Master degree in Materials Science

Computational Methods for the Use of Materials

Computational Methods for the Use of Materials

Academic year 2023/2024

Sommario del corso

• Course objectives
• Results of learning outcomes
• Program
• Course delivery
• Learning assessment methods
• Suggested readings and bibliography
• Class schedule
• Teaching tools
• Course card

Course objectives

This course aims at providing students who have already acquired knowledge on the properties of materials and their basis, with methods for the selection and use of different materials for specific applicative functions, life cycle analysis and the basics of selected materials modelling approaches, including machine learning, finite elements and computational thermodynamics methods.

The practicals are about case studies on the use, selection or failure of materials. Furthermore examples with modelling implementations on practical cases will be showed. Hence the students will acquire the ability to use different softwares and implement models for materials.

Results of learning outcomes

It is expected that the students will acquire abilities in evaluating the pros and cons in using different materials, knowledge of technological properties in relation to the processing techniques, fundamentals of life cycle analysis. Besides, the students are expected to learn the fundamentals of machine learning, finite elements and computational thermodynamics methods and will acquire the ability to apply these approaches to practical problems in materials science.

Program

Selection and use of Materials
The concept of selection of materials: motivation, processes, costs. Selection based on mechanical and surface properties. Description of Ashby’s charts. Technological properties of materials: friction, wear, thermal shock, oxidation, corrosion.

Life Cycle Analysis (LCA)
Definition and goals of the Life Cycle assessment. Description of the main of LCA: i) goal and scope; ii) Life Cycle Inventory; iii) Life Cycle Impact Assessment; iv) Interpretation. Case studies.

Machine learning (ML)

Introduction to ML: supervised/unsupervised learning, regression, classification, data mining. Data handling, cleaning and preparation. Selecting and engineering features and models. Hyperparameters, cross-validation, bias, variance. Most common learning algorithms: linear, polynomial, logistic regression; k-Nearest Neighbors; Support Vector Machines; Decision Trees; Ensemble learning; Random Forests. Introduction to neural networks and deep learning.

Computational Thermodynamics

Introduction to the CALPHAD method. Single and multicomponent thermodynamic equilibrium. Models for the Gibbs energy. Calculation of phase diagrams. Construction of databases after critical evaluation of experimental information as well as first-principles calculated data. Examples of applications. 

Finite Elements

Introduction to finite elements and differential equations in materials science. Strong and weak formulation. Weighted residuals methods. Iterative solutions schemes. Examples in 1D and 3D.

Course delivery

Lectures 48 hours. Laboratory 32 hours

Attendance to lecture is advised but not compulsory. Attendance to lab classes is compulsory.

Learning assessment methods

The exam consists of two parts:

- oral questions on the topics dealt with during the lectures

- the writing and discussion of a report on laboratory activity.

M. Ashby, Materials Selection in Mechanical Design, Butterworths and Heinemann.

Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly.

H.L. Lukas, S.G. Fries, B. Sundman: Computational thermodynamics, the Calphad method, Cambridge University Press (2007).

O. C. Zienkiewicz, Kenneth Morgan Morgan, Finite Elements and Approximation, Dover Publications

Teachers’ notes.

Class schedule

• Teaching material
• Exams and finals
• Class schedule
• Go to Moodle
• Register for the course
• Enrolled students
• Invia email agli studenti registrati
• Export table