Engin 103

Project 2

Good Design: Data Modeling and the Predictability of the “Kick Start” Mechanism

Predictability and reliability are certainly two necessary features of a well-designed machine. This can be achieved by eliminating or minimizing all components that may lead to unpredictable behavior. The simplest design that can perform the required tasks should always be in the short-list of candidates for a prototype. On the other hand, common machines require humans as operators, and humans as complex systems work under the influence of many factors that easily lead to unpredictable behavior. Minimizing human intervention, ideally to an intelligent ‘digital” response of yes/no, helps restore predictability. For example, I used to drive a long distance on a regular basis, by using highways, very-low-traffic schedules, and cruise control, my total travel time from point A to point B could be predicted within 1% of error.

In this project, you are required to work in teams to do data modeling to demonstrate the predictability of a “kick start” mechanism. The “kick start” mechanism will be a simple pendulum with released at different initial heights, it will then kick a vehicle at its lowest point (highest velocity). You will be asked to predict how far the vehicle will move forward, using data modeling. That is, you will need to model the data (X: initial height of pendulum; Y: distance travelled by vehicle) by relating them with the best equation Y’=f(X), where f could be a polynomial in X. Once the best equation is obtained, it will be possible to predict the distance travelled Y’ for any initial height X. If you took into consideration engineering principles and how to eliminate unpredictable behaviors as discussed above, your system should be predictable, that is, the predicted value Y’ is very close to the actual travelled distance Y. Each team should present in both days to receive full credits awarded to project completion and presentation (see credit table below).

In this project, the vehicle could be a commercial toy one or built by the team, the focus will be on data modeling and predictability so do not spend too much time in building a nice vehicle. The team will use data modeling or curve-fitting (see CW 3 and 4) with Excel (© by Microsoft) to make a model the (X,Y) data as explained above. If you can do a calibration of your vehicle performance using data modeling, then it is predictable. The calibration using data modeling is device-specific, it needs to be redone if any change has been made to the vehicle or ramp.

Important questions to be considered:

Can I predict the distance travelled by the vehicle using Physics equation?

What are the differences between the data-modeling prediction and the Physics prediction?

Total materials cost should be less than $30, copies of receipts to be submitted with project reports.

Each team will do a 5 minute presention on their device in each of the two presentation days. In the first day, the teams will give an introduction (what they did, how they achieved predictability, etc.) followed by the demonstrations. The webpage on the project, along with the project report will be due the class after the second day of the presentations (please check the e-syllabus for exact dates). A progress report on the project will be due about a week before the first of the two presentation days (please check the e-syllabus for exact date). The project report is expected to be a good written document (see Good Writing Practices), and graded under three categories: correct grammar and neat presentation; logical arguments and structure; accurate report of the team project, completeness, and no plagiarism. Project report will be submitted in hardcopies with member signatures and also in electronic form (see Computer Files: Names and Electronic Submissions). A complete report should include the following sections:

-Introduction: brief description of project objectives in your own words, background information needed for the design with emphasis on predictability of the ”kick start” mechanism, work distribution among the team members, and timelines for the different parts of the project: research, design, building, analysis/calibration.

-Design and building: this section should include sketches and diagrams: how the different design elements and hardware components were selected enhance the predictability of the mechanism, the list of components with specification and prices.

-Analysis/Calibration: should include data obtained from your device: tables of X and Y, insert of Excel worksheet with discussions of different types of function relating those two variables and pinpointing which function is the best (with the smallest “standard deviation”, see CW 3 and 4). It should also include results from testing the prediction made by this model and an assessment of the predictabily of your device. It should also include a brief manual of operation, troubleshouting list, and appropriate recognition of other author’s materials if used in your project.

-Conclusion: overview of the team achivement and lections learned for the future.

Grades will be computed as follows:

Items			Points
Project completed, if presented both days			50
Project performance (performed specified tasks)			10
Good design (predictability: existence of a function relating starting force to resulting rotation)			10
Project presentation and webpage			10
Written reports	Progress report		5
	Project report: will not accepted without all member’s signatures on percentage of participation	Grammar and presentation	5
		Logical arguments; structure	5
		Accurate and complete	5
Total project grade			100