computer aided geometric design (talk at jai hind college, mumbai, 15 th december, 2004)
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Computer Aided Geometric Design (talk at Jai Hind College, Mumbai, 15 th December, 2004). Milind Sohoni Department of Computer Science and Engg. IIT Powai Email :[email protected] Sources : www.cse.iitb.ac.in/sohoni - PowerPoint PPT PresentationTRANSCRIPT
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Computer Aided Geometric Design(talk at Jai Hind College, Mumbai, 15th December, 2004)
Milind Sohoni Department of Computer Science and Engg.
IIT Powai
Email:[email protected]
Sources: www.cse.iitb.ac.in/sohoni
www.cse.iitb.ac.in/sohoni/gsslcourse
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• Ahmedabad-Visual Design Office
• Kolhapur-Mechanical Design Office
• Saki Naka – Die Manufacturer
• Lucknow- Soap manufacturer
A Solid Modeling Fable
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Ahmedabad-Visual Design
• Input: A dream soap tablet
• Output:• Sketches/Drawings• Weights• Packaging needs
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Soaps
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More Soaps
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Ahmedabad (Contd.)
Top View
Front View Side View
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Kolhapur-ME Design Office
• Called an expert CARPENTER
• Produce a model (check volume etc.)
• Sample the model
and produce a data-
set
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Kolhapur(contd.)
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Kolhapur (contd.)
• Connect these sample-points into a faceting
• Do mechanical analysis
• Send to Saki Naka
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Saki Naka-Die Manufacturer
• Take the input faceted solid.
• Produce Tool Paths • Produce Die
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Lucknow-Soaps
• Use the die to manufacture soaps
• Package and transport to points of sale
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Problems began…
• The die degraded in Lucknow
• The Carpenter died in Kolhapur
• Saki Naka upgraded its CNC machine
• The wooden model eroded
But
The Drawings were there!
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So Then….
• The same process was repeated but…
The shape was different!
The customer was suspicious and sales dropped!!!
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The Soap Alive !
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What was lacking was…A Reproducible Solid-
Model.• Surfaces defn• Tactile/point
sampling• Volume
computation• Analysis
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The Solid-Modeller
Modeller
OperationsRepresentations
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The mechanical solid-modeller
Operations• Volume
Unions/Intersections• Extrude holes/bosses• Ribs, fillets, blends
etc.
Representation• Surfaces-x,y,z as
functions in 2 parameters
• Edges –x,y,z as functions in 1 parameter
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Examples of Solid Models
Torus Lock
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Even more examples
Slanted Torus Bearing
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Other Modellers-Surface Modelling
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Chemical plants.
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Chemical Plants (contd.)
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Basic Solution: Represent each surface/edge by equations
e1
e2
e1: part of a line
X=1+t; Y=t, Z=1.2+t
t in [0,2.3]
e2: part of a circle
X=1.2 +0.8 cos t
Y=0.8+0.8 sin t
Z=1.2
T in [-2.3,2.3]
f1
f1: part of a planeX=3+2u-1.8vY=4-2uZ=7[u,v] in Box
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A Basic Problem
Construction of defining equations
• Given data points
arrive at a curve approximating
this point-set.• Obtain the
equation of this curve
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The Basic Process
• Choose a set of basis
functions
• Observe these at the data points
• Get the best linear combination
In our case, Polynomials 1, x, x^2, x^3
P(x)=a0+a1.x+a2.x^2+…
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The Observations Process
1 1 1
x 1.2 3.1
x^2 1.44 9.61
=B
v 6.1 2
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The Matrix SettingWe have• The basis observations Matrix B which is 5-by-100• The desired observations Matrix v which is 1-by-100We want:• a which is 1-by-5 so that aB is close to v
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The minimization
• Minimize least-square error (i.e. distance squared).(6.1-a0.1-a1.1.2-a2.1.44)^2+…+(2-1.a0 - 3.1a1 - 9.61 a2)^2 +…
Thus, this is a quadratic function in the variables
a0,a1,a2,…
And is easily minimized.
1 1 1
x 1.2 3.1
x^2 1.44 9.61
v 6.1 2
a0
a1
a2
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A Picture
Essentially, projection of v onto the space spanned by the basis vectors
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The calculation
• How does one minimize
1.1 a0^2 +3.7 a0 a1 +6.9 a1^2 ?
• Differentiate!
2.2 a0 + 3.7 a1 =0
3.7 a0 +13.8 a1=0
• Now Solve to get a0,a1
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We did this and….
• So we did this for surfaces (very similar) and here are the pictures…
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And the surface..
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Unsatisfactory….
• Observation: the defect is because of bad curvatures, which is really swings in double-derivatives!
• So, how do we rectify this?• We must ensure that if
p(x)=a0 +a1.x +a2.x^2 +… and
q(x)=p’’(x) then
q(x)>=0 for all x
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What does this mean?
q(x)=2.a2+6.a3.x+12.a4.x^2 +…
Thus q(1)>=0, q(2)>=0 means
2.a2 +6.a3 +12.a4 >=0
2.a2 +12.a3+48.a4 >=0
• Whence, we need to pose some linear inequalities on the variables a0,a1,a2,…
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So here is the picture…
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The smooth picture
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Another example
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The rough and the smooth
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In conclusion
• A brief introduction to CAGD• Curves and Surfaces as equations• Optimization-Least square• Quadratic Programing Linear Constraints Quadratic CostsSee www.cse.iitb.ac.in/sohoni/gsslcourse THANKS