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
• Notes
• Class schedule
• Teaching tools
• Course card

Course objectives

This curriculum is designed for students who already possess knowledge of material properties and their fundamental principles. It focuses on methods for selecting and using various materials for specific applications, life cycle analysis, and an introduction to selected materials modeling approaches. These include machine learning, finite element methods, and computational thermodynamics.

The practical components of the course involve case studies on the use, selection, or failure of materials. Additionally, examples of modeling implementations applied to practical scenarios will be demonstrated. Through these practicals, students will develop the skills to utilize various software tools and implement models for materials analysis and design.

Results of learning outcomes

KNOWLEDGE AND UNDERSTANDING

• Understand the selection process of materials for a specific application.
• Grasp the fundamentals of life cycle analysis, machine learning, finite elements, and computational thermodynamics methods.

APPLYING KNOWLEDGE AND UNDERSTANDING

• Apply advanced computational and theoretical approaches to solve practical problems in materials science.
• Utilize different software packages to implement these concepts in practical applications.

INDEPENDENT JUDGEMENT

• Evaluate the advantages and disadvantages of using various materials in specific applications, considering environmental and technological factors.

COMMUNICATION SKILLS

• Effectively communicate the rationale behind material selection and the application of different modeling techniques in solving practical materials science problems.

LEARNING SKILLS

• Develop the ability to continuously update their knowledge on the latest methodologies and technologies in materials science through self-directed learning.

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 stages 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

48 hours of classroom lectures. 32 hours of laboratory sessions.

Attendance to the lectures is optional. Attendance to the laboratory sessions is always compulsory. Each student should be present at least at the 70% of laboratory sessions.

During these laboratory sessions, students will have the opportunity to practically apply the concepts covered in the lectures. They will also utilize various software packages to support their learning and experimentation. Additionally, students will be organized into working teams, with each team comprising 4 to 6 members.

Learning assessment methods

Students will be graded according to:

1. An oral examination consisting of open-ended questions that focus on the content covered during the lectures.

2. The creation of a lab report, which each working team will prepare based on their laboratory activities. 

The final grade will be determined by the oral examination and the lab report, each contributing according to their respective CFUs.

Teachers’ notes.

Notes

Class schedule

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