Units / MEC3822
MEC3822 · Artificial intelligence in manufacturing
2026 Handbook6 credit pointsLevel 3Department of Mechanical and Aerospace Engineering
Last checked: 23 Aug 2026 UTCOverview
This unit aims to provide an understanding of how artificial intelligence (AI) techniques can be used to solve manufacturing problems. The topic covers various fundamental aspects of artificial intelligence such as heuristic, fuzzy logic, machine learning and genetic algorithms, along with their applications in manufacturing. Programming techniques will be used to construct practical solutions based on the appropriate AI techniques.
Areas of study: Minor: Artificial intelligence in engineering Minor: Intelligent manufacturing
Offerings
| Campus | Teaching period | Mode |
|---|---|---|
| Malaysia | First semester | Teaching activities are on-campus (ON-CAMPUS) |
Assessment
The Handbook lists an examination for this unit.
| # | Assessment | Type | Weight | Hurdle |
|---|---|---|---|---|
| 1 | Lab 1: Heuristic and search-based problem-solving | Written | 10% | Threshold |
| 2 | Lab 2: Fuzzy logic | Written | 10% | Threshold |
| 3 | Lab 3: Introduction to machine learning | Written | 10% | Threshold |
| 4 | Lab 4: Neural networks | Written | 10% | Threshold |
| 5 | Lab 5: Genetic algorithms | Written | 10% | Threshold |
| 6 | Final assessment | Examination | 50% | Threshold |
Continuous assessment: 50% Final assessment: 50% This unit contains threshold hurdle requirements that you must achieve to be able to pass the unit. You are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final assessment component. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Requisites
prerequisite
- ENG1013 — Engineering smart systems
Learning outcomes
- Apply appropriate artificial intelligence techniques to solve common engineering problems in a manufacturing setup.
- Construct algorithms and programs that can put various artificial intelligence techniques into practice.
- Apply the programs to common engineering problems in a manufacturing setup to generate solutions.
- Analyse and interpret results produced by various artificial intelligence techniques.
Workload
The minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of 3-6 hours of scheduled learning activities and 6-9 hours of independent study per week. Scheduled activities may include a combination of teacher-directed learning, peer-directed learning and online engagement. Independent study may include associated readings, assessment and preparation for scheduled activities.
| Activity | Duration |
|---|---|
| Workshops | 36 hours |
| Laboratories | 22 hours |
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