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Quantifying Design Aesthetics – My TEDxUND talk

A few years ago I started watching TED talks over the internet, mostly during lunch time.  The talks’ topics that I watched were mostly design, engineering, and art related.  When I learned that The University of Notre Dame was going to hold a TEDx event I applied to be a speaker, to share my research with the TEDx community.  After the selection process, I was one of the 19 speakers chosen!

The email read:

“We were very impressed with your ideas and your passion, and as such, we are officially inviting you to perform at TEDxUND 2014, to be held on January 21st, 2014, at the DeBartolo Performing Arts Center…”

Then the preparation process started.  I had to explain my research in 12 minutes!  I’m just going to comment that once I understood that the purpose of the TEDx talks is to share ideas, it enabled me to focus only on the essential information needed. The organizers were very supportive giving speakers the tools needed to present on stage, which help me gain the confidence to stand in front of the TEDx audience.  The talk was streamed real-time and last week a recording of the presentation was posted on the TEDx Talks YouTube channel.  Here is my presentation:

After the talk, I had a good time talking to people and answering questions.  The most common question was: Where was the “beauty” number of each wheel rim? For each of the wheel rims I only presented three quantified Gestalt principles but there were more. Nevertheless, they weren’t shown for simplicity; remember the goal was to share an idea, not to present years of research in 12 minutes.  To summarize the “beauty” number is a unit vector and its dimensions are equal to the number of quantified Gestalt principles.  With that said, if you really want a scalar number, then is just a matter of taking the Euclidean norm of the vector.

The second comment was regarding the complexity of the wheel rims and now looking at the video it seems like I passed these slides quickly so here you can see the two examples of wheel rims with similar complexity that were not shown in the youtube video (at 8:26).

Wheel rims with similar complexity

Wheel rims with similar complexity.

People didn’t ask me about the equations to quantify the Gestalt principles. I don’t know if it was because they were shown briefly; this was on purpose as time was limited.  I think that here it is appropriate to share that slide.

Summary Slide of Gestalt Principles Equations

This is just a summary and more information regarding the equations can be found at my webpage.

Lastly, I want to thank all the people from the University of Notre Dame that helped me prepare for the talk.  Also, I want to thank everyone from academia, industry and friends that have guided me through this research journey questioning, challenging and inspiring myself to do good research.

When we solve problems together, we don’t only solve them, we also create new knowledge together…

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.

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.

University of Notre Dame participation on SAE Baja Competition (continuation)

  • Day 2: Design Evaluation/ Technical and Dynamic Brake Inspections

Day 2 started very early because the technical and dynamic brake inspections, and design evaluation took place. The technical inspection needs to be passed in order to advance in the competition, and with 105 registered teams it is important to be early in line. The Notre Dame Baja car #81, after passing all the inspections described above the car gets a sticker as you can see below.

Baja_SAE_Tech_Sticker_Notre_Dame_Car

 

The design event took place at 1:00pm. This is an engineering competition and the students needed to show their designs, analysis and validation of their vehicle. There, students Matt Hubbard, Matt Goedke, Ted Docherty and John Fisher presented the vehicle engineering design. The car has a unique drive train / rear suspension configuration that was explained to the design judges to convince them that it will have a superior performance than the other vehicles.

At the end of the day, the team went to the dynamic brake inspection where they needed to show that the vehicle could lock all four tires at the same time.  Student Matt Hubbard drove the car and was able to pass the test.  This completes the sticker and the car is ready for the dynamics events.

Baja_SAE_Tech_Sticker_Notre_Dame

  • Day 3: Dynamic Events

Today the competition took place at Hogback Hill MX in Palmyra, NY.  This is a motocross facility where the dynamics events take place.  Each of the dynamics events taking place today has two hits.  The team staged the car first for the hill climb event; here the car has to climb a very steep hill from a standing start.  In the first try the car wasn’t able to clear the hill, but since the line for the second hit was long the team decided to go to Land Maneuverability.  Here things like the vehicle turning radius were tested in a course with many twists, turns, and slaloms.  Due to the complexity of the track, on the first run the car went off course but on the second run the car was able to complete the event.  The last event of the day was Suspension and Traction (see picture below): this event tests the suspension travel, vehicle ground clearance, and ability to go over very rough terrain.  For this event the team decided to complete only the obstacles that didn’t pose a tread to the vehicle, as many vehicles were breaking trying to complete some of the obstacles and the amount of points in the event was not worth the chance of breaking the car just before the endurance competition.

After all the day events finished, the drivers walked the endurance track.  A video of the track was taken for drivers further review of the track.

  • Day 4: Endurance

The only event today is the endurance event; it consists of a four-hour race.  The track conditions were muddier than expected by the team.  Since by the rules tires can’t be changed after technical inspection, the team had to run with tires not specified for mud making the car slower on those sections of the track.  The car was able to climb every hill and pass every obstacle of the endurance track.  This is important because if a car gets stuck or needs help to pass a section more than 3 times it gets disqualified.  About half an hour into the race, the engine died and the car was taken to the pit area.  It is important to mention and highlight that all cars use the same engine without any modifications.  After a first inspection the team found that the starter cable was broken, it was replaced and the engine still was not starting, and making now a grinding noise.  A technician from Briggs & Stratton was on site to help teams and helped the team debug the problem, a damage spark cable.  The team moved quickly to make all the repairs and the car was able to go back to the race about an hour later.  Then, 45 minutes to the completion of the four hours and completing 12 laps one of the belts of the drive train broke.  This took the car out of the race since the replacement of the belt couldn’t be finished before the end of the race.

Baja_SAE_Endurance_Notre_Dame

After all the dynamics events of the competition the car is structurally without any failure.  The team will work to understand why the belt failed, fix the car and pass it on to the next team as a workbench to test and validate new designs.

University of Notre Dame first day on SAE Baja competition

The Society of Automotive Engineers (SAE) as part of their Collegiate Design Series (CDS) has a Baja competition, where students design, build and race a small all terrain vehicle. Students from the Department of Aerospace and Mechanical Engineering took on the challenge and during this past year they designed, built and tested a small Baja vehicle. This vehicle runs with a stock 10 hp engine, and a CVT transmission. All components, except the engine, were designed or selected by the students. After the vehicle was assembled, they tested for about 6 hours, where they tuned the suspension and CVT, recorded data, and trained drivers.

NDBaja_SAE

Yesterday, the team drove from South Bend, IN to Rochester, NY. The competition is from June 6 to June 10 and is hosted by Rochester Institute of Technology in Rochester, NY. The summarized schedule of the competition:

  •  Thursday – Registration and engine governor setting
  •  Friday – Technical inspection and engineering design evaluation
  •  Saturday – Dynamic events
  •  Sunday – Endurance race (4 hour race)

If you want to know more about the team you can visit their webpage (http://www3.nd.edu/~ndbaja/index.html) or Facebook page (https://www.facebook.com/NDBaja). You can also show the team your support through Twitter @NDBaja, which will be giving updates during the competition.

Update: the team was able to register and get their engine approved.

Lessons from Formula SAE experience.

About a year ago a question in the LinkedIN Formula SAE group was posted.   It was related to the one thing that you would go back and change with the experience that you have today.  It was a nice forum discussion and many ideas were presented.  So in this post I will be summarizing the ideas that were presented using Wordle, discuss my own view of the results and go over how the word cloud was constructed.

The actual question posted was:

“With everything you learned (or are learning) from Formula, if you could go back and change one thing, what would you change about your team? Could be: goals, designs, management, equipment, etc…” –Anthony Stielow

This question generated 23 comments, most of them at least 4 sentences long.  The answers in the form of a word cloud are below.  From each comment the important words were recorded, synonyms were merged and then the list of words was input into Wordle.net to generate a “word cloud”.

 

The most mentioned topic was project management, and most of the remaining topics were related to this topic.  The comments went over how project management or time management was something that their teams needed to improve.  The team could have good ideas but it was difficult to complete them in the chaos of designing, building and testing a vehicle.  This is in agreement with Claude Rouelle’s (FSAE Design Judge) advice to build a good team first (project management) and then build a good car (video).

These lessons mentioned in the post are not engineering related, however Formula SAE is an engineering design competition.  From my own experience in the competition I can tell that engineering also has a big role in the competition.  There is no way yet to bypass the thermodynamics laws, drag coefficients and tire slip angles.  The complexity of the project is what makes project management equally important.

Now let’s briefly talk about how to get better at project management.  Organization has to be at the top of the list.  Teams can first set goals for the vehicle, weight less than XX, so many HP, just to give two examples.  Then they need to set a plan to get at those goals.  The more specific the plan the better; it should have deadlines, big tasks divided into small tasks and people in charge of those tasks.

FSAE is an experience and no plan will be able to manage all situations.  The time will come when problems will arrive and you have to set a new or modified plan, goals, and tasks.  That is ok.  The important issue is to not wait until you can not fix mistakes or problems properly.

FSAE is also about people solving problems.  One tool for this purpose is brainstorming.  Know the brainstorming rules (link).   Another tool to solve engineering problems is the TRIZ methodology.

A lot can be written to help FSAE students however the single piece of advice that helped me when I was a student was:

“Success is simple. Do what’s right, the right way, at the right time!” -Arnold H. Glasgow

The right solution is dependent on time and method!

 

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.