title: The mechanical behaviour of an ultrafine grained Ti-47Al-2Cr (at%) alloy in tension and compression and at different temperatures

authors: Nadakuduru, Vijay Navaratna

subjects: ultrafine grained

abstract: A bulk ultrafine grained (UFG) Ti-47Al-2Cr (at%) alloy has been produced using a powder metallurgy process that combines high energy mechanical milling (HEMM) of a mixture of Ti, Al and Cr powders to produce a Ti/Al/Cr composite powder and hot isostatic pressing (HIP) of the composite powder compact. The purpose of the present study is to determine the mechanical behaviour of the alloy in tension and compression at room temperature (RT) and elevated temperatures, and also to compare the compression behaviour of the material with its tensile behaviour. It has been found that due to the residual pores, lack of full level interparticle bonding and high oxygen content (0.87wt%) in the consolidated samples, the UFG TiAl based alloy has a very low room temperature tensile fracture strength of 100 MPa and shows no tensile ductility. However these microstructural defects and high oxygen content have much less significant effect on the room temperature compressive mechanical properties, and the alloy shows a high compressive yield strength of  1410 MPa, and some ductility (plastic strain to fracture  4%). At elevated temperatures of 800oC and above, the alloy shows high tensile and compressive ductility as demonstrated by 75% tensile elongation to fracture and no cracking in upset forging with a height reduction of 50% at 900oC. The yield strength of the alloy at 900oC is 55 MPa in tension and  33 MPa in compression, both of which are lower than those of coarse grained TiAl based alloys with similar compositions at 900oC. This is due to a higher creep rate of the UFG alloy caused by the small grains. The good formability of the UFG TiAl based alloy as reflected by the lower critical temperature above which the alloy becomes highly formable indicates that the material can be used as a suitable precursor for secondary thermomechanical processing and super-plastic forming.

text: The mechanical behaviour of an ultrafine grained Ti-47Al-2Cr (at%) alloy in tension and compression and at different temperatures A bulk ultrafine grained (UFG) Ti-47Al-2Cr (at%) alloy has been produced using a powder metallurgy process that combines high energy mechanical milling (HEMM) of a mixture of Ti, Al and Cr powders to produce a Ti/Al/Cr composite powder and hot isostatic pressing (HIP) of the composite powder compact. The purpose of the present study is to determine the mechanical behaviour of the alloy in tension and compression at room temperature (RT) and elevated temperatures, and also to compare the compression behaviour of the material with its tensile behaviour. It has been found that due to the residual pores, lack of full level interparticle bonding and high oxygen content (0.87wt%) in the consolidated samples, the UFG TiAl based alloy has a very low room temperature tensile fracture strength of 100 MPa and shows no tensile ductility. However these microstructural defects and high oxygen content have much less significant effect on the room temperature compressive mechanical properties, and the alloy shows a high compressive yield strength of  1410 MPa, and some ductility (plastic strain to fracture  4%). At elevated temperatures of 800oC and above, the alloy shows high tensile and compressive ductility as demonstrated by 75% tensile elongation to fracture and no cracking in upset forging with a height reduction of 50% at 900oC. The yield strength of the alloy at 900oC is 55 MPa in tension and  33 MPa in compression, both of which are lower than those of coarse grained TiAl based alloys with similar compositions at 900oC. This is due to a higher creep rate of the UFG alloy caused by the small grains. The good formability of the UFG TiAl based alloy as reflected by the lower critical temperature above which the alloy becomes highly formable indicates that the material can be used as a suitable precursor for secondary thermomechanical processing and super-plastic forming.

year: 2011

title: Learning discrete and Lipschitz representations

authors: Gouk, Henry

subjects: Machine Learning

abstract: Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to solving this problem typically rely on training neural networks using labelled training data. In order to construct an accurate embedding function that will generalise to data not seen during training, one must either gather a very large training dataset, or adequately bias the learning process. This thesis focuses on the task of incorporating new inductive biases into the representation learning paradigm by constraining the set of functions that a learned feature extractor can come from.

The first part of this thesis investigates how one can learn a mapping that changes slowly with respect to its input. This is first addressed by deriving the Lipschitz constant of common feed-forward neural network architectures, and subsequently demonstrating how this constant can be constrained during training. Following this, it is investigated how a similar goal can be accomplished when one assumes that the inputs of interest lie near a low dimensional manifold embedded in a high dimensional vector space. This results in an algorithm that takes advantage of an empirical analog to the Lipschitz constant. Experimental results show that these methods have favourable performance compared to other methods commonly used for imposing inductive biases on neural network learning algorithms.

In the second part of this thesis, methods for extracting representations using decision tree models are developed. The first method presented is a problem transformation approach that allows one to reuse existing tree induction techniques. The second approach shows how one can incrementally construct decision trees using gradient information as the source of supervision, allowing one to use an ensemble of decision trees as a layer in a neural network. The experimental results indicate that these approaches improve the performance of representation learning on tabular data across multiple tasks.

text: Learning discrete and Lipschitz representations Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to solving this problem typically rely on training neural networks using labelled training data. In order to construct an accurate embedding function that will generalise to data not seen during training, one must either gather a very large training dataset, or adequately bias the learning process. This thesis focuses on the task of incorporating new inductive biases into the representation learning paradigm by constraining the set of functions that a learned feature extractor can come from.

The first part of this thesis investigates how one can learn a mapping that changes slowly with respect to its input. This is first addressed by deriving the Lipschitz constant of common feed-forward neural network architectures, and subsequently demonstrating how this constant can be constrained during training. Following this, it is investigated how a similar goal can be accomplished when one assumes that the inputs of interest lie near a low dimensional manifold embedded in a high dimensional vector space. This results in an algorithm that takes advantage of an empirical analog to the Lipschitz constant. Experimental results show that these methods have favourable performance compared to other methods commonly used for imposing inductive biases on neural network learning algorithms.

In the second part of this thesis, methods for extracting representations using decision tree models are developed. The first method presented is a problem transformation approach that allows one to reuse existing tree induction techniques. The second approach shows how one can incrementally construct decision trees using gradient information as the source of supervision, allowing one to use an ensemble of decision trees as a layer in a neural network. The experimental results indicate that these approaches improve the performance of representation learning on tabular data across multiple tasks.

year: 2019

title: Maori and epilepsy: Personal perceptions of the cause, treatment and consequences of epilepsy by Maori in the Bay of Plenty

authors: Simonsen, Kiri

subjects: Maori

abstract: This paper discusses the perceptions of epilepsy held by Maori in the Bay of Plenty. The paper
begins by introducing the purpose and rationale of the research. It then moves on to describe the
aims and qualitative research methods that were used to collect the data. Finally the paper
discusses the findings of the research, this includes: a close look at the unique perceptions of
epilepsy that were reported by Maori in the Bay of Plenty; the lack of resources and services
available in a small rural town of the Bay of Plenty; the services desired by Maori; attitudes
towards medication and the inappropriate behaviour many of the participants experienced by the
medical profession.

text: Maori and epilepsy: Personal perceptions of the cause, treatment and consequences of epilepsy by Maori in the Bay of Plenty This paper discusses the perceptions of epilepsy held by Maori in the Bay of Plenty. The paper
begins by introducing the purpose and rationale of the research. It then moves on to describe the
aims and qualitative research methods that were used to collect the data. Finally the paper
discusses the findings of the research, this includes: a close look at the unique perceptions of
epilepsy that were reported by Maori in the Bay of Plenty; the lack of resources and services
available in a small rural town of the Bay of Plenty; the services desired by Maori; attitudes
towards medication and the inappropriate behaviour many of the participants experienced by the
medical profession.

year: 1999