Gas tungsten arc welding of NiTi shape memory alloy

Shape memory alloys are characterized by the ability of recovering their initial shape after being deformed and by superelasticity. Since the discovery of these alloys, a new field of interest emerged not only for the scientific community but also to many industries. However, these alloys present poor machinability which constitute a constrain in the design of complex components for new applications. Thus, the demand for joining techniques able to join these alloys without compromising their properties became of great importance to enlarge the complexity of existing applications. Literature shows that these alloys are joined mainly using laser welding. In the present study, similar NiTi butt joints, were produced using TIG welding. The welds were performed in 1.5 mm thick plates across the rolling direction. A special fixture and gas assist device was designed and manufactured. Also a robot arm was adapted to accommodate the welding torch to assure the repeatability of the welding parameters. Welds were successfully achieved without macroscopic defects, such as pores and distortions. Very superficial oxidation was seen on the top surface due to insufficient shielding gas flow on the weld face. The welded joints were mechanically tested and structurally characterized. Testing methods were used to evaluate macro and microstructure, as well as the phase transformation temperatures, the mechanical single and cyclic behaviour and the shape recovery ability. Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), microhardness measurements were techniques also used to evaluate the welded joints. A depletion in Ni in the fusion zone was seen, as well as a shift in Ms temperature. For strain values of 4% the accumulated irrecoverable strain was of about 30% and increased with the strain imposed during cycling. Nevertheless, a complete recovery of initial shape was observed when testing the shape memory effect on a dedicated device that introduces a deformation of 6.7%. That is, the welding procedure does not remove the ability of the specimens to recover their initial shape.

Cognitive assessment of topological movement Patterns and direction turns: An influence of scale

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.