Effect of welding tool geometry on microstructure and hardness in Friction Stir Welded Plates

Friction Stir Welding is a solid state welding process for joining aluminum and other metallic alloys where the metal is not melted during the process and the original metal characteristic remains unchanged as far as possible. It was invented in 1991 in The Welding Institute (TWI), United Kingdom to substitute conventional welding of aluminum alloy. Friction Stir Welding can be use in shipbuilding marine, aerospace industry and railway industries. The welding parameters and tool pin profile play a major role in defining the weld quality. This report covers the theoretical background of friction stir welding, literature reviews on related research works, implemented methodology, and current results. The objective of the project is to study the effect of welding tool geometry on the microstructure and hardness of welded plates. The project will focus on three types of pin design which are straight cylindrical, threaded cylindrical and tapered. The tools are fabricated using CNC Machine and undergo heat treatment. Two plates of aluminum alloy work piece were set-up in butt joint configuration and clamped rigidly during welding operation. The work pieces are welded using different type of welding tool pin profile and different sets of parameters. Welded work pieces that have been welded by the designed tools were undergo lab testing which are hardness and microstructure test in order to rectify the difference of the weld results. From this project, it can be observed that different geometry of tool will produce different weld result

The cranking formula and the spurious behaviour of the mass parameters

We discuss some aspects of the approach of the mass parameters by means of the simple cranking model. In particular, it is well known that the numerical application of this formula is often subject to ambiguities or contradictions. It is found that these problems are induced by the presence of two derivatives in the formula. To overcome these problems, we state a useful ansatz and we develop a number of simple arguments which tend to justify the removal of these terms. As soon as this is done, the formula becomes simpler and easier to interpret. In this respect, it is shown how the shell effects affect the mass parameters. A number of numerical tests help us in our conclusions.Comment: version 3 corrigendum of the ansatz of section V, corrigendum of the legend of Fig3. Submission = text file + 5 figure

Single particle calculations for a Woods-Saxon potential with triaxial deformations, and large Cartesian oscillator basis

We present a computer program which solves the Schrodinger equation of the stationary states for an average nuclear potential of Woods-Saxon type. In this work, we take specifically into account triaxial (i.e. ellipsoidal) nuclear surfaces. The deformation is specified by the usual Bohr parameters. The calculations are carried out in two stages. In the first, one calculates the representative matrix of the Hamiltonian in the cartesian oscillator basis. In the second stage one diagonalizes this matrix with the help of subroutines of the EISPACK library. If it is wished, one can calculate all eigenvalues, or only the part of the eigenvalues that are contained in a fixed interval defined in advance. In this latter case the eigenvectors are given conjointly. The program is very rapid, and the run-time is mainly used for the diagonalization. Thus, it is possible to use a significant number of the basis states in order to insure a best convergence of the results.Comment: no figures, but tbles in separate pdf file

Low-lying quadrupole collective states of the light and medium Xenon isotopes

Collective low lying levels of light and medium Xenon isotopes are deduced from the Generalized Bohr Hamiltonian (GBH). The microscopic seven functions entering into the GBH are built from a deformed mean field of the Woods-Saxon type. Theoretical spectra are found to be close to the ones of the experimental data taking into account that the calculations are completely microscopic, that is to say, without any fitting of parameters.Comment: 8 pages, 4 figures, 1 tabl

Enhanced Neurologic Concept Recognition using a Named Entity Recognition Model based on Transformers

Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks to One based on Bidirectional Encoder Representation from Transformers. Models Were Evaluated for Accuracy of Text Span Identification on Three Text Corpora: Physician Notes from an Electronic Health Record, Case Histories from Neurologic Textbooks, and Clinical Synopses from an Online Database of Genetic Diseases. Both Models Performed Best on the Professionally-Written Clinical Synopses and Worst on the Physician-Written Clinical Notes. Both Models Performed Better When Signs and Symptoms Were Represented as Shorter Text Spans. Consistent with Prior Studies that Examined the Recognition of Diseases and Drugs, the Model based on Bidirectional Encoder Representations from Transformers Outperformed the Model based on Convolutional Neural Networks for Recognizing Signs and Symptoms. Recall for Signs and Symptoms Ranged from 59.5% to 82.0% and Precision Ranged from 61.7% to 80.4%. with Further Advances in NLP, Fully Automated Recognition of Signs and Symptoms in Electronic Health Records and the Medical Literature Should Be Feasible