Crazy Machines are collaborative design-build projects where student teams follow authentic workflows to create functional automated systems that solve specified technical challenges. Teams integrate multiple technologies, manage resources and timelines, and demonstrate professional communication skills through progressive deliverables culminating in live system demonstrations.
Key features
Lane 1: Secure assessment
Supported by face-to-face lab sessions with hands-on building and live demonstrations
Teams of 3-4 students collaborating on authentic engineering challenges over 12 weeks
Simulates real-world engineering workflows from conceptual design through implementation, testing and professional documentation
Develops technical skills in digital systems programming, mechanical design, project management and engineering communication
Significantly reduces academic misconduct through physical demonstrations, peer evaluation, and multiple checkpoint submissions
Authentic professional context creating museum exhibits encourages creative problem-solving within strict technical constraints
How it works
Educators provide detailed project specifications, for example, dimensional constraints, timing requirements, interface standards, and technical requirements
Teams are formed in weeks 1-2 with clear expectations for role allocation and peer accountability
Resources include component inventories, lab access, 3D printing facilities, power supplies, and comprehensive rubrics for all submission stages
Museum context and Swiss Transport Museum exemplar video provide authentic professional motivation and clear goal setting
Teams progress through structured engineering phases
Four major deliverables track progress: detailed progress report with Gantt charts and component specifications, professional 3-minute demonstration video showcasing technical design decisions, live machine demonstration proving full functionality, and comprehensive final report documenting complete engineering process
Continuous lab access enables iterative building, testing, and refinement of mechanical and electronic subsystems
Regular check-ins monitor technical progress, team dynamics, resource management, and adherence to project timeline
Live machine demonstrations during Study Week where teams present fully functional modules that successfully receive, manipulate, and deliver steel balls within timing constraints
Assessment evaluates technical complexity, system integration quality, innovative use of recycled materials, timing precision, and automatic reset capability
Peer evaluation forms ensure individual accountability within collaborative team structures
Professional documentation demonstrates engineering communication skills and design process mastery
Curtin snapshot
Case Study
A/Prof Cesar Ortega-Sanchez
“Students experience the complete engineering lifecycle – from initial concept through to working prototype. The integration challenges they face mirror real industry projects, and seeing their machines actually work in sequence as a museum exhibit is incredibly rewarding for everyone.”
Faculty of Science and Engineering
Cesar’s example assessment
About my unit: Faculty of Science and Engineering | 50-100 students | In-Person | Group work
My Advanced Digital Design students work in teams to create Crazy Machine modules – automated contraptions designed as museum exhibits that receive a steel ball, manipulate it through various mechanisms for exactly 45-60 seconds, then deliver it seamlessly to the next team’s module. Each 40x40x40cm module must demonstrate sophisticated integration of FPGA programming, sensor networks, actuator control, and creative mechanical design using primarily recycled materials.
The project spans 12 weeks with four major deliverables that mirror professional engineering workflows. Teams submit progress reports outlining system architecture and detailed project planning, create professional demonstration videos showcasing technical design decisions and current functionality, perform live machine demonstrations proving full operational capability, and produce comprehensive final reports documenting their complete engineering design process.
Teams must balance technical sophistication with reliability – modules need multiple unique mechanisms, innovative sensor integration, creative reuse of waste materials, precise timing control, and fully autonomous reset capability. The assessment evaluates both technical achievement and professional engineering communication skills.
My advice
Structure the project around early system integration rather than last-minute assembly panic. Encourage teams to prototype and test individual subsystems thoroughly before attempting full integration and emphasise that mechanical reliability is just as important as software sophistication. The peer evaluation component is crucial for maintaining individual accountability – set crystal-clear expectations about personal contributions and team dynamics from day one. Most importantly, remind students that their modules will be interconnected, so interface requirements are non-negotiable – one team’s failure affects everyone downstream.
Suggested marking criteria
Machine description, system block diagrams, requirements and specifications, project planning with Gantt charts, task allocation justification, component lists, progress documentation, and professional writing quality
Team introduction, technical machine description using appropriate terminology, clear demonstration of working elements, and professional production quality
Machine completeness, ball receiving/delivery capability, precise timing (60±5 seconds), hardware/software complexity, mechanical sophistication, innovative use of recycled materials, autonomous reset functionality, and aesthetic presentation
Professional documentation, clear problem statement, comprehensive specifications, detailed planning, system architecture diagrams, design decisions and assumptions, implementation details, verification testing, integration challenges, and adherence to word limits
Note: Marking criteria and weighting are suggested guidelines. Specific descriptions should be adapted to relevant content and learning objectives.
