Recycling and Recharging of Supreme Garnet in Abrasive Waterjet Machining

Abrasive waterjet (AWJ) technology is widely used for cutting technical materials, cleaning contaminated surfaces, polishing hard-to-machine materials, etc. However, its main disadvantage is high cutting cost. Therefore, recycling and recharging abrasives used in the AWJ cutting process have been subject to many studies. This chapter presents a study on the recycling and recharging of Supreme garnet (or IMC garnet) in abrasive waterjet machining. In this study, the reusability of the recycled and recharged garnet was explored. Also, the cutting performance and the cutting quality of the recycled and recharged abrasive were investigated. Finally, the optimum particle size for recycling and recharging was found

A study on multi-criteria decision-making in powder mixed electric discharge machining cylindrical shaped parts

In life as well as in engineering, many times, it is necessary to choose the best option among many different options. That will be more difficult when the criteria given for the selection contradict each other. For example, when external cylindrical grinding, the minimum surface roughness requirement necessitates a small depth of cut and feed rate. The material removal rate will be reduced in this case, and this requirement will conflict with the maximum material removal rate requirement. To solve the above problem, a very useful tool is multi-criteria decision-making (MCDM). In this paper, for the first time, MCDM results for powder mixed discharge machining (PMEDM) cylindrical parts of SKD11 tool steel with copper electrodes have been presented. In this work, eighteen experiments with the L18 (16×53) design using the Taguchi method were conducted. Six main input process parameters include the powder concentration, the pulse current, the servo voltage, the pulse on time, and the pulse off time. To select an alternative that simultaneously ensures two criteria including minimum surface roughness (RS) and maximum material removal speed (MRS), four different MCDM methods including MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), TOPSIS (Technique for order of preference by similarity to ideal solution), and EAMR (Area-based Method of Ranking) and two methods of criteria weight calculation including MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were selected. The results of MCDM when PMEDM SKD11 tool steel cylindrical parts with two methods for weight determination and four methods for solving MCDM problem were evaluated. In addition, the best alternative to ensure simultaneous minimum RS and maximum MRS was proposed

Experimental Investigation of White Layer Thickness on EDM Processed Silicon Steel Using ANFIS Approach

Since the white layer thickness influences the surface quality of the machined specimens using electrical discharge machining process, the prediction of such parameter is highly important in the present scenario. Adaptive network based fuzzy inference system based white layer thickness prediction on machining processed silicon steel has been attempted in the present study. Three machining process parameters such as open circuit voltage, peak current and duty factor have been utilized for the training purpose owing their importance on determining white layer thickness. The accuracy of the prediction has been analyzed by comparing the predicted values from the architecture testing with the real time measured values. From the experimental results, it has been found that the developed adaptive network based fuzzy inference system can predict the average white layer thickness in an efficient way with accuracy of 96.8%. It has also been observed that the electrical process parameters have highly contributed on determining average white layer thickness

Cost optimization of two-stage helical gearboxes with second stage double gear-sets

In practice, the cost of a gearbox plays a very important role in the trade. Therefore, reducing the cost of gearboxes is an important task not only when manufacturing the gearboxes but also when designing them. In order to reduce the cost of a gearbox, there are many solutions in which determining the optimal partial gear ratios of a gearbox is an effective measure. This is because it not only the size, the mass but also the cost of a gearbox depends greatly on the partial gear ratios. This work presents a method for calculating the cost function of two-stage helical gearboxes with second-stage double gear-sets based on the mass of the components that construct the gearbox. The cost objective function is minimized to achieve the optimal transmission ratios. Furthermore, screening experiments are carried out with nine important input parameters that have significant effects on the optimum transmission ratio of the second stage. These parameters are the total gearbox ratio, the coefficient of wheel face width of the first stage, coefficient of wheel face width of the second stage, the allowable contact stress of the first stage, the allowable contact stress of the second stage, the output torque, the cost of gearbox housing, the cost of gears, and the shaft cost. The experimental results of were analysed by using the Analysis of Variance (ANOVA) method with the help of Minitab 19 software. The results demonstrate that the effective weight of the input parameters and their interactions on the output response was investigated. Also, a regression model for computing the optimal transmission ratio of the second stage was proposed. This brings significance not only in the design process but also in manufacturing since the gearbox cost can decreas

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

Background The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95$7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the $24·8 billion (95$43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and$37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained