The course is about decision support systems in advanced manufacturing technology, based on industrial metrology tools and procedures. The course includes an extensive review of advanced manufacturing processes and industrial metrology tools, methods, algorithms and their applicability, configurations, subsystems, structure, design and operational capability. During the course, extensive training is given in handling and evaluation of production and measurement data through use of applicable statistical tools and algorithms for machine learning to receive and account result with traceability. The course intends to teach the students how measuring techniques assist decision support in advanced production. On completion of the course, the students will be able to apply new knowledge through three main activities : design, carry out and document personal research.
MG2045 Decision-making for Advanced Manufacturing 6.0 credits
Information per course offering
Information for Autumn 2024 Start 28 Oct 2024 programme students
- Course location
KTH Campus
- Duration
- 28 Oct 2024 - 13 Jan 2025
- Periods
- P2 (6.0 hp)
- Pace of study
33%
- Application code
50803
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Number of places
Min: 5
- Target group
Compulsory for TIEMM MOPR1
Conditionally elective for TPRMM
Elective for all other Master programmes as long as it can be included in your programme.
- Planned modular schedule
- [object Object]
- Schedule
Contact
Robert Tomkowski (rtom@kth.se)
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus MG2045 (Autumn 2021–)Content and learning outcomes
Course contents
Intended learning outcomes
After passing the course, the students should be able to:
- Describe advanced manufacturing processes
- Explain the importance of metrology (measurement technology) in advanced manufacturing processes
- Describe methods and instruments that are used industry for metrology purposes
- Evaluate measurement and manufacturing data by use of suitable statistical tools and algorithms for machine learning
- Apply decision support systems for advanced manufacturing
Literature and preparations
Specific prerequisites
Admitted to a Master's programme (two-year).
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- INLA - Individual home assignment, 1.5 credits, grading scale: A, B, C, D, E, FX, F
- LABA - Laboratory work, 1.5 credits, grading scale: P, F
- PROA - Project work, 3.0 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.