised PCG, wavelet time scattering features were extracted. After that, by employing SVMs, these features were classified for pathology. RESULTS: For the analysis, the PCG signal obtained from the Physionet dataset was considered. Heavy low-pass filtering utilizing a Low-Pass Butterworth Filter (LPBF) is entailed in the pre-processing step. This removed 98% of the noise inherently present in the signal. Further, the signal strength was ameliorated by denoising it utilizing the WT technique. Promising results with maximum noise removal of up to 99% are exhibited by the method. From the PCG, Wavelet Scattering (WS) features were extracted, which were later wielded to categorize the PCG utilizing SVMs with 99.72% accuracy for different sounds. DISCUSSION: The Classification accuracies are analogized with other classification techniques present in the literature. This technique exhibited propitious outcomes with a 3% improvement in the F1 score when weighed against the top-notch techniques. The improvement in the metrics is attributed to the usage of the pre-processing stage comprising of Low-pass filter and WT method, WS Transform (WST), and SVMs. CONCLUSION: The superiority of the proposed technique is advocated by the comparative investigation with prevailing methodologies. The system revealed that Coronary Artery Disease (CAD) can be implemented with superior methods to achieve high accuracy.