OPTIMIZATION OF CUTTING CONDITIONS FOR SUSTAINABLE MACHINING OF SINTERED POWDER METAL STEELS USING PCBN AND CARBIDE TOOLS

Powder metals are becoming a popular choice in the automotive and other manufacturing industries because of their ability to meet wide ranging product functional requirements without compromising the performance of the product. They offer various advantages, including weight reduction, near net-shape processing capability, and their ability to be sintered to achieve desired properties in the end-product. However, in order to satisfy the product design requirements during manufacturing, they need to be machined to the required tolerances. Machining of powder metals is quite different to machining of traditional metals because of their specific properties, including porosity. This thesis work deals with the finish machining of powder metal steels in automotive applications, for increased tool-life/reduced tool-wear. Tool-life is affected by a variety of factors such as tool grade selection, tool coating, cutting conditions and tool geometry including cutting edge geometry. This work involves optimization of cutting conditions for plunge cutting and boring operations of automotive powder metal components using PCBN and carbide tools. The cycle time of the process introduces an additional constraint for the optimization model along with the tool-wear criterion. Optimized cutting conditions are achieved for maximum tool-life

Using Internal Bar Strength as a Key Indicator for Trading Country ETFs

This report aims to investigate the effectiveness of using internal bar strength (IBS) as a key indicator for trading country exchange-traded funds (ETFs). The study uses a quantitative approach to analyze historical price data for a bucket of country ETFs over a period of 10 years and uses the idea of Mean Reversion to create a profitable trading strategy. Our findings suggest that IBS can be a useful technical indicator for predicting short-term price movements in this basket of ETFs

Cross-Domain Consumer Review Analysis

The paper presents a cross-domain review analysis on four popular review datasets: Amazon, Yelp, Steam, IMDb. The analysis is performed using Hadoop and Spark, which allows for efficient and scalable processing of large datasets. By examining close to 12 million reviews from these four online forums, we hope to uncover interesting trends in sales and customer sentiment over the years. Our analysis will include a study of the number of reviews and their distribution over time, as well as an examination of the relationship between various review attributes such as upvotes, creation time, rating, and sentiment. By comparing the reviews across different domains, we hope to gain insight into the factors that drive customer satisfaction and engagement in different product categories

Study of sociodemographic factors and their relationship with the morbidity among adolescent girls of Pune slum area

Background: Socio-demographic factors such as age, gender, socioeconomic status, and education can influence health and nutritional outcomes, especially in adolescent girls. Aims and objectives of research work was to study of sociodemographic factors and their relationship with the morbidity among adolescent girls residing in slum areas of Pune region.Methods: A cross-sectional study of the urban slum population from October 2011 to September 2012. Adolescent girls of the age group 10-19 years who gave consent and are living in a slum area for more than 6 months were enrolled. Information on health status including morbidity was collected by health examination.Results: Majority of the adolescent girls (54.02%) had to age between 14-16 years. Majority of the adolescent girls (96.4%) were studying and were underweight (41.07%; of which majority 59.78% were in the age group 14-16years). Out of the 41.07% underweight girls, the majority (84.78%) had anaemia (p<0.05). A total 26.34% study subjects were stunted and 12.95% were thin (for both, commonly reported in the age group of 14-16 years). Majority of the girls (48.2%) belonged to class III SES. In class III socioeconomic status group mild, moderate and severe anaemia was reported in 50.45%, 41.51%, and 36.36% cases, respectively.Conclusions: Among the studied sociodemographic factors lower socioeconomic status and overcrowding had an impact on the morbidities. However, we noted a significant impact of morbidity in underweight girls

Integrating Gender into the Climate-Smart Village Approach of Scaling out Adaptation Options in Agriculture

As a step towards building climate resilience, a USAID funded project aimed at scaling out climate-smart agriculture through the CSV approach in Madhya Pradesh in India, is providing an enabling platform to women farmers to manage and lead climate change adaptation and mitigation interventions in their villages. This info note presents preliminary information on each of the major steps of a CSV approach and its components and linking them with gender and CSA. It focuses on the initial findings of a baseline assessment, the process of CSV formation and the initial output of CSA implementation (for Rabi/monsoon season) with due consideration of gender dynamics in agriculture

Pancreatic anastomosis training models:current status and future directions

Postoperative pancreatic fistula (POPF) is a major cause of morbidity and mortality after pancreatoduodenectomy (PD), and previous research has focused on patient-related risk factors and comparisons between anastomotic techniques. However, it is recognized that surgeon experience is an important factor in POPF outcomes, and that there is a significant learning curve for the pancreatic anastomosis. The aim of this study was to review the current literature on training models for the pancreatic anastomosis, and to explore areas for future research. It is concluded that research is needed to understand the mechanical properties of the human pancreas in an effort to develop a synthetic model that closely mimics its mechanical properties. Virtual reality (VR) is an attractive alternative to synthetic models for surgical training, and further work is needed to develop a VR pancreatic anastomosis training module that provides both high fidelity and haptic feedback

HP-Multigrid for RANS-Modeled Turbulent Flows in a High-Order Flux Reconstruction Framework

High-order (HO) methods are of concerted academic and industrial interest in recent years due to their improved accuracy and their capability to deal with complex geometries [1]. Of particular note is the flux reconstruction method [2], which unifies several existing HO schemes into a simpler and computationally efficient approach that has been shown to work on all element types (including simplices) in two and three dimensions. There is considerable interest to apply HO methods to industrially relevant problems. At the same time, accurate and robust turbulence modeling techniques are essential for reliable results. As outlined in the National Aeronautics and Space Administration's CFD vision 2030 study, Large Eddy Simulation (LES) still remains impractical for industrial cases therefore, Reynolds-Averaged Navier Stokes (RANS) and hybrid RANS-LES methods hold high significance in the near future [4]. Achieving fastest convergence to steady-state is important in the context of RANS simulations, for which several convergence acceleration techniques are being investigated. Multigrid methods are an industry standard in Finite Volume (FV) type schemes and are increasingly being applied to HO methods in the form of p-multigrid [22]. They exploit the polynomial hierarchy of the solution space to represent errors on a coarser resolution. A natural extension of this idea is hp-multigrid, where we can augument the classical h-multigrid to the polynomial hierarchy [23]. In this paper we illustrate the application of high-order flux reconstruction methods to simulate compressible, turbulent flows on body-fitted meshes. The case in point is the turbulent flow over a flat plate [24]. Turbulence is modeled through the RANS approach using the one-equation Spalart-Allmaras model. Grid-coarsening for the h-levels is performed by removing every other line in each direction from the original mesh. The system is driven to a steady-state solution using hp-multigrid convergence acceleration with local time-stepping using an explicit Runge-Kutta time-marcher. We show that the augumented h-multigrid is highly effective with a 10X to 24X drop in convergence time