Summary
In this activity, team C was tasked to evaluate the designs our team members have made and to decide which ones need to be redeveloped, modified, or just scrapped. We were to create two different charts, called “scoring and screening matrices”, which would be used to do the evaluation. The “screening matrix” is a chart consisting of 4 categories for the vehicles to be evaluated on. The “scoring matrix” is a chart which is also used for evaluation, but is more quantitative.
Team C’s “concept screening” matrix is shown below.
| Concept Screening Matrix | |||||||||||||||||
| Success Criteria | Reference AEV | Design #1 | Design #2 | Design #3 | Design #4 | ||||||||||||
| Minimal Blockage | 0 | + | 0 | 0 | 0 | ||||||||||||
| Maintenance | 0 | + | 0 | – | + | ||||||||||||
| Durability | 0 | 0 | 0 | – | 0 | ||||||||||||
| Safety | 0 | 0 | 0 | 0 | 0 | ||||||||||||
| Sum +’s | 0 | 2 | 0 | 0 | 1 | ||||||||||||
| Sum 0’s | 5 | 2 | 0 | 1 | 0 | ||||||||||||
| Sum -‘s | 0 | 0 | 0 | 2 | 0 | ||||||||||||
| Net Score | 0 | 2 | 0 | -2 | 1 | ||||||||||||
| Continue? | No | Yes | No | No | Revise | ||||||||||||
As shown in the chart above, each AEV was judged based on its performance and efficiency in specific categories. A 0 was given if the model was no different than the original AEV design in a specific category, a – was given if it was worse, and a + was given if it improved in that category. Each of the +’s 0’s and -‘s are scored up, and a total score is given. We used this method to judge which model would be the best. Based off of the screening matrix, we have decided to continue design #1, and to modify design #4 for further testing.
| Concept Scoring Matrix | ||||||||||||||
| Reference Aev | Design #1 | Design #2 | Design #3 | Design #4 | ||||||||||
| Success Criteria | Weight | Rating | Score | Rating | Score | Rating | Score | Rating | Score | Rating | Score | |||
| Stability | 20% | 3 | 4 | 3 | 2 | 3 | ||||||||
| Minimal Blockage | 15% | 3 | 4 | 1 | 1 | 2 | ||||||||
| Maintenance | 15% | 2 | 3 | 2 | 2 | 3 | ||||||||
| Durability | 25% | 3 | 3 | 2 | 1 | 3 | ||||||||
| Safety | 25% | 3 | 2 | 2 | 2 | 3 | ||||||||
| Total Score | 2.85 | 3.35 | 2.05 | 1.6 | 2.85 | |||||||||
| Continue? | No | Yes | No | No | No | |||||||||
As shown in the chart above, we scored each model based on its performance in each of the categories on a scale of 1-5, 1 being the worst, and 5 being the best. We summed up each of the scores given and came to a conclusion as to which AEV worked the best. Through checking the highest final score, our team has decided to overall continue Design #1, while using some qualities from the other designs which excelled in some categories. Design #1 shall be continued, design #4 shall be revised, and designs #2 and #3 shall be scrapped.
Takeaways
Our team, by completing this lab, has concluded how important comparing and contrasting designs with your team members is. Even if a single model isn’t perfect, using parts of it which work well in one compiled model could help your team create an efficient AEV overall. These matrices help us see which model succeeds the greatest in which category, and can give us good oversight on these qualities.