Hybrid Tool Research Articles

Clinical Applications and Advancements of Positron Emission Tomography/Computed Tomography in Cardio-Oncology: A Comprehensive Literature Review and Emerging Perspectives.

Recent advancements in molecular biology, biotechnology, chemistry/radiochemistry, artificial intelligence, and imaging techniques have significantly propelled the field of cardiovascular molecular imaging. This review aims to provide a comprehensive overview of the current state of cardiovascular positron emission tomography (PET) imaging and cardiac computed tomography (CT), exploring their roles in elucidating molecular and cellular processes, enabling early disease detection, and guiding novel therapeutic interventions for cardiovascular conditions. Cardiovascular PET imaging strives to uncover molecular and cellular events preceding visible anatomical manifestations or physiological changes. Meanwhile, cardiac CT has evolved into a multifaceted modality, offering insights into both anatomy and function. Utilizing advanced CT technologies allows for a thorough evaluation, encompassing fractional flow reserve, perfusion imaging, pericoronary adipose tissue attenuation, atherosclerotic plaque characterization, cardiomyopathies, structural cardiac abnormalities, and congenital heart anomalies. The emergence of hybrid imaging, combining PET and CT, presents innovative prospects in cardiology. This approach enables the simultaneous assessment of cardiac perfusion and coronary anatomy in a singular scan, providing complementary insights relevant to potential coronary artery disease. Despite the substantial potential impact, operational familiarity with this hybrid tool remains limited, and its integration into routine clinical practice warrants further exploration. In summary, the review underscores the transformative impact of recent technological advancements on cardiovascular molecular imaging. The integration of PET and CT, along with their individual capabilities, holds promise for early disease detection and informed clinical decision-making. While acknowledging the potential of hybrid imaging, it emphasizes the need for increased operational familiarity and continued exploration to facilitate its seamless integration into routine clinical practice. The insights gained from this review contribute to the ongoing dialogue in the field, offering a foundation for future research and advancements in cardiovascular imaging.

High-efficiency and stable laser transmission for laser and electrochemical machining

Laser and electrochemical machining (LECM) has been increasingly adopted to improve the efficiency and surface quality of micromachining. The coupling and high-efficiency transmission of laser in electrolyte are critical to the implementation of LECM, which were rarely studied in previous research. In this study, the geometric and simulation model of laser coupling and transmission within the electrolyte jet has been established for laser and shaped tube electrochemical machining (Laser-STEM). The critical coupling condition and transmission efficiency of laser beam was investigated theoretically. Influences of laser coupling errors on the laser transmission efficiency and laser power intensity distribution at the exit of hybrid tool electrode were explored. Results indicated that the angular offset had the greatest impact on laser transmission efficiency, while the longitudinal offset impact the least, which were accordance with the experimental results. The thresholds of laser power intensity and the frequency of the laser-induced electrolyte breakdown were measured. The thresholds for laser-induced breakdown in NaNO3 solution were higher than that in NaCl solution. The laser power intensity threshold of laser-induced electrolyte breakdown increased with the increase of focal length, and the frequency of smaller than 4.5 could be obtained with the laser power density ranging from 2.35e9 to 1.06e10 W/cm2 at the focal spot. The distribution trended to more uniform at the exit with the increase of hybrid tool electrode length. A laser coupling efficiency of larger than 81.2 % has been achieved by utilizing the hybrid tool electrode with NA of 0.534 and acceptance angle of 32.3°. Finally, experiments with a designed raster-shaped workpiece were conducted to verify the feasibility of laser transmission in large depth in the Laser-STEM process. The high aspect-ratio small holes with a depth of 50 mm, and a diameter of 1.3 mm have been processed by using Laser-STEM.

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD – chemical oxygen demand, NH4-N – ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

A hybrid tool wear prediction model based on JDA

PurposeAiming at solving the problems of low prediction accuracy and poor generalization caused by the difference in tool wear data distribution and the fixation of single global model parameters, a hybrid prediction modeling method for tool wear based on joint distribution adaptation (JDA) is proposed.Design/methodology/approachFirstly, JDA is exploited to adapt the data features with different data distributions. Then, the adapted data features are identified by the KNN classifier. Finally, according to the tool state classification results, different regression prediction models are assigned to different wear stages to complete the whole tool wear prediction task.FindingsThe results of milling experiments show that the maximum prediction accuracy of this method is 95.13%, and it has good recognition accuracy and generalization performance. Through the application of the tool wear hybrid prediction modeling method, the prediction accuracy and generalization performance of the model are improved and the tool monitoring is realized.Originality/valueThe research results can provide solutions and a theoretical basis for the application of tool wear monitoring technology in practical industrial applications.

An integrated hybrid approach for assembly tolerance transfer and allocation

PurposeThis study aims to develop an optimal tolerance allocation strategy involves integrating the unique transfer (UT) approach and the difficulty coefficient evaluation (DCE) routine in an interactive hybrid method. This method combines the strengths of both UT and DCE, ensuring simultaneous utilization for enhanced performance. The proposed tolerancing model manifests an integrated computer-aided design (CAD) tool.Design/methodology/approachBy combining UT and DCE based on failure mode, effects and criticality analysis (FMECA) tool and the Ishikawa diagram, the proposed collaborative hybrid tool ensures an efficient and optimal tolerance allocation approach. The integration of these methodologies not only addresses specific transfer challenges through UT but also conducts a thorough evaluation of difficulty coefficients via DCE routine using reliability analysis tools as FMECA tool and the Ishikawa diagram. This comprehensive framework contributes to a robust and informed decision-making process in tolerance allocation, ultimately optimizing the design and manufacturing processes.FindingsThe presented methodology is implemented with the aim of generating allocated tolerances that align with specific difficulty requirements, facilitating the creation of a mechanical assembly characterized by high quality and low cost. To substantiate and validate the conceptual framework and methods, an integrated tool has been developed, featuring a graphical user interface (GUI) designed in MATLAB. This interface serves as a platform to showcase various interactive and integrated tolerance allocation approaches that adhere to both functional and manufacturing prerequisites. The proposed integrated tool, designed with a GUI in MATLAB, offers the capability to execute various examples that effectively demonstrate the benefits of the developed tolerance transfer and allocation methodology.Originality/valueThe originality of the proposed approach is the twining between the UT and DCE simultaneous in an integrated and concurrent tolerance transfer and allocation model. Therefore, the proposed approach is named an integrated CAD/tolerance model based on the manufacturing difficulty tool. The obtained results underscore the tangible advantages stemming from the integration of this innovative tolerance transfer and allocation approach. These benefits include a notable reduction in total cost and a concurrent enhancement in product quality.

Investigation of a modified IEA 15MW wind turbine including a winglet with a new mid-fidelity acoustic toolchain

This paper presents the applicability of a new hybrid tool chain for wind turbine rotor turbulent boundary-layer trailing-edge noise (TBL-TEN) to complex rotor geometries. The research introduces a rapid, high-fidelity 2D CFD/CAA method, which enables the assessment of innovative low-noise profile designs concerning TBL-TEN, coupled with a mid-fidelity extrapolation technique for the 3D analysis, aiming to minimize computational time essential for design processes. This procedure is applied here to compare the changes in performance, load distribution and noise reduction if a winglet is added to a large-scale wind turbine, using the example of the IEA Wind TCP Task 47 15-MW-reference turbine [12]. In the first step, a 3D CFD calculation of the entire turbine is performed in order to derive the local flow parameters and thus the input variables for the 2D CAA. These results for the 2D TEN are then extrapolated for the entire turbine using the fast summation and extrapolation method TAP (Turbine acoustic prediction) [1]. As only data for the TEN is included here, only this noise source for the winglet can be analyzed. For a more complete comparison, a 3D scale-resolving simulation was set up for a static wind tunnel situation in order to be able to qualitatively classify the tip/vortex interaction noise that was not recorded by the procedure mentioned above.

Multiple Criteria Group Decision-Making Using Fermatean Fuzzy Soft Matrices and Their Medical Applications

This paper introduces a brand new algebraic methodology, Fermatean fuzzy soft matrices. A new hybrid tool called the Fermatean fuzzy soft set combines the strengths of the Fermatean fuzzy set and the parametric tool soft set to handle vague and ambiguous situations in various mathematical problems. Fundamental set operations of Fermatean fuzzy soft matrices are defined, and their properties are examined. Two algorithms using the score matrix and utility matrix have been proposed for medical diagnosis problems. Each of the applications has been successfully demonstrated using numerical examples. Finally, a brief comparison between the proposed model and several previous models is presented to ensure the feasibility of the proposed strategy.

Propaganda Mission Command: a comparative social media analysis between the Badri Force 313 and the PMC Wagner

Between 2021 and 2023 we have witnessed two major military operations, both relating to the field of hybrid conflict: the Taliban conquest of Afghanistan and the Russo-Ukrainian War. Two military units stood out, respectively, the Badri Force 313 and the PMC Wagner. Both units proved the existence of a strong propaganda component combined with operations conducted on the ground. This paper aims to highlight through two comparative case studies, the existence of a new parameter that joins the operational capacity, that is, Propaganda Mission Command. This tool has been observed within the respective digital ecosystems and has had serious tactical-strategic repercussions within the combat theatres. The online communication of the two military apparatuses influenced recruitment, funding, and even the public’s knowledge of the conflict. Finally, the use of propaganda in both these units contributed to the development of a new concept of Auftragstaktik that involves the communicative sphere. The existence of such a phenomenon should stimulate public and academic debates regarding the new nature of conflicts. Conflicts in which, as we observed, the propaganda/communication component is now indivisible from the military one and can result in remarkable operational and/or strategic advantages. The analysis of this hybrid tool is also meant to underline its difference from classic state propaganda. The two case studies show a different kind of “Propaganda Mission Command” that is framed within a military context, is related to “branded” units, and can shift the balance of conflicts towards one specific side through influence and psyops.

Post-Treatment and Hybrid Techniques for Prolonging the Service Life of Fused Deposition Modeling Printed Automotive Parts: A Wear Strength Perspective

<div>This study aims to explore the wear characteristics of fused deposition modeling (FDM) printed automotive parts and techniques to improve wear performance. The surface roughness of the parts printed from this widely used additive manufacturing technology requires more attention to reduce surface roughness further and subsequently the mechanical strength of the printed geometries. The main aspect of this study is to examine the effect of process parameters and annealing on the surface roughness and the wear rate of FDM printed acrylonitrile butadiene styrene (ABS) parts to diminish the issue mentioned above. American Society for Testing and Materials (ASTM) G99 specified test specimens were fabricated for the investigations. The parameters considered in this study were nozzle temperature, infill density, printing velocity, and top/bottom pattern. The hybrid tool, i.e., GA–ANN (genetic algorithm–artificial neural network) has been opted to train, predict, and optimize the surface roughness and sliding wear of the printed parts. Results disclose that the minimum surface roughness obtained with GA–ANN was 1.05482 μm for infill density of 68%, nozzle temperature of 230°C, printing velocity of 80 mm/sec, and for concentric type of top/bottom pattern. In extension of this study, annealing was performed on the specimens printed on the optimized results obtained from the analysis at three different temperatures of 110°C, 150°C, and 190°C and for a fixed period of time of 60 min as a post-treatment process to further study the impact of annealing on the surface roughness and wear rate. The surface roughness of the samples showed a discernible improvement as a result of annealing, which can further make significant inroads in automotive industries.</div>

An AHP parsimonious based approach to handle manufacturing errors in production processes

This paper introduces a new hybrid tool to classify manufacturing errors in production processes. Our tool is a hybrid procedure based on the combination of the parsimonious AHP method with a clustering method. Parsimonious AHP permits to use a much smaller number of pairwise comparisons with respect to classical-AHP. At the same time, the use of a clustering method, allows for the assignment of the alternatives on the basis of their closeness to each other and not on the basis of some reference profiles as happens in several other methods. Furthermore, some veto systems help the decision maker to better define how the errors belong to different classes in a participative way. The approach has been tested in a largest automotive plants in Italy. This approach has provided the company with a participative and robust theoretically funded tool that supports the understanding of the seriousness of the manufacturing errors. From a methodological point of view, the hybrid tools, for the first time, combine the newly developed parsimonious AHP with a clustering method. From a practical point of view, this paper has introduced a new tool to classify and improve the production quality of the processes to an extremely competitive sector.

Deep learning trends and future perspectives of web security and vulnerabilities

Web applications play a vital role in modern digital world. Their pervasiveness is mainly underpinned by numerous technological advances that can often lead to misconfigurations, thereby opening a way for a variety of attack vectors. The rapid development of E-commerce, big data, cloud computing and other technologies, further enterprise services are entering to the internet world and have increasingly become the key targets of network attacks. Therefore, the appropriate remedies are essential to maintain the very fabric of security in digital world. This paper aims to identify such vulnerabilities that need to be addressed for ensuring the web security. We identify and compare the static, dynamic, and hybrid tools that can counter the prevalent attacks perpetrated through the identified vulnerabilities. Additionally, we also review the applications of AI in intrusion detection and pinpoint the research gaps. Finally, we cross-compare the various security models and highlight the relevant future research directions.

Hybrid tools for improved removal and surface finish of metals and non-metals

Increasing productivity and surface quality in manufacturing is inevitable for the efficient use of resources. Precision smooth surfaces are generally achieved through sequential milling, grinding, fine-grinding, and polishing. This research aims to increase productivity through a shorter process chain. Hybrid tooling technology is presented where an elastic fine grinding tool is integrated with a conventional milling cutter or a grinding tool, in order to produce smooth surfaces on metal and non-metals. A hybrid milling cutter with an elastic tool could produce a low-waviness surface as compared to that of a milled-only surface. Surface roughness could be reduced by almost 50% using the hybrid milling tool on a steel workpiece. A monocrystalline silicon surface was machined with a hybrid grinding tool. Surface roughness of Ra ~ 100 nm could be obtained for hybrid tool in comparison to Ra ~ 200 nm with grinding only tool. The surface machined with a hybrid tool showed ductile mode cutting streaks, whereas, the ground surface was observed to be completely in brittle mode. Additionally, the hybrid tools also showed reduced machining vibrations due to the damping effect induced by the elastic tool member.

Hybrid machine learning approach for accurate and expeditious 3D scanning to enhance rapid prototyping reliability in orthotics using RSM-RSMOGA-MOGANN

Abstract This study aims to develop a multidisciplinary artificial hybrid machine learning (AHML) approach to reduce the scanning time (ST) of the human wrist and improve the accuracy of 3D scanning for anthropometric data collection. A systematic AHML approach was deployed to scan the human wrist distal end optimally using a portable SENSE 2.0 3D scanner. A central composite design (CCD) matrix was developed for three input variables; light intensity (LI = 12–20 W/m2), capture angle (CA = 10°–50°), and scanning distance (SD = 10–20 inches) for executing the experimental runs. For accuracy evaluation, the wrist perimeter on the distal end was checked using CREO Parametric software for wrist perimeter error (WPE). Various AHML tools were developed using: response surface methodology (RSM), multi-objective genetic algorithm RSM, and multi-objective genetic algorithm neural networking (MOGANN). The optimal process parameters recommended by the hybrid tools were experimentally validated for their prediction accuracy. The MOGANN approach combined with the Bayesian regularization algorithm (trainabr) provided the best mutual combination of optimal ST = 20.072 sec and WPE = 0.375 cm corresponding to LI = 12.001 W/m2, CA = 29.428°, and SD = 18.214 inch, with a significant percentage reduction of 55.83% in WPE. Executing 3D scanning of the human wrist over the optimized process parameters predicted by AHML tools will ensure the availability of precise scans for the rapid prototyping of customized orthotic devices in a reliable manner.

Платформенные экосистемы: накануне нового прорыва в области коммерциализации интеллектуальных прав

The aim of the work is to analyze the new investment capabilities that appeared as a result of the development of new platform ecosystems that form the economic circulation of digital intellectual assets of copyright. The productive ideas of the possible use of intellectual assets in financial engineering were voiced back in the 1980s, i.e., almost at the dawn of the emergence of financial engineering as a science itself. For example, in one of the first reference books on financial engineering and financial innovation, the growing use of legislation on intellectual property protection was mentioned as one of the aspects of financial engineering, the significance of which would grow in the coming years. The main stage of development is characterized by a massive transformation of business models and the transition to platform ecosystems, much more mobile and susceptible to organizational and financial innovations. A huge role in this process was played by the rapid development of information technology. The work shows that, by analogy with the period preceding the first transactions of the intellectual property secretion in the 1990s, there is currently a steady increase in the income of microstock platforms. In addition to ordinary investors, the ecosystem of microstocks includes an extensive contingent of the so-called creative investors – authors of digital images, video and sound files posted in the collections of such companies. Investors of this category are participants in the total intellectual capital successfully controlled by microstocks. The basics of the business of microstocks were laid back in the early 2000s, in fact, in the wake of the “dot-com”, when there was a stir of exciting, sound and literary content from the multimillion-dollar army of creators of the modern Internet. On the basis of ten-year observations of the portfolio of digital images, the study confirms that (1) the position of the author/investor on digital assets is not exposed to losses in the classical sense; (2) a correlation between the yield of the portfolio of digital images of the individual author and the profitability of the action of the microphotostock company, in the collection of which the portfolio is located, is absent. The results prove that the portfolio of digital intellectual assets of the author/investor can be used as a hedging element as part of a hybrid investment position that includes the shares of a microstock company. In addition, preliminary quantitative assessments of the possible contingent of investors who are interested in implementing the ideas of the proposed hybrid tool are made in the work. The study allows predicting the emergence of new ideas and investment capabilities in the field of commercial circulation of digital intellectual assets.

How to measure social health in the context of cognitive decline and dementia - A systematic review on instruments.

Background:Besides aspects of physical and mental health and quality of life aspects the concept of social health is getting increasing attention in dementia research. Current research has led to a new umbrella concept of social health and first studies show relationships between social health markers and cognitive decline and dementia. But so far, no general overview exists how to measure social health in empirical studies.Objective:The objective of this study therefore is to provide a systematic overview of instruments measuring aspects of social health and proposing a classification based on the new umbrella concept of social health.Methods:Following the PRISMA 2020 guidelines a systematic review was conducted. The online search covered the databases PubMed/MEDLINE, PsychINFO, CINAHL within a publication period from 1st January 2000 till 15th October 2020 for English publications. To classify instruments a new multidimensional framework of social health was used.Results:A total of 150 studies with 68 single instruments were included into the study. A broad range of study types was covered (i.e., feasibility studies, cross-national panel studies) from all over the world, with the majority of studies being conducted in the USA. Most of the described instruments consist of self-report measures, but also proxy and hybrid tools were found. The length of the instruments in terms of the number of single items ranged from 3-126 items, with a median length of 13 items. On the individual level of social health with the three domains capacities, autonomy and social participation we classified 42 instruments and on the social environment level with the three domains structure, functions and appraisal we classified 53 instruments. A large part of the identified instruments only addresses single aspects of social health and does not address the multidimensionality of the concept.Conclusion:A structured overview of measures related to the conceptual framework of social health can help develop appropriate interventions for people with dementia and improve the conditions for living well with dementia. Furthermore, the creation of new standardized and terminologically consistent measures of social health is one of the tasks for future research in the field of social health.

Performance Evaluation of Additively Manufactured and Hybrid Indexable Milling Tools in Machining of AISI4140+QT and Ti-6Al-4V

Abstract Additively manufactured (AM) cutting tools enable new design concepts in tool development concerning lightweight construction, damping behavior, and internal cutting fluid supply. So far, the impact of the layer-by-layer manufacturing technique onto the tool performance during machining of aerospace materials is widely unknown. In this study, the performance characteristics of AM indexable milling tools were investigated and compared to a conventionally manufactured tool of the same geometry. The AM tools were manufactured by laser powder bed fusion (LPBF) from a newly developed bainitic steel alloy. All tools were analyzed with two main focuses. Outside the machine tool, geometrical characteristics and mechanical properties were taken into account. Inside the machine tool, during roughing of AISI4140+QT (42CrMo4+QT) and Ti-6Al-4V (ASTM Ti-Grade 5), tool wear, cutting forces, surface roughness and vibration behavior were evaluated. Three tool concepts were compared, a conventional tool, an AM tool, and a hybrid tool with a conventionally manufactured preform and an AM head. Differences in the performance behavior could be traced back mainly to geometrical inaccuracies caused during postprocessing of the tools. Thus, LPBF-manufactured basic bodies seem to meet the requirements as applications for indexable milling tools. Based on this work, future investigations using AM cutting tools can be carried out assuming a negligible influence of the manufacturing technique onto the tool performance during machining.

Mitigating the impacts of COVID-19: failure mode and effect analysis and supply chain resilience (FMEA-SCR) combined model

PurposeThe coronavirus disease (COVID-19) pandemic generated a worldwide financial crisis by impacting several links of the supply chain, however companies can take advantage by quantitatively measuring the disruptive impacts.Design/methodology/approachThis study sought to develop the failure mode and effect analysis and supply chain resilience (FMEA-SCR), a hybrid tool developed using a potential failure mode and effect analysis (FMEA) applied to supply chain resilience (SCR) and taking into account the capability factors and business processes.FindingsIn order to validate, the proposed model was applied into two different organizational study cases: an university and a cooperative managing urban solid wastes with recyclable potential (MSWRP). Through the procedures described here any organization can understand and assess in a simplified way the impacts over their supply chain generated by such a crisis.Originality/valueThis study synthesizes three different procedures into a single method called FMEA-SCR, allowing organizations to understand and assess in a simplified way, the impacts over their supply chain generated by COVID-19. To this end, it brought together the studies developed by Rajesh and Ravi (2015) and Curkovic et al. (2015), on possible causes of disruptions in SC, the capability factors of Pettit et al. (2010) used by organizations to mitigate the effects of disruptions, besides Lambert's and Croxton (2005) business processes, thus weaving a method that allows organizations to visualize, analyze and classify the pandemic impacts over their supply chain.

A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans.

Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.

GENERATION OF EFFUSION HOLES ON ULTRA-HIGH TEMPERATURE ALLOY BY MICRO ELECTRO-DISCHARGE MACHINING PROCESS

Fabrication of surface features like effusion holes on ultra-high temperature (UHT) alloys with micro electro-discharge machining ([Formula: see text]-EDM) is a very convolute strategy. Due to the exceptional mechanical properties and resistance to ease brittle transformation at elevated temperatures, UHT alloys are being widely used in hot sections of aero engines. This research tailored the existing capabilities of [Formula: see text]-EDM setup by employing a copper-tungsten (Cu-W) hybrid tool electrode which has been rarely reported. This research addresses investigations, predictive modeling, and optimization during [Formula: see text]-EDM of UHT alloy, namely Inconel 718. For experimentation, the factors like inter-electrodes gap (IEG), current ([Formula: see text]), and pulse on/off time ([Formula: see text]on, [Formula: see text]off) were considered as input variables to analyze the responses like overcut (OC), tool wear rate (TWR), material removal rate (MRR), and diametric alterations in between the top and bottom shape of holes’ feature (DE). The multi-objective desirability-based artificial bee colony optimization (MOABC) projected optimal parametric settings of 5.276 A, 16.389 [Formula: see text]s, 1.77 [Formula: see text]s, and 50 [Formula: see text]m for [Formula: see text], [Formula: see text]on, [Formula: see text]off, and IEG, respectively. These settings provide MRR, TWR, OC, and DE solutions of 0.001479, −0.00038, 0.030239, and 0.047048 mm, respectively. Further, the confirmation test has been performed to validate the optimal solution. The results supported the ideal settings with error values of 0.03177, 1.31578, 0.05863, and 0.04704 for MRR, TWR, OC, and DE, respectively. Distinctive diagnostic tests assessed by several statistical parameters ([Formula: see text], [Formula: see text], [Formula: see text]2, AD-P values) confirmed that the accuracy of developed models for prediction of various performance characteristics has high degree of resemblance with experimental results and the adequacy as well as accuracy of models is also demonstrated. Moreover, the surface morphology has also been scrutinized by microscopical observation to perceive the consequences of the electro-discharge phenomenon on both electrodes. Overall, the research comprises experimentation, surface integrity study, and parametric optimization followed by a confirmation test to perform the betterment of the current [Formula: see text]-EDM approach.

The effect of various harvesting techniques and drying methods on the yield and quality of Liberoid coffee

In the upstream process, the dominant factor determines the quality of green coffee beans. Harvest and post-harvest techniques can influence the physical, chemical, and taste qualities by up to 60%. The goal of this study was to see how different harvesting techniques and drying methods affected the yield and quality of Liberoid coffee green beans grown in a dry process. From June to August 2022, the research was carried out at the Agricultural Science Park, Indonesian Industrial and Beverage Crops Research Institute Sukabumi West Java. The study used a factorial randomized block design, with Liberica and Excelsa coffee as the first factor, various harvesting techniques (cherry and rainbow fruit harvest) as the second factor, and the drying method as the third. There were eight treatment combinations, each of which was repeated three times. According to the findings, all Liberica and Excelsa coffee varieties found the highest yield in the cherry fruit. Furthermore, when compared to direct drying, the decrease in water content and weight loss/day is faster and more evenly distributed when using Hybrid tools. A berry provided the best physical quality and chemical content.