Umbrella check-out system

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Revision as of 20:58, 5 December 2008 (view source) Bberkowi (Talk | contribs) (→Table 1: List of parts) ← Older edit		Revision as of 21:04, 5 December 2008 (view source) Mcushman (Talk | contribs) (→Quantitative Analysis: Traffic Study) Newer edit →
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	===Quantitative Analysis: Traffic Study===		===Quantitative Analysis: Traffic Study===
-	Summarize our findings here, link back to main analysis page	+	Mechanical Analysis
		+
		+	====Motivations====
		+	The physical construction of our device is relatively simple. It contains few components, and those components are already well-developed and commonly used. The size of the device is constrained by the umbrellas, and device weight is not a large concern since it will be stationary. Given this, we did not feel that a strictly mechanical analysis would provide us the insight that we need to develop a complete solution. In addition to the design of the hardware, the issue of implementing the system had to be addressed. This is where we chose to focus our analysis.
		+
		+	The largest issue of implementation is determining where to place the units, and how many modular units to put at each location. Traffic flow into and out of each doorway is the major determinant of this, so we decided to find out what the traffic flow was like as a function of time at the major doorways on campus. By determining the flow rate and difference in number of umbrellas at each location we can determine the number of umbrellas needed at each location for a given time, and the speed at which these umbrellas need to dry in order to be ready for reuse.
		+
		+	====Methods====
		+	Two methods were used to investigate traffic flow at the major doorways.  The first was to model our campus using a [[Umbrella_Traffic_Flow_Analysis#Model | Markov simulation]].  This is model that uses probabilities of users moving from one location to another to estimate overall traffic flux around campus.
		+
		+	The second method was sitting by doorways and [[Umbrella_Traffic_Flow_Analysis#Counter | counting]] people going into and out of each doorway.  Data was logged, time stamped, and analyzed later.
		+
		+	====Findings====
		+	Our largest learning from these analyses is that it is very difficult to model traffic flow.  Personally counting everyone moving through a given doorway is time-intensive.  Modeling traffic flow is faster, but the number of [[Umbrella_Traffic_Flow_Analysis#Assumptions | assumptions]] necessary to produce a functional model may invalidate the results.  Even if we could accurately model traffic flow on campus for a rainy day, the existence of the checkout system could alter traffic patterns significantly.
		+
		+	From observing people moving in and out of doorways, we got an order of magnitude estimation of necessary umbrella capacity at the major locations.  Our analysis shows that it will take a capacity of approximately 200 to 300 umbrellas at the major doorways on campus.
		+
		+	The simplest solution to solve the modeling and estimation inaccuracies is to overpopulate the system initially, and then move units around as needed to meet demand.  A full analysis to get a better estimate of traffic flow would be to difficult, if not impossible, to achieve, and would require many hours to realize.  Overpopulation of units is simple and fast.
	==Conclusions==		==Conclusions==

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Revision as of 21:04, 5 December 2008

Final Wiki outline for ED2

Contents 1 Executive Summary 2 Design 2.1 Objectives and Opportunities 2.2 Physical Design 3 Table 1: Bill of Materials 3.1 Distribution and Placement 3.2 Failure Modes 3.3 Manufacturing and Production 3.4 Prototype History 3.4.1 First Prototype 3.4.2 Second Prototype 3.4.3 Final Concept 4 Analysis 4.1 Comparison of Alternatives 4.2 Quantitative Analysis: Traffic Study 4.2.1 Motivations 4.2.2 Methods 4.2.3 Findings 5 Conclusions

Executive Summary

Design

Objectives and Opportunities

Why we believe that this is a feasible idea, user study quotes

Physical Design

Component design, and how the unit operates.

Table 1: Bill of Materials

Table 1: Components of Umbralla Dispensing unit

Part Number	Name	Qty.	Function	Material	Manufacturing Process	Image	Engineering Drawing	Vendor Info
1	Dispenser Shell	1	Encloses unit	18 Gauge Steel	Punching, Bending			Not Applicable
2	Short Support	2	Maintains rigidity of unit	Steel	Cold Rolling			Not Applicable
3	Long Support	2	Maintains rigidity of unit	Steel	Cold Rolling			Not Applicable
1	Part 1	1	does stuff	Steel	Stamping	Image:Imagename.jpg	Image:Imagename.jpg	Image:Imagename.jpg

Distribution and Placement

Discuss how units would be placed and distributed.

Failure Modes

FMEA table

Manufacturing and Production

DFMA details, production volume

Prototype History

Discuss changes in our design through time.

First Prototype

Our initial prototype was designed to be a small modular unit with a simple design and low part count. The flat sides of the device allow multiple units to be placed next to each other for an umbrella distribution with multiple locations to take out and return umbrellas. The body of the device was built with wood material and painted red. The ramp on the top of the device, which was made of low-friction teflon material, moves umbrellas quickly from the receiving end to the dispensing end with no additional mechanical input from the system. Keeping the number of the device low allows the device to be reliable. The only places where the umbrellas need to be actively dealt with by the system are the input and output chutes. For the input chute, there was a latch attached to torsional spring to make sure that umbrellas cannot taken out from the receiving end once they are returned. Also, for the output chute, a turnstile was implemented to make sure that one umbrella is taken out at a time.

Second Prototype

Changes for our second prototype. -cardswipe activates electronic system -motor to power turnstile - will be replaced with worm drive to prevent backturning -also plan to add an encoder for better accuracy

-ventilation holes in side of box -ramp on inlet for umbrellas -removable drip tray -purchased umbrellas for use in system, cost: ~$3 each, lathed handles to fit unit

-evaluated barcode scanning -difficult and expensive to interface with microcontroller -now looking at RFID scanner from Parallax

-quantitative analysis -People counting, Markov model, computer vision tracking -big results: major entrances will need to carry ~2-300 umbrellas, gives us a ballpark of number of units necessary at those locations

Final Concept

Differences between our second and final prototypes.

Analysis

Comparison of Alternatives

QFD, etc can go here.

Quantitative Analysis: Traffic Study

Mechanical Analysis

Motivations

The physical construction of our device is relatively simple. It contains few components, and those components are already well-developed and commonly used. The size of the device is constrained by the umbrellas, and device weight is not a large concern since it will be stationary. Given this, we did not feel that a strictly mechanical analysis would provide us the insight that we need to develop a complete solution. In addition to the design of the hardware, the issue of implementing the system had to be addressed. This is where we chose to focus our analysis.

The largest issue of implementation is determining where to place the units, and how many modular units to put at each location. Traffic flow into and out of each doorway is the major determinant of this, so we decided to find out what the traffic flow was like as a function of time at the major doorways on campus. By determining the flow rate and difference in number of umbrellas at each location we can determine the number of umbrellas needed at each location for a given time, and the speed at which these umbrellas need to dry in order to be ready for reuse.

Methods

Two methods were used to investigate traffic flow at the major doorways. The first was to model our campus using a Markov simulation. This is model that uses probabilities of users moving from one location to another to estimate overall traffic flux around campus.

The second method was sitting by doorways and counting people going into and out of each doorway. Data was logged, time stamped, and analyzed later.

Findings

Our largest learning from these analyses is that it is very difficult to model traffic flow. Personally counting everyone moving through a given doorway is time-intensive. Modeling traffic flow is faster, but the number of assumptions necessary to produce a functional model may invalidate the results. Even if we could accurately model traffic flow on campus for a rainy day, the existence of the checkout system could alter traffic patterns significantly.

From observing people moving in and out of doorways, we got an order of magnitude estimation of necessary umbrella capacity at the major locations. Our analysis shows that it will take a capacity of approximately 200 to 300 umbrellas at the major doorways on campus.

The simplest solution to solve the modeling and estimation inaccuracies is to overpopulate the system initially, and then move units around as needed to meet demand. A full analysis to get a better estimate of traffic flow would be to difficult, if not impossible, to achieve, and would require many hours to realize. Overpopulation of units is simple and fast.

Conclusions