Outline

Overview

The rapid development of advanced manufacturing processes such as additive manufacturing and associated digital technologies have significantly disrupted the established engineering design practices. This requires engineering designers to have an in-depth understanding of new manufacturing technologies, as well as new business models and production systems. This course will focus on the emerging additive manufacturing technology. The course will provide the required design knowledge for additive manufacturing and hands-on lab activities from design and fabrication to characterization. The unit will teach fundamental science and technologies from week 1 to week 8, covering topics such as computer integrated product design, materials selection for additive manufacturing and different processing technologies. From week 8, the students will work in groups, selecting their own project and applying their design knowledge to make a product meeting the design requirements. The lectures will expand on sustainable design, applications of current additive manufacturing, and future developments of the emerging design/manufacturing techniques. One seminar day will be organized in the final week for all the groups to present and discuss their work. The course will combine technically rich lectures and hands-on lab activities, delivering a comprehensive understanding of the design process for advanced manufacturing.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Background knowledge of mechanical design, manufacturing, and engineering materials
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Li Chang, li.chang@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Case studies	Assignment 1 Practice Problems	5%	Week 04 Due date: 20 Mar 2026 at 23:59 Closing date: 27 Mar 2026	5-8 pages calculations	AI allowed
Outcomes assessed:
Case studies	Lab report Students prepare the report to analyze the lab results independently	10%	Week 05 Due date: 27 Mar 2026 at 23:59 Closing date: 15 Apr 2026	5-8 pages lab report	AI allowed
Outcomes assessed:
Case studies	Project proposal Project proposal for project plan, methodology and main targets	10%	Week 06 Due date: 02 Apr 2026 at 23:59	10-`12 pages	AI allowed
Outcomes assessed:
In-person written or creative task	Quiz supervised quiz, multiple choice and practice problems	10%	Week 07 Due date: 15 Apr 2026 at 13:00 Closing date: 15 Apr 2026	2-3 page calculations	AI prohibited
Outcomes assessed:
Practical skill	Assignment 2 Practice Problems	5%	Week 10 Due date: 08 May 2026 at 23:59 Closing date: 15 May 2026	5-10 pages calculations	AI allowed
Outcomes assessed:
Case studies	Lab report Students prepare the report to analyze the lab results independently	10%	Week 11 Due date: 15 May 2026 at 23:59 Closing date: 22 May 2026	5-8 pages lab report	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	presentation for the major project present of the project outcomes	15%	Week 13 Due date: 29 May 2026 at 14:00 Closing date: 29 May 2026	20 minutes presentation + 5 minutes Q&A	AI prohibited
Outcomes assessed:
Case studies	Final report for major project The results and main finding of the major project with sound discucssions	35%	Week 13 Due date: 29 May 2026 at 23:59	25-30 page project report	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Lab report: Students are required to attend a lab session and submit a written report.
Assignment 1: Students will be required to submit an assignment in response to practical problems on basic mechanical behaviour of engineering materials.
Assignment 2: Students will be required to demonstrate their knowledge of failure analyses using failure and fracture criteria

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when students demonstrate the learning outcomes for the unit at an exceptional standard.
Distinction	75 - 84	Awarded when students demonstrate the learning outcomes for the unit at a very high standard.
Credit	65 - 74	Awarded when students demonstrate the learning outcomes for the unit at a high standard.
Pass	50 - 64	Awarded when students demonstrate the learning outcomes for the unit at an acceptable standard.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The Assessment Procedures 2011 provide that any written work submitted after 11:59pm on the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to advanced/additive manufacturing	Lecture (2 hr)
Week 02	Generative Design for advanced manufacturing: computer Integrated product design	Lecture (2 hr)
Week 02	Generative Design for advanced manufacturing: computer Integrated product design	Tutorial (2 hr)
Week 03	Design of tailored structure for end applications: Material selections and processing technologies for advanced manufacturing	Lecture (2 hr)
Week 03	Design of tailored structure for end applications: Material selections and processing technologies for advanced manufacturing	Practical (3 hr)
Week 04	Design for laser manufacturing processing	Lecture (2 hr)
	Design for laser manufacturing processing	Tutorial (2 hr)
	Design for laser manufacturing processing	Practical (1 hr)
Week 05	Design for fusion deposition modelling of polymer Parts	Lecture (2 hr)
	Design for fusion deposition modelling of polymer Parts	Tutorial (2 hr)
	Design for fusion deposition modelling of polymer Parts	Practical (1 hr)
Week 06	Design for powder bed fusion of metal parts	Lecture (2 hr)
Week 06	Design for powder bed fusion of metal parts	Practical (1 hr)
Week 07	Design shape-memory materials, 4D printing & bio-active materials	Lecture (2 hr)
Week 07	Design shape-memory materials, 4D printing & bio-active materials	Practical (2 hr)
Week 08	Design for fabricating micro parts and micro features	Lecture (2 hr)
Week 08	Design for fabricating micro parts and micro features	Tutorial (2 hr)
Week 10	Sustainable design for advanced manufacturing: Product Life-cycle management	Lecture (2 hr)
Week 10	Sustainable design for advanced manufacturing: Product Life-cycle management	Tutorial (2 hr)
Week 11	Future Directions in Advanced Manufacturing: Industry 4.0,	Lecture (2 hr)
Week 11	Future Directions in Advanced Manufacturing: Industry 4.0,	Tutorial (2 hr)
Week 12	Digital twins and cyber security	Lecture (2 hr)
Week 12	Digital twins and cyber security	Tutorial (2 hr)
Week 13	Case studies/seminars	Lecture (2 hr)
	literature reading, project preparation and attempting assignments	Self-directed learning (80 hr)
	Case studies/seminars	Tutorial (2 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Grasp comprehensive design knowledge of advanced manufacturing processes and material selections for different applications.
LO2. Apply and use industry-relevant software tools for advanced manufacturing.
LO3. Design and fabricate engineering components and structures such as gears and pumps using additive manufacturing technology, to satisfy specified performance and functionality requirements.
LO4. Understand the concepts of sustainable design and product life-cycle management
LO5. Understand the latest trends and business opportunities in advanced manufacturing
LO6. Collaborate effectively in team processes in multi-cultural and multi-disciplinary contexts
LO7. Prepare an engineering report and communicate effectively in professional and academic contexts.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Based the feedback received from students in last year, we moved the project proposal to week 6. Thus, students will have more time to revise and prepare the major the project.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

MECH5461: Design for Advanced Manufacturing

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links