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Unit outline_

ARCH9110: Code to Production

Intensive December, 2022 [Block mode] - Camperdown/Darlington, Sydney

Code to Production is an elective that explores the potential of an iterative design process from parametric variations; to analysis and simulation; to digital prototyping and manufacturing. The course has a two-fold agenda: to examine the performance of complex geometries available through computational design processes, and to translate the optimised design by digital manufacturing into construction and prototype (CNC/robotic fabrication). Based upon the development of a series of controlled variations derived through parametric and scripting methods, the elective aims to further expand an understanding of structural and acoustic performance of these geometries. It reviews an open system of design research in which design process, structural analysis and acoustic analysis are deployed to improve the acoustic and structural performance of complex spatial geometries, and derive fabrication knowledge for architectural practice. The unit of study extends students' knowledge of advanced computational design, interdisciplinary processes and fabrication methodologies by application of commercial and specialist 3D-modelling, scripting, analysis and manufacturing packages (including various software such as McNeel Rhino and Grasshopper, Karamba, RhinoNest and KUKA/prc).

Unit details and rules

Academic unit Architecture
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Dagmar Reinhardt, dagmar.reinhardt@sydney.edu.au
Lecturer(s) Dagmar Reinhardt, dagmar.reinhardt@sydney.edu.au
Tutor(s) Eduardo De Oliveira Barata, eduardo.barata@sydney.edu.au
Lynn Masuda, rin.masuda@sydney.edu.au
Dylan Wozniak-O'Connor, dylan.wozniak-oconnor@sydney.edu.au
Type Description Weight Due Length
Assignment group assignment MID CRIT (A1)
Assignment 1: present and submit design and work report. 40%, due Nov 25.
40% Week 01
Due date: 25 Nov 2022 at 14:00
50 page draft report, 5 min mov.
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment group assignment FINAL PRESENTATION (A2.1)
Assignment 2.1: present via all materials. PDF /mov of robot, design, work.
20% Week 02
Due date: 02 Dec 2022 at 14:00
80-100 page draft report
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment group assignment FINAL SUBMISSION (A2.2)
Assignment 2.2: PDF/movie of robot, design and work report.
40% Week 03
Due date: 09 Dec 2022 at 10:00
80-100p report, 5 min movie and files.
Outcomes assessed: LO1 LO2 LO3 LO4
Group assignment with individually assessed component = group assignment with individually assessed component ?

Assessment summary

As per 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

Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.

Distinction

75 - 84

Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.

Credit

65 - 74

Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.

Pass

50 - 64

Work demonstrating satisfactory achievement of the learning outcomes assessed.

Fail

0 - 49

Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

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:

As per Canvas.

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Week 1: EXPLORE AND LEARN Mo Nov 21: full day, 10-4pm (1hr break) Tues Nov 22: full day, 10-4pm (1hr break) Wed Nov 23: full day, 10-4pm (1hr break) Thu Nov 24: full day, 10-4pm (1hr break) Fri Nov 25: full day, 10 - 1 pm, 2-4pm Presentation Block teaching (20 hr) LO1 LO2 LO3 LO4
Week 02 Mo Nov 28: full day, 10-4pm (1hr break) Tues Nov 29: full day, 10-4pm (1hr break) Wed Nov 30: full day, 10-4pm (1hr break) Fri Dec 2: 2-4pm, Final Presentation Block teaching (17 hr) LO1 LO2 LO3 LO4

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 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.

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

  • LO1. understand the robotic interface and tools as taught through software environment and machinery
  • LO2. use common software features including Rhino, Grasshopper, and kuka\prc
  • LO3. describe and discuss design and robotic application in a set project and framework with others
  • LO4. develop a design project that adopts the robotic framework, is aesthetically pleasing, and well-documented.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
LO1         
LO2         
LO3         
LO4         

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

changes made to 2022 iteration of unit

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.