Optical and analytical electron microscopy of ductility-dip cracking in Ni-base filler metal 52 -- Initial studies

Microcharacterization studies were performed on weld-metal microstructures of a Ni-base filler metal. Specimens were taken from the fusion zone and the weld-metal heat-affected zone of transverse- and spot-Varestraint welds. The filler metal was first deposited onto a steel substrate by hot-wire, gas tungsten arc welding before specimen removal. Optical microscopy indicates the crack morphology is intergranular and is along high-angle, migrated grain boundaries. At low magnifications, scanning electron microscopy reveals a relatively smooth fracture surface. However, at higher magnifications the grain faces exhibit microductility. Analytical electron microscopy reveals high-angle, migrated grain boundaries decorated with MC (Ti, Cr) and M{sub 23}C{sub 6} (Cr, Ni, Fe) precipitates ranging from 10 to 200 n. Auger electron spectroscopy of pre-strained Gleeble specimens fractured in situ revealed internal ductility-dip cracks decorated with magnesium aluminate (MgAl{sub 2}O{sub 4}) spinel particles (1,000 nm)

Method and Apparatus for In-Process Sensing of Manufacturing Quality

A method for determining the quality of an examined weld joint comprising the steps of providing acoustical data from the examined weld joint, and performing a neural network operation on the acoustical data determine the quality of the examined weld joint produced by a friction weld process. The neural network may be trained by the steps of providing acoustical data and observable data from at least one test weld joint, and training the neural network based on the acoustical data and observable data to form a trained neural network so that the trained neural network is capable of determining the quality of a examined weld joint based on acoustical data from the examined weld joint. In addition, an apparatus having a housing, acoustical sensors mounted therein, and means for mounting the housing on a friction weld device so that the acoustical sensors do not contact the weld joint. The apparatus may sample the acoustical data necessary for the neural network to determine the quality of a weld joint

Quantum optics in the phase space - A tutorial on Gaussian states

In this tutorial, we introduce the basic concepts and mathematical tools needed for phase-space description of a very common class of states, whose phase properties are described by Gaussian Wigner functions: the Gaussian states. In particular, we address their manipulation, evolution and characterization in view of their application to quantum information.Comment: Tutorial. 23 pages, 1 figure. Updated version accepted for publication in EPJ - ST devoted to the memory of Federico Casagrand

Optical and analytical electron microscopy of ductility-dip cracking in Ni-base filler metal 52 -- Initial studies

Microcharacterization studies were performed on weld-metal microstructures of a Ni-base filler metal. Specimens were taken from the fusion zone and the weld-metal heat-affected zone of transverse- and spot-Varestraint welds. The filler metal was first deposited onto a steel substrate by hot-wire, gas tungsten arc welding before specimen removal. Optical microscopy indicates the crack morphology is intergranular and is along high-angle, migrated grain boundaries. At low magnifications, scanning electron microscopy reveals a relatively smooth fracture surface. However, at higher magnifications the grain faces exhibit microductility. Analytical electron microscopy reveals high-angle, migrated grain boundaries decorated with MC (Ti, Cr) and M{sub 23}C{sub 6} (Cr, Ni, Fe) precipitates ranging from 10 to 200 n. Auger electron spectroscopy of pre-strained Gleeble specimens fractured in situ revealed internal ductility-dip cracks decorated with magnesium aluminate (MgAl{sub 2}O{sub 4}) spinel particles (1,000 nm)

Multi-objective design and optimization of hard magnetic alloys free of rare earths

This work demonstrates a novel approach to design and optimization of rare-earth free magnetic materials for targeted properties by effectively using various computational and statistical tools. From the open literature, we defined the alloying elements and bounds of their concentrations to develop a new system of Alnico alloys. Initial compositions of candidate alloys were generated using a quasi-random sequence generation algorithm. Response surface methodology approach was used to develop surrogate models to efficiently link alloy chemistry with desired macroscopic properties for these multi-component systems. The most accurate meta-models were used for multi-objective optimization of desired properties by utilizing various evolutionary approaches. Various statistical tools and pattern recognition techniques were used to determine patterns and correlations within the created dataset. Pareto-optimized candidate alloys were experimentally validated and used to improve the accuracy of the response surface generation used by the multi-objective optimizer to find the next generation of Pareto-optimal alloys. Results over the cycles show significant experimentally verified improvement in the properties of these alloys