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Mae Dr Amit Das yn Uwch Ddarlithydd ym Mhrifysgol Abertawe.
Mae Dr Amit Das yn Uwch Ddarlithydd ym Mhrifysgol Abertawe.
This module provides an introduction to several AI algorithms for engineering/physics problems. The specific engineering problems chosen to demonstrate the benefits offered by AI algorithms are 1) interpretation/processing of data from distributed sensors; 2) optimisation of material/device properties through physics informed neural network. The module teaches students basic statistical skills underpinning machine learning/artificial intelligence such as probability analysis and regression, as well as several case studies that use existing AI software to analyse engineering problems. Two assessments (exam and individual project, each carries 50%) for each term, designed to examine understanding of the basic machine leaning concepts and using the software to solve engineering problems, take place in the middle of term and end of term. Emphasis is placed on the use of existing software for tackling engineering problems.
This module considers the most important solidification and solid-state phase transformations that can occur in metallic materials and how these transformations determine microstructural evolution. The kinetics of phase transformations are discussed and procedures for controlling microstructures, and hence mechanical properties, detailed by reference to the processing of commercially significant alloys.
The course will provide students with the opportunity to construct programmes that solve more advanced numerical problems and also simulate some real physical phenomena that are important in engineering. The course finishes when students synthesize mathematical, numerical and fundamental metallurgical principles into a fully operational diffusion simulation.
This module is concerned with the microscopic examination, measurement and interpretation of the microstructures of metals. This involves the development skills in the microstructural characterisation of metals and the development of sound microstructural measurement methodologies. This is then reinforced by logical reasoning based on an understanding of binary phase diagrams of the nature of the phase transformations occurring in metals and of the influence of processing on microstructure. This module also details the mathematical descriptions necessary for the solution of a variety of critical engineering problems, with an emphasis on materials processing.
The module aims to develop fundamental research skills. It comprises the development of supervised research work leading to a dissertation in the field of the Master's degree programme. The specific research topic will be chosen by the student following consultation with academic staff.
This module is based around four separate case studies in Materials. Each case study will focus on a particular engineering material, component or structure. Within each case study, students will be presented with some initial material, along with suggested links to further information. Students will then undertake independent study either individually or in groups, with a final report presented on the findings. In some case studies, presentations will be part of the assessment. The specific case studies will include engineering design, materials selection for engineering applications, failure analysis and materials characterisation techniques.
The project will be selected following discussions with the Course Coordinator. Original research will be carried out, implementing the work necessary to achieve the milestones, objectives and deliverables outlined in the Project Plan. Following approval of the Project Plan and completion of mandatory safety training and documentation, the student will begin the research programme and subsequently comply with the following. Attendance of progress meetings with supervisor(s), as required: Submission and approval of supervisor(s) of an interim research progress report; Short oral presentation of project progress; Submission of thesis, according to University Regulations, with a target date of September 30th. Not available to visiting or exchange students.