Investigating the Effects of SiC Abrasive Particles on Friction Element Welding

The growing demands on reducing the harmful emissions from automobiles have forced automakers to reduce the weight of the vehicle. The increasing demands on improving the fuel economy also has challenged automotive manufacturers to make the vehicle as lightweight as possible. However, the challenge is also to ensure that the vehicle meets safety standards. For the vehicle to meet these standards, it needs to be of adequate strength as well. Automotive manufacturers have adopted a strategy of using multi-material construction to achieve the target. But with multi-material construction comes the requirement of advanced joining techniques that are capable of joining dissimilar materials. The requirement of the advanced techniques is due to the difference in physical and chemical properties of the dissimilar materials to be joined. The conventional methods are either unable to join the dissimilar material or form a joint with defects and of poor quality. Friction Element Welding (FEW) is one of the advanced joining techniques capable of joining dissimilar materials effectively. The process is based on the concepts of friction welding technique where the materials to be joined are heated to the temperature below their melting temperatures. In FEW, a friction element is used to form a friction weld. It has been found that the FEW process although has a low processing time, it is still higher than a few of its competitors. Most of the processing time of the FEW process is taken by the second step of the process, i.e., the cleaning step. Cleaning step parameters are the dominating factors that affect the processing time of the process. The cleaning step involves removing the coatings/impurities present on the bottom sheet of the materials to be joined while also pre- iii heating the friction element. The removal of coatings/impurities, however, can be accelerated with the use of abrasive particles. This study focuses on the effect of abrasive particles on the cleaning time and processing time of FEW. Silicon carbide abrasive particles have a high hardness and provide higher wear rates. The higher wear rates promote the wearing off of coatings from the surface of the materials. Silicon carbide abrasive particles were placed in a pre-drilled pocket in an aluminum top sheet. Design of Experiments (DOE) involved two levels of pocket size, pocket depth, abrasive particle size, and volume fraction of abrasives. The results show that abrasive particle size and volume fraction of abrasive particles were the dominating factors in determining the cleaning step time and overall processing time. Lower particle size and volume fraction of abrasives resulted in a reduction of cleaning time and processing time. Cross-tension strength (CTS) tests were performed, followed by microscopy analysis and hardness testing to study the effect of abrasives on the joint quality. The best case was observed for 6 mm pocket size, 0.2 mm pocket depth, 5 μm abrasive particle size, and 50% volume fraction of abrasives. The best case with abrasives was compared with the FEW sample which does not involve pocket and abrasives. The comparison showed that the inclusion of abrasives results in a reduction in cleaning time by 39.93% and processing time by 14.28%. The CTS of the joints formed with abrasives was slightly higher than the case without abrasives. Both the cases showed a button pull-out failure when subjected to CTS loading conditions. Microstructural analysis showed a presence of hard SiC and wider martensite phase, which is a probable reason for an increase in the joint strength for the joints that involved iv abrasives. The Microhardness tests further supported the CTS results. For the joints involving abrasives, a marginally higher hardness was observed along the cross-section. The significance of this study lies in the opportunities to reduce the processing time of the joining process using abrasive particles

Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space

This paper proposes a novel framework for multi-group shape analysis relying on a hierarchical graphical statistical model on shapes within a population.The framework represents individual shapes as point setsmodulo translation, rotation, and scale, following the notion in Kendall shape space.While individual shapes are derived from their group shape model, each group shape model is derived from a single population shape model. The hierarchical model follows the natural organization of population data and the top level in the hierarchy provides a common frame of reference for multigroup shape analysis, e.g. classification and hypothesis testing. Unlike typical shape-modeling approaches, the proposed model is a generative model that defines a joint distribution of object-boundary data and the shape-model variables. Furthermore, it naturally enforces optimal correspondences during the process of model fitting and thereby subsumes the so-called correspondence problem. The proposed inference scheme employs an expectation maximization (EM) algorithm that treats the individual and group shape variables as hidden random variables and integrates them out before estimating the parameters (population mean and variance and the group variances). The underpinning of the EM algorithm is the sampling of pointsets, in Kendall shape space, from their posterior distribution, for which we exploit a highly-efficient scheme based on Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted hierarchical model to perform (1) hypothesis testing for comparison between pairs of groups using permutation testing and (2) classification for image retrieval. The paper validates the proposed framework on simulated data and demonstrates results on real data.Comment: 9 pages, 7 figures, International Conference on Machine Learning 201

Nonparametric neighborhood statistics for MRI denoising

technical reportThis paper presents a novel method for denoising MR images that relies on an optimal estimation, combining a likelihood model with an adaptive image prior. The method models images as random fields and exploits the properties of independent Rician noise to learn the higher-order statistics of image neighborhoods from corrupted input data. It uses these statistics as priors within a Bayesian denoising framework. This paper presents an information-theoretic method for characterizing neighborhood structure using nonparametric density estimation. The formulation generalizes easily to simultaneous denoising of multimodal MRI, exploiting the relationships between modalities to further enhance performance. The method, relying on the information content of input data for noise estimation and setting important parameters, does not require significant parameter tuning. Qualitative and quantitative results on real, simulated, and multimodal data, including comparisons with other approaches, demonstrate the effectiveness of the method

Exploring Community Participation in Decision-Making Processes in Lainya County, South Sudan

Community participation has no single definition, but in simple terms it can be defined as a process of empowering the community or citizens by involving them in decision-making processes at all levels of government being from County, Payam and Bomas on all issues of concern that affect them politically, socially and economically. The main aim of this study is to investigate and identify the nature of and extent to which communities are given opportunities to participate in decision-making processes for effective service delivery in Lainya County. The main objectives of the study were to identify the obstacles to community participation in decision-making processes and how communities can be encouraged to participate effectively in decision-making, to meet their needs and interests. It further examines the extent to which communities play a role in promoting effective service delivery through participating in decision-making processes in Lainya County South Sudan. In this study, interviews, focus group discussions and observations were used as the main instruments for data collection on the issue of community participation as a tool for effective service delivery in Lainya County. These instruments are used to determine the extent to which communities are involved in decision-making processes to meet their needs and interests for effective service delivery. Data was collected from County officials comprising of Administrators, Chiefs, Women Associations Youth Associations and opinion leaders using both structured and unstructured interviews, focus group discussions and observations. Emerging from the study is that community participation is an integral part of the County developmental planning process. The study findings suggest that, public meetings, hearings, community workshops and seminars were the main mechanisms for community participation being used by the administrators. The study therefore, recommended among other things that; community participation be encouraged to promote community involvement in decision-making processes for effective service delivery. It also recommended that community participation and involvement be encouraged to initiate community development as it enhances program sustainability and ownership. The study further recommended that the top-down approach to decision making be replaced by bottom-up approaches, which emphasizes seeking communities’ ideas first before any developmental plans take place or are implemented. This study will significantly contribute to effective service delivery in Lainya County in the Republic of South Sudan