Best advice to Formula SAE, Baja SAE and any other student built projects.

This video can be the introduction to any Formula SAE, Baja SAE, and any other SAE Collegiate Design Series competition.

Questions like:

“How do I start designing a Formula SAE vehicle?”

“How do I organize a Formula SAE team?”

and many others are answered here.

httpvh://vimeo.com/41854831

My favorite two parts:

“Build an A team first and a C car first; and then… you will end up with and A team and A car” (4:40)

“Design, then Manufacture” (14:37)

What do you think about his advice?

How important is the Design event in Formula SAE?

In this post I’m going to discuss the importance of the design event in Formula SAE competitions.  It will start with a brief explanation of the design event, and tips on how to do well on the event. Using the results from the past competitions answer the question:

How important is the design event in Formula SAE?

Now lets start with: What is the design event?

The design event is part of the static events at Formula SAE competitions.  Its purpose is to judge the students engineering effort into the design of the vehicle.  As stated by the rules:

“The car that illustrates the best use of engineering to meet the design goals and the best understanding of the design by the team members will win the design event.”

Each team has approximately 30 minutes for their presentation and they are divided into:

  • Set up – 3 minutes: for placing your car (in finished condition), students and any other materials for presentation in the judging area.
  • Introduction – 1 to 4 minutes: where the team can present the car, their goals, and mention whatever they want to emphasis about the car.
  • Judges Q&A 25 to 28 minutes: Here the judges will ask the students the fundamentals about the car, its design, governing physics, and validation.
  • After the Q & A the team has to let clear the area quickly for the next one in line.

Tips on how to prepare for the design event:

  • Design report and Design spec sheet: the judges will read this information before the design event.  Consider the design report as the resume of your car, it should emphasize the strong design parts of the vehicle.
  • One student per judge: a minimum of one student at all times per judge; the judges want to see that the team has an understanding of the vehicle and score less teams where one student answers all the questions.  I would recommend at least 2 students per judge.
  • Presentation material availability: have your data, analysis and everything else that you might want to show the judges near and available.  Here is where posters, binders and parts prototypes help to explain your car to the judges.
  • Questions the judges want you to answer: students.sae.org has a document with these questions (find it here).
  • End of Design Q&A: leave pictures of your car with the judges, it is allowed by rule C5.14 and helps the judges remember your car.

Now lets move to the question: How important is the design event for the overall Formula SAE competition?

endurance vs design fsae

I started by collecting results from Formula SAE competitions in the USA (a total of 12 competitions between 2006 and 2013).  From the results collected, design, endurance, and overall scores where extracted.

First the data of one competition is explored using a scatter plot of the endurance vs design score.

enduranceVsDesignFSAE2010

The plot above shows visually signs of a linear relationship between the scores.  To investigate further the mean of the endurance scores is plotted vs the design scores below.

 meanEnduranceVsDesignFSAE2010

Here a linear relationship between design and endurance score is more visible.  In order to confirm formally this linear relationship the Pearson correlation coefficient was calculated between each of these scores.  This coefficient measures the linear relationship between two variables.  And here the variables used were design and endurance scores and then design and overall scores.

FSAEdesigncorrelation

To summarize, all correlation coefficients were significant (p < 0.05) with most of them attaining higher significance (p<0.01).  The mean of the correlation between  design and endurance score is 0.484 and the mean between design and overall score is 0.730.  Unfortunately, there is no established threshold value for the Pearson correlation coefficient to establish a linear relationship between two variables, here due to the nature of all the uncertainty and complexity of the competition a perfect correlation was not expected.  However, there are a number of conclusions that can be extracted from the data.  First, since the correlations are positive this means that the design event score is proportional to the endurance and overall score.

Knowing the possibility of a linear relationship between design and endurance, and design and overall score, linear regression is used to find the contribution of the design score to these events.  The linear regression used the design score as the independent variable and endurance or total score as the dependent variable (see equations below).

LinearRegressionEndurance LinearRegressionOverall

The Beta coefficients are summarize in the following table, with the significant of Beta 1.

 

RegressionFSAEsummary EnduranceRegressionFSAEsummary Total

The Beta 1 coefficient quantifies how much the endurance and overall score is increased by increasing the design score by 1 point.  For the endurance score between all the competitions reported here for an increase of 1 point in design a mean of 1.695 points are increased in endurance; in the overall score for each point increase in design, a mean of 5.343 points are increased in the overall score.  A clear picture is established when revising the standardized Beta 1 coefficient, which measures the effect or contribution of the independent variable (design score) to the dependent variable (endurance or overall score).  On average the design event score can predict about 50% of the endurance score and 73% of the overall score.

Throughout the discussion of the correlation results it was assumed that the design event was the causation for the other scores.  This was assumed because a team that was able to prove the design judges that their design is correct and meets the competition goals is the one that will perform better at the dynamic events, like endurance.  In the opposite way, a team doing well at the dynamic events will also be likely to have a good score in the design event, but this is because in order to have good dynamic scores, teams have to do their homework and design correctly the car for the competition objectives.  This post when referring to design is referring to good design that also involves manufacturing and testing!

With this knowledge, teams on all levels should understand that the tenth of a second that they needed or the saving of 10 pounds (4.53 kg) can be better found at the design stage. Give the design competition the importance that it has.  Think of it as if were 750 points out of the 1,000 points of the competition because according to the numbers shown before that statement is not that far from true.

I would like to know your thoughts, opinions or stories about the design event and how it influenced the dynamic events.

 

 

PS:  The idea for this post was a product of good conversations at the Formula SAE Michigan 2013 and Baja SAE RIT 2013 competitions.  In the conversations the question of how important is the design event was brought to my attention and I try to answer it here to some extent.

Pugh Method: How to decide between different designs?

How can engineers decide systematically between different designs? How can engineers do a concept evaluation and selection?

One method, called Pugh method, helps engineers in design decisions by establishing a procedure to choose the best design from the considered designs.  This method is also known as Decision-Matrix Method or Pugh Concept Selection.  There are variations of the method however I’m going to explain here how I use it.

Step 1:  Make a list of the criteria that you want to compare between different designs.  Each criterion should be an objectively quantifiable measure.

Criteria
Criterion 1
Criterion 2
Criterion M

 

Step 2: Establish weights factors for each criterion.  A number between 1 and 10 can be chosen for each criteria, the bigger the scale the more experienced you should be to impose the weights.  Other approach can be to distribute a number of points (e.g. 100) between all criterions.  This step can be challenging for novice engineers, one way to overcome this is to just classify them in a 3-point scale where 1 is important, 2 is very important and 3 is extremely important.  The last option is to omit the use of the weights; this would mean that all criterions are equally important.  Whatever weight approach you choose I have to warn that the design selected is influenced by the selection of the weights.  The last issue before passing to the next step is that the order matters when you use this method, establish the weights factor before any analysis is made!  Otherwise you will be unconsciously biased toward one design and assign weights that benefit the strong criterions of that particular design.

Criteria Weights
Criterion 1

3

Criterion 2

2

Criterion M

3

 

Step 3:  Generation of different designs.  The designs can be generated with Brainstorming, or TRIZ just to mention two examples.  However the way to generate the designs is not the focus here.  The number of designs to evaluate will depend on the complexity of the product being designed.  That being said I would advice not to do a Pugh matrix for just 2 designs, in practice something between 3 to 7 designs could be compared.  At first generate as many designs as possible but then filter them to a manageable quantity.

Criteria Weights Design 1 Design 2 Design N
Criterion 1

3

Criterion 2

2

Criterion M

3

 

 

Step 4: Analysis of designs.  This is the step were the classical engineering takes place.  You will quantify mass, energy lost, stress, flow, etc.  All the criterions will need an analysis to quantify it, thus those numbers will have units.

Criteria Weights Design 1 Design 2 Design N
Criterion 1 Analysis

3

#.## [Kg]

#.## [Kg]

#.## [Kg]

Criterion 2Analysis

2

#.## %

#.## %

#.## %

Criterion MAnalysis

3

#.## [MPa]

#.## [MPa]

#.## [MPa]

 

Step 5: Fill the matrix.  Now for each design a number has to be calculated to fill its criterion cell.

Criteria Weights Design 1 Design 2 Design N
Criterion 1

3

?

?

?

Criterion 2

2

?

?

?

Criterion M

3

?

?

?

Again, there is more than one way to do this.  A common way is to establish one of the designs as the Datum design, and compare the other designs criterion analysis numbers (from Step 4) against the Datum design.  A scale is established beforehand, a common one goes from -3 to 3.  If the design is better than the Datum it will get a positive number and the magnitude of the number depends on how much better it is.

After using this approach, I started to modify it in order to have a minimal number of decisions based on the designer assessment of the analysis numbers.  So instead of choosing a number between -3 and 3, I calculated one.  The procedure starts by calculating the average across designs for the criterions.   Then that average is subtracted to each design criterion and that is the number that is input into the decision matrix.

Criteria Weights Design 1 Design 2 Design N
Criterion 1

3

±#.##

±#.##

±#.##

Criterion 2

2

±#.##

±#.##

±#.##

Criterion M

3

±#.##

±#.##

±#.##

 

Step 6: Calculate each design score.  This is done by multiplying each criterion weight by the design cell value (±#.##) and summing all the values for the design.  This procedure is repeated for all designs.  Then the design with the higher score is the best design and the decision was made taken into consideration all of the criterions and designs in an objective manner.

Criteria Weights Design 1 Design 2 Design N
Criterion 1

3

±#.##

±#.##

±#.##

Criterion 2

2

±#.##

±#.##

±#.##

Criterion M

3

±#.##

±#.##

±#.##

Total:

#.##

#.##

#.##

 

 

Now that the steps are explained, we can go over a specific example.  Since a previous post already discussed Baja and Formula SAE Frame Design we are going to use a frame / chassis as the example for the Pugh Method (decision-matrix method).

 

Step 1: Make a list of the criteria that you want to compare between different designs.

  • Torsional Stiffness
  • Torsional Stiffness to Weight ratio
  • Frontal Impact (Max Stress)
  • Roll Over (Max Stress)
  • CG height
  • Weight

Step 2: Establish weight factors for each criterion.  In this case choose a number between 1 and 10.

Criteria Weight (1-10)
Torsional Stiffness 9
Torsional Stiffness to weight ratio 10
Frontal Impact 7
Roll Over 8
CG height 8

 

Step 3: Generate Different Designs.

 

Step 4: Analysis of designs.

Criteria Design 1 Design 2 Design 3 Design 4 Design 5 Design 6
Torsional Stiffness [lbf-deg] 857.81 1057.3 1128.5 1444.9 1009.26 1430.8
Torsional Stiffness to weight ratio 14.767 17.595 18.761 32.293 16.877 23.141
Frontal Impact [psi] 53,011 47,775 38,961 24,444 36,791 26,238
Roll Over [psi] 33,929 28,835 30,995 28,174 36,176 32,705
CG height [in.] 9.64 9.47 9.94 9.78 9.77 9.60

 

Step 5: Fill in the matrix.  In this case each criteria was averaged across designs.  Then each criteria average was subtracted from each design criterion.  This is known as to center the values.  See the example below.

Criteria Design 1 Design 2 Design 3 Design 4 Design 5 Design 6 Average
Torsional Stiffness [lbf-deg]

857.81

1,057.3

1,128.5

1,444.9

1,009.26

1,430.8

1,154.76 (Average of all designs TS)

= Criterion-Average

857.81-1,154.76 =

 -296.95

 

Then the procedure is repeated for the whole table.

Criteria Design 1 Design 2 Design 3 Design 4 Design 5 Design 6 Average
Torsional Stiffness [lbf-deg]

8,57.81

1,057.3

1,128.5

1,444.9

1,009.26

1,430.8

1,154.76

= Criterion-Average

-296.95

-97.46

-26.26

290.13

-145.50

276.04

Torsional Stiffness to weight ratio

14.767

17.595

18.761

32.293

16.877

23.141

20.57

= Criterion-Average

-5.80

-2.98

-1.81

11.72

-3.69

2.56

Frontal Impact [psi]

53,011

47,775

38,961

24,444

36,791

26238

37,870

= Criterion-Average

15,141

9,905

1,091

-13,426

-1,079

-11632

Roll Over [psi]

33,929

28,835

30,995

28,174

36,176

32705

31,802.33

= Criterion-Average

2,127

-2,967

-807

-3,628

4,374

902.67

CG height [in.]

9.64

9.47

9.94

9.78

9.77

9.6

9.7

= Criterion-Average

-0.06

-0.23

0.24

0.08

0.07

-0.1

The only problem now is that each criterion is on different scales, we want to  have all in the same scale.  This can be accomplished by dividing each centered value by the biggest value for that criterion.  The resulting table should look like this:

Criteria Weight Design 1 Design 2 Design 3 Design 4 Design 5 Design 6
Torsional Stiffness [lbf-deg]

9

-1.0234

-0.3359

-0.0905

1

-0.5014

0.9514

Torsional Stiffness to weight ratio

10

-0.4953

-0.2540

-0.1545

1

-0.3152

0.2191

Frontal Impact [psi]

7

1

0.6541

0.0720

-0.8867

-0.0712

-0.7682

Roll Over [psi]

8

0.4862

-0.6784

-0.1845

-0.8295

1

0.2063

CG height [in.]

8

-0.25

-0.9583

1

0.3333

0.2916

-0.4166

 

Step 6: Calculate each design score. See the example for Design 1

Criteria Weight Design 1
Torsional Stiffness [lbf-deg]

9

-1.0234

Torsional Stiffness to weight ratio

10

-0.4953

Frontal Impact [psi]

7

1

Roll Over [psi]

8

0.4862

CG height [in.]

8

-0.25

Totals

9 x (-1.02) +10 x ( -0.49) + 7 x 1 +8 x 0.48 + 8 x (-0.25) = -5.2744

 

This is the final Pugh Decision Matrix

Criteria Weight Design 1 Design 2 Design 3 Design 4 Design 5 Design 6
Torsional Stiffness [lbf-deg]

9

-1.0234

-0.3359

-0.0905

1

-0.5014

0.9514

Torsional Stiffness to weight ratio

10

-0.4953

-0.2540

-0.1545

1

-0.3152

0.2191

Frontal Impact [psi]

7

1

0.6541

0.0720

-0.8867

-0.0712

-0.7682

Roll Over [psi]

8

0.4862

-0.6784

-0.1845

-0.8295

1

0.2063

CG height [in.]

8

-0.25

-0.9583

1

0.3333

0.2916

-0.4166

Totals

-5.2744

-14.0784

4.6676

8.8228

2.1682

3.6942

 

Design 4 is the design that the decision matrix chose based in the analysis and weight factors.  With the specific procedure carried here, once the designer establish the criterion weights, all other numbers are calculated without need of the designer to interpret or assign ratings to the designs.

As was mentioned in the description of the general steps there are many variations to the Pugh method.  This is the version that I ended up using, after using it over the years for Formula SAE design decision-making.

Baja and Formula SAE Frame Design

The purpose of this post is to give an idea of how to design a tubular space frame for the Baja or Formula SAE competitions. This is the procedure that I have come up with after being involved in the design, construction and testing of frames for Formula SAE vehicles.

First, what is a frame? What is its role in the vehicle? The frame is a bracket that holds many systems of the car together. The frame also transmits the loads of the suspension! These two are the two most important general roles of the frame.

Where do you start? I have experienced myself through the years all the possible combinations: define suspension points and engine first, then design the frame and adapt systems to the frame design, to the other end where you let all your systems floating in space and design a frame around the systems. My conclusion so far is that you should try to design everything at once and iterate as much as possible. This is because the frame is another system of the car!

Where to start? Pencil and paper, with a sketch, many sketches. The idea at this stage is to generate as many designs as possible. In your sketch of the frame try to also incorporate other systems (e.g. engine).  When sketching first just draw the required rules members and then add the rest.  Also have in mind the manufacturability of the design (angles of notches and diameter of tube bends).  Once the sketches are generated look at them and start to combine the good parts of the sketches and leave the parts you don’t like. At the end choose at least 4 designs but no more than 8. Then decide what are going to be the metrics by which you will judge the design (e.g. weight, cg, torsional stiffness).

This leaves us with the task to model the frame in CAD software. It does not matter what software you are using these steps are generalized:

1. Make a hand drawn sketch with front, side and top view.

2. Identify all the nodes of the sketch and number them.

3. Make a table with the coordinates of all the nodes (at this point these will be rough numbers but the idea is to start, they can be changed later).

4. Now open your prefer CAD software.

5. Create all the points from the table in step 3.

6. Draw lines between points (for curve sections a center point of the arc is needed most of the time).

7. Then almost all software packages have a piping, frame or beam toolbox where you can select the beam cross section and apply it to the line.  This step can vary greatly between different CAD software, but the idea underneath is the same.

8. Most likely the beams are crossing each other at the nodes, thus usually the same toolbar where the cross sections were applied to the lines will have a mating or coupling section where you can specify the connectivity between them (which tube goes first and which one is notched).

9. Save.

Once you have the model go into assembly mode and start adding all the components even if they are not completely designed.  At this point the integration between systems starts an iteration process.  At the same time, the metrics by which the design of the chassis was going to be chosen now can be calculated.

Steps 4 through 8 are shown using PTC CREO 2.0


These post will always be evolving and if you have any suggestions to improve it feel free to comment below or send me an email JLugo{at}ND.edu. Thanks to Bob Kobayashi and Oliver Chmell for their suggestions.

Time Management

Time management is a topic that is not mentioned often in engineering designs or manufacture reviews.  Sure expectations for deadlines are mentioned, and the status to completion of a task is mentioned too, but how to manage time and what are the consequences of bad time management aren’t mentioned that often.  It is this second that I want to share an example I learned when I was a Formula SAE student (Univ. of Puerto Rico).  Now that I’m a graduate student it can help the Baja SAE team (Univ. of Notre Dame) that I’m helping out and all others reading.

All teams are composed of people that work different, some are efficient and others have to put more hours to complete the same job.  If this is not your case stop reading here and congratulations; but for the rest of us:  What happens when someone can’t finish a task by the deadline?  Examples from my Formula SAE years: (a) a student that was supposed to work on “X” suddenly disappears after midterms (b) the design doesn’t work, (c) the machining was ruined, and so on.  What is the resulting workload for those that stayed to finish the work (finish the car)?

Here is what I learned. The simplest scenario is composed of two players and each one of them had to complete 50% of the work by midterms and the other 50% by the end of the semester. In this case everything was perfect and no one had to work more.

Now, let’s say that student 2 completed 50% of the work by midterms, but then at that point in time is notified that student 1 left the team and never completed his/her work.  Reasons for leaving a team can range from lowering grades, up to group dynamics.  Now student 2 has to finish the original workload and add the work from student 1.  The point and eye opener is that student 2 now has to triple the amount of work (150%), when compared to the first part of the semester.

If you are working in any of the SAE collegiate design competitions and feel that close to the end you are working much more than in the beginning this might be one of the reasons.  In this simple example when someone doesn’t finish the work, the result is that it triples the workload for the one that stays!  Very likely, if you are reading this, you are the one that sticks, just have this triple factor in mind next time.  Remember that time is a scarce nonrenewable resource.  This was the first of a series of posts in which I will discuss and share a few things of what I’ve learned in Formula SAE.