Outline

Overview

The aim of this unit of study is to enhance a student's manufacturing engineering skills using advanced Computer Aided Manufacturing (CAM) as a framework. The unit of study focuses on subtractive; additive, assembly and automation manufacturing processes as applied to a project. The management, planning and marketing of a typical manufacturing engineering project are also discussed. Through integrated project-based learning and hands-on-machine training, students learn how to: successfully complete a scalable manufacturing-based systems project, apply manufacturing management and system skills, product planning, the science in designing and selecting a manufacturing method, how to effectively present ideas and outcomes using a combination of video and report-based methods.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	(MECH2400 or MECH9400) and (MECH3660 or MECH8660 or MECH9660)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
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 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Creative work	Lab 2 - Rapid Engineering / 3D Scanning Laboratory Rapid Engineering / 3 D Scanning Laboratory	5%	Multiple weeks	Max 10 pages	AI allowed
Outcomes assessed:
Creative work	Lab 3 - Robot Assembly Laboratory Robot Assembly Laboratory	5%	Multiple weeks	1 minute video	AI allowed
Outcomes assessed:
Written work	Assignment 1 - Machining / Manufacturing Automation CNC Code Submission / LabVIEW file submission	25%	Week 05 Due date: 05 Sep 2025 at 23:59 Closing date: 19 Sep 2025	Max 10 pages	AI allowed
Outcomes assessed:
Written work	Assignment 2 - Manufacturing System Design / Manufacturing Automation Manufacturing System Design / Manufacturing Automation	25%	Week 09 Due date: 10 Oct 2025 at 23:59 Closing date: 24 Oct 2025	Max 10 pages	AI allowed
Outcomes assessed:
Out-of-class quiz	Lab 1 - CNC Mill Laboratory CNC Mill Laboratory	10%	Week 13 Due date: 07 Nov 2025 at 23:59 Closing date: 07 Nov 2025	Tour and 1 hour online Quiz	AI allowed
Outcomes assessed:
Written work	Systems Engineering Report Systems Engineering Report	25%	Week 13 Due date: 07 Nov 2025 at 23:59 Closing date: 21 Nov 2025	Max 30 pages	AI allowed
Outcomes assessed:
Presentation	Systems Engineering Presentation Video A 6 minute video of the Group's Final Project.	5%	Week 13 Due date: 07 Nov 2025 at 23:59 Closing date: 21 Nov 2025	6 minutes.	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Assignment 1: Machining / Manufacturing Automation. CNC code to be submitted as a .nc file./ A basic report and LabVIEW file are to also be to be submitted.

Assignment 2: Manufacturing System Design / Manufacturing Automation. A Manufacturing System Design Report / A basic report and LabVIEW file are also to be to be submitted.

Lab 1: CNC Mill Demonstration Laboratory that enables students to interact with CNC maching scenarios. Online Quiz Assessment.

Lab 2: Rapid Engineering / 3D Scanning Laboratory. A report submission that enables students to undertake a Rapid Engineering / 3D Scanning task.

Lab 3: Robot Assembly Laboratory. A video submission that enables students to undertake a Robot Assembly task using the experimental robot setup at laboratories of the University.

Systems Engineering Report: A final report which includes the many themes covered in the Unit of Study and is focused on the manufacturing of an assembly from a Systems Engineering approach.

Systems Engineering Presentation Video: A group presentation of 5 to 6 minutes in duration. The format required is in a pre-recorded video (.mp4 format) which is aligned with the Systems Engineering Report.

Detailed information for each assessment can be found on Canvas.

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	Awarded when students do not meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

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:

Late penalties are in accordance with University Guidelines.

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
Ongoing	Non-contact independent work doing research, homework, and working on assignments, group meetings, and prior readings across multiple weeks.	Independent study (90 hr)
Week 01	Introduction to unit of study and CNC Machining	Lecture (1 hr)
	CAD SolidWorks refresher and CNC machining	Tutorial (1 hr)
	The Current State of Manufacturing	Lecture (1 hr)
	Research Reading Activity	Tutorial (1 hr)
Week 02	Writing CNC machine code and Introduction to CIMCO	Lecture (1 hr)
	Writing CNC machine code and Introduction to CIMCO	Tutorial (1 hr)
	Data Acquisition and LabVIEW	Lecture (1 hr)
	Data Aquisition and LabVIEW	Tutorial (1 hr)
Week 03	Jigs and Fixtures in Machining	Lecture (1 hr)
	Jigs and Fixtures in Machining	Tutorial (1 hr)
	Analogue Sampling and LabVIEW	Lecture (1 hr)
	Analogue Sampling and LabVIEW	Tutorial (1 hr)
Week 04	Machining Forces and Cutting Time	Lecture (1 hr)
	Machining Forces and Cutting Time	Tutorial (1 hr)
	Data Processing with LabVIEW	Lecture (1 hr)
	Data Processing with LabVIEW	Tutorial (1 hr)
Week 05	Construction and Kinematics of CNC machines	Lecture (1 hr)
	Construction and Kinematics of CNC Machines	Tutorial (1 hr)
	Instruments and Communication Protocols	Lecture (1 hr)
	Work on Assignment 1	Tutorial (1 hr)
Week 06	3D Printing	Lecture (1 hr)
	3D Printing and STL Files	Tutorial (1 hr)
	Software Design Patterns	Lecture (1 hr)
	Software Design Patterns	Tutorial (1 hr)
Week 07	Super Tutorial on Assignment 2	Lecture (1 hr)
	Work on Assignment 2	Tutorial (1 hr)
	Events, Queues and Object Orientated Programming	Lecture (1 hr)
	Events, Queues and Object Orientated Programming	Tutorial (1 hr)
Week 08	CAMWorks 2.5 Axis Machining	Lecture (1 hr)
	CAMWorks 2.5 Axis Machining	Tutorial (1 hr)
	Automation Review	Lecture (1 hr)
	Software Design Patterns + Review Questions	Tutorial (1 hr)
Week 09	Embedded Systems and Cloud Storage	Lecture (1 hr)
Week 09	Work on Assignment 2	Tutorial (1 hr)
Week 10	CAMWorks 4 Axis Machining	Lecture (1 hr)
	CAMWorks 4 Axis Machining	Tutorial (1 hr)
	Automated Test Systems	Lecture (1 hr)
	Arduino Focused Tutorial Work	Tutorial (1 hr)
Week 11	Process Engineering	Lecture (1 hr)
	Process Engineering	Tutorial (1 hr)
	Automated Inspection Systems and AI in Vision	Lecture (1 hr)
	Arduino Focused Tutorial Work	Tutorial (1 hr)
Week 12	CAMWorks Post Processors	Lecture (1 hr)
	CAMWorks Post Processors	Tutorial (1 hr)
	Case Studies - Automation	Lecture (1 hr)
	Systems Engineering Project Super Tutorial	Tutorial (1 hr)
Week 13	Systems Engineering Project Super Tutorial	Lecture (1 hr)
	Systems Engineering Project Tutorial	Tutorial (1 hr)
	Systems Engineering Project Automation Review Super Tutorial	Lecture (1 hr)
	Systems Engineering Project Automation Review Tutorial	Tutorial (1 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. Interpret a design within the context of how it will be manufactured using a subtractive manufacturing.
LO2. Write Computer Numerical Control (CNC) codes using a simulator and be able to apply them to manufacture a component using a CNC machine.
LO3. Gain skills in selecting and designing jigs and fixtures to be used in the manufacture of a component.
LO4. Understand the structure and kinematics of a CNC machine, as well as the types of machine architectures and the corresponding capabilities, advantages, and disadvantages of each architecture.
LO5. Gain skills in Systems Engineering, product planning, manufacturing sequence, time, teamwork, project management, cost and express the outcomes in a comprehensive report.
LO6. Understand the most important aspects of the pre-processing in 3D printing technologies and its effects in the components quality and productivity.
LO7. Understand the nomenclature and selection process for different commercially available 3D printing systems with consideration for their relative merits and the .stl file format.
LO8. Use SolidWorks combined with CAMWorks to be introduced to multi axis machining of components that have complex non-orthogonal geometry.
LO9. Gain skills in the development and compiling of Post Processors for use within machining packages such as CAMWorks.
LO10. Understanding the Current State of Manufacturing Automation.
LO11. Understanding the Methodology of Data Acquisition.
LO12. Understanding the Methodology of Analogue Sampling.
LO13. Understanding the Methodology of Data Processing.
LO14. Gain Skills in Understanding Instruments and Communications Protocols using LabVIEW as a Framework.
LO15. Gain Skills in Event Structures and Object Orientated Programming.
LO16. Understanding Software Design Patterns.
LO17. Gain Skills in Embedded Systems using LabVIEW as a Framework.
LO18. Gain Skills in Automated Test Systems.
LO19. Gain Skills in Automated Inspection System and AI in Vision Using LabVIEW as a Framework.
LO20. Gain Skills in the use Assembly Robot Simulator.
LO21. Gain Skills in the use of 3D Scanning and Metrology Arms.

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.

The UOS has been updated to match S2 2025 calendar. Content for 2025 has been revised to include 1. Manufacturing Automation. 2. Highly Non-Linear FEA tools to simulate subtractive, additive and formative areas of Manufacturing Engineering.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

AMME5902: Computer Aided Manufacturing

Semester 2, 2025 [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