Outline

Overview

The unit aims to teach the fundamentals of manufacturing processes and systems in mechanical and mechatronic engineering, including traditional manufacturing and advanced manufacturing technologies. Manufacturing processes are also compared in terms of their level of energy consumption, natural resources used and level of pollution generated in the manufacture of products. This unit aims to develop the following attributes: to understand the fundamental principles of manufacturing technologies for the above mentioned engineering areas; to gain the ability to select existing manufacturing processes and systems for direct engineering applications; to develop ability to create innovative new manufacturing technologies for advanced industrial applications; to develop ability to invent new manufacturing systems. At the end of this unit students will have a good understanding of the following: merits and advantages of individual manufacturing processes and systems; principles of developing new technologies; comprehensive applications and strategic selection of manufacturing processes and systems and the impact they may each have on our environment. Course content will include: CAD / CAM: An introduction into the use of CAD and manual CNC coding as separate tools combined with an introduction into the kinematics and structural requirements in the construction of a CNC machine. Rapid Engineering: An introduction into the most current Rapid Engineering methods currently in use. Manufacturing Processes: Common processes and their science (machining, casting, powder metallurgy, metal working, welding) and their relative merits, limitations and the impact they may each have on our environment.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	MECH9660
Assumed knowledge ?	MECH9400 Mechanical Design 1
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Paul Briozzo, paul.briozzo@sydney.edu.au
Lecturer(s)	Karina Taylor, karina.taylor@sydney.edu.au
Lecturer(s)	Paul Briozzo, paul.briozzo@sydney.edu.au
Tutor(s)	Luke Halls, luke.halls@sydney.edu.au
	Freddy Caro Diaz, freddy.carodiaz@sydney.edu.au
	Thai Nguyen, thai.nguyen@sydney.edu.au
	Justin Wootton, justin.wootton@sydney.edu.au
	Lauren Punter, lauren.punter@sydney.edu.au
	Lucas Parker, lucas.parker@sydney.edu.au

Assessment

The census date for this unit availability is 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Rapid Engineering Laboratory Group report	5%	Multiple weeks	15 pages in total.
Outcomes assessed:
Assignment	Robot Assembly Laboratory Video submission of the UR5 robot	5%	Multiple weeks	Video submissions no longer than 90 sec.
Outcomes assessed:
Assignment	Assignment 1 - Computer Numerical Control and Automation Report and LabVIEW File Submission	25%	Week 05 Due date: 22 Mar 2024 at 23:59 Closing date: 12 Apr 2024	20 pages in total.
Outcomes assessed:
Assignment	Assignment 2 - Manufacturing Processes and Automation Report and LabVIEW File Submission	25%	Week 10 Due date: 03 May 2024 at 23:59 Closing date: 24 May 2024	20 pages in total.
Outcomes assessed:
Assignment	CNC Laboratory Refer to the CANVAS module on the Virtual CNC Mill Laboratory.	10%	Week 12 Due date: 17 May 2024 at 23:59 Closing date: 07 Jun 2024	90 mins.
Outcomes assessed:
Assignment	Project - Machine Design, Manufacturing and Automation Group Project - Incorporating Machine Design and Automation	30%	Week 13 Due date: 24 May 2024 at 23:59 Closing date: 14 Jun 2024	20 pages in total.
Outcomes assessed:
= group assignment ?

Assessment summary

Assignment 1: Assignment 1 has two parts and incorporates the content of the early part of the unit of study from the areas of CNC machining and automation systems using LabVIEW. Assignment 1 is an individual assignment.
Assignment 2: Assignment 2 has two parts and requires students to compare manufacturing processes in the design of a component and automation system using LabVIEW. Assignment 2 is an individual assignment.
Report: A Group report and combines elements of Machine Design, Manufacturing and Automation.
Rapid engineering laboratory: A group report focusing on the rapid engineering laboratory and individual research must be submitted no later than two weeks of attending the rapid engineering laboratory.
Robotic assembly laboratory: A group video focusing on the robotic pick and place assembly laboratory must be submitted via the CANVAS ARC video submission system.
CNC Machining laboratory: Attendance of the CNC demonstration and a small online module to reinforce the experience.
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 guide to grades.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 2023 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 2023. 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
Multiple weeks	Non-contact independent work doing research, homework, and working on assignments, group meetings and prior readings across multiple weeks	Independent study (78 hr)
Week 01	1. Introduction to Unit of Study and CNC Machining; 2. Manufacturing Automation - Introduction to Automation and Testing	Lecture and tutorial (4 hr)
Week 02	1. CNC Machining; 2. Manufacturing Automation - Data Acquisition	Lecture and tutorial (4 hr)
Week 03	1. Cutting Forces plus Jigs and Fixtures; 2. Manufacturing Automation - Analogue Sampling	Lecture and tutorial (4 hr)
Week 04	1. CNC Machining - Structure and Kinematics of CNC machines; 2. Manufacturing Automation - Data Processing	Lecture and tutorial (4 hr)
Week 05	1. CNC Machining Review Lecture; 2. Manufacturing Automation - Software Developments - Design Patterns	Lecture and tutorial (4 hr)
Week 06	1. Manufacturing Processes - Casting; 2. 2. Manufacturing Automation - Automated Inspection Systems	Lecture and tutorial (4 hr)
Week 07	1. Manufacturing Processes - Casting Part 2; 2. Manufacturing Automation - Automated Inspection Systems	Lecture and tutorial (4 hr)
Week 08	1. Manufacturing Processes - Shrink Fits; 2. Manufacturing Automation - Finding Measurements in VBAI	Lecture and tutorial (4 hr)
Week 09	1. Manufacturing Processes - Welding Part 1; 2. Manufacturing Automation - Measurements and OCR in VBAI	Lecture and tutorial (4 hr)
Week 10	1. Manufacturing Processes - Metal Forming Thin; 2. Manufacturing Automation - VBAI Tools and Vision	Lecture and tutorial (4 hr)
Week 11	1. Manufacturing Processes - Forging Part 1; 2. Manufacturing Automation - Motion Control and Embedded Systems	Lecture and tutorial (4 hr)
Week 12	1. Manufacturing Processes - Forging Part 2; 2. Manufacturing Automation - Motion Control	Lecture and tutorial (4 hr)
Week 13	1. Manufacturing Processes - Powder Metallurgy; 2. Manufacturing Automation - Review Lecture	Lecture and tutorial (4 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.

Required readings

All readings for this unit can be accessed through Canvas.

Paul Briozzo, MECH3660 8660 9660 Manufacturing Engineering.

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. Display familiarity in the use of CNC machining in manufacturing
LO2. Understand the basic principles of automation and testing
LO3. Understand the methodology of analogue sampling
LO4. Understand the methodology of data acquisition
LO5. Understand the development of software design patterns structure
LO6. Understand the fundamental principles of manufacturing technologies for mechanical and mechatronic engineering
LO7. Understand the principles of automated inspection systems
LO8. Understand the fundamentals in finding measurements using the method of LabVIEW, VBAI, OCR and Vision
LO9. Discuss the major problems in the current manufacturing practice and provide suggestions to overcome or improve them
LO10. Determine functional requirements of a product in terms of application and suitability
LO11. Discuss the merits and disadvantages of an individual manufacturing method
LO12. Determine the basic manufacturing considerations necessary to realise the function, including the selection of materials and the manufacturing method, taking into account the strength and reliability
LO13. Understand the fundamentals in motion control
LO14. Contribute to effective team processes

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.

Assessment feedback will be returned more promptly.

Additional information

Appeals to assesment results are subject to Clause 3.1.2, https://www.sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2012/253&RendNum=0,

i.e. 15 working days (3 weeks).

The major group project require you to review your performance and that of your team members using SPARKPLUS. Individual marks for group assessments will be adjusted based on these reviews.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

MECH8660: Manufacturing and Sustainability

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect