Model a simple chassis Peer-graded Assignment Solution

3D Model Creation with Autodesk Fusion 360 Peer-graded Assignment Solution

Design is the first phase in the digital manufacturing process. In this course, through a series of lectures and hands-on lessons, we’ll examine a designer’s approach to the design and manufacturing process—from concept to 3D model. We’ll start by applying design thinking to understand user needs, and then we’ll explore design criteria as we dive deeper into Autodesk® Fusion 360™ sketching, modeling, rendering, and documentation features.

Model a simple chassis Peer-graded Assignment Solution

Model a simple chassis using whatever geometry you deem necessary. This can be a simple flat plate setup like off the shelf FPV drones, or a custom design. Include your flight time estimate and final thrust ratio along with your material choice and weight estimate.

Review criteria

1. Yes/No: Was the design completed?

2. Yes/No: Did the design contain all parts needed to fly?

3. Yes/No: Did the design meet the flight time minimum?

4. Yes/No: Did the design meet the mass criteria?

Options: Design complexity: does the design show any complex features other than a basic sketch and extrude? No (0 points), some (1 point), very complex (2 points).

Ungraded text box, Reviewers leave feedback on the design. Provide feedback on the pros and cons of the design both mechanically and aesthetically.

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
## 
## Call:
## lm(formula = mpg ~ wt + qsec + am, data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4811 -1.5555 -0.7257  1.4110  4.6610 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   6.6819     8.0101   0.834 0.411235    
## wt           -3.9165     0.7112  -5.507 6.95e-06 ***
## qsec          1.2259     0.2887   4.247 0.000216 ***
## am            2.9358     1.4109   2.081 0.046716 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.459 on 28 degrees of freedom
## Multiple R-squared:  0.8497, Adjusted R-squared:  0.8336 
## F-statistic: 52.75 on 3 and 28 DF,  p-value: 1.21e-11
Conclusion:

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This course is intended for audiences of all experiences who are interested in learning about new skills in a business context; there are no prerequisite courses.

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