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Automation of Materials Manufacturing
University College London (UCL)London, United Kingdom
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Program Details
- Degree
- Masters
- Major
- Chemical Engineering | Materials Engineering | Manufacturing Technology
- Area of study
- Engineering | Manufacturing and Processing
- Course Language
- English
Program Overview
Automation of Materials Manufacturing (CENG0066)
Key Information
- Faculty: Faculty of Engineering Sciences
- Teaching department: Chemical Engineering
- Credit value: 15
- Restrictions: Pre-requisite: CENG0068 Fundamentals of Data Science
Alternative Credit Options
There are no alternative credit options available for this module.
Description
Aim:
- Provide fundamentals on automation in materials manufacturing.
- Provide practical understanding and tools for the development of data-driven models and digital twins for material manufacturing.
- Bridge the gap between hardware and software development in material synthesis by using data-driven models and digital twins for process simulation, monitoring, and optimization.
- Train students on the use of a range of practical computational tools for online data analysis, process monitoring, and optimization.
- Train students on effective team working with others to deliver a process design project on automated materials manufacturing.
Synopsis:
In this computational module, students will learn how to apply machine learning techniques and statistical methods to develop data-driven models and "digital twins" (i.e., in-silico surrogates of selected material manufacturing processes related to material synthesis in flow or batch). This will be done through a project where students will learn:
- Fundamentals of I/O digital communication in automated flow synthesis processes.
- How to develop and use data-driven models and digital twins to simulate, monitor, and/or optimize material synthesis at the lab scale.
- How to assess the potential scalability of synthesis processes using statistical techniques.
The module will be delivered through face-to-face lectures, seminars from industrial experts, and computer tutorials aiming to bridge the gap between data-driven modeling and experimentation in chemical manufacturing.
Learning Outcomes:
- Automate processes suitable for the synthesis of materials in industrial applications.
- Develop computational tools for data visualization and analysis in automated material manufacturing.
- Make informed decisions and propose new solutions aided by data acquisition, processing, and analysis.
- Practically assess the viability of material synthesis solutions for the manufacturing of materials at a larger scale using information from automated lab-scale experiments.
- Proficiently develop data-driven models and digital twins for process simulation, monitoring, and optimization.
Module Deliveries for 2026/27 Academic Year
- Intended teaching term: Term 2
- Postgraduate (FHEQ Level 7)
Teaching and Assessment
- Mode of study: In person
- Intended teaching location: UCL East
- Methods of assessment:
- 70% Coursework (3 assessments)
- 30% Group activity
- Mark scheme: Numeric Marks
Other Information
- Number of students on module in previous year: 8
- Module leader: Dr. Reza Abbasi
About University
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