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fix typo, add $slop support to robertson recess

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Adrian Mariano 2022-01-30 11:54:36 -05:00
parent 03e4491e72
commit 774b5c6b54
2 changed files with 16 additions and 7 deletions
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14
linalg.scad
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@ -416,14 +416,19 @@ function block_matrix(M) =
// Function: linear_solve()
// Usage:
// solv = linear_solve(A,b)
// solv = linear_solve(A,b,[pivot])
// Description:
// Solves the linear system Ax=b. If `A` is square and non-singular the unique solution is returned. If `A` is overdetermined
// the least squares solution is returned. If `A` is underdetermined, the minimal norm solution is returned.
// If `A` is rank deficient or singular then linear_solve returns `[]`. If `b` is a matrix that is compatible with `A`
// then the problem is solved for the matrix valued right hand side and a matrix is returned. Note that if you
// want to solve Ax=b1 and Ax=b2 that you need to form the matrix `transpose([b1,b2])` for the right hand side and then
// transpose the returned value.
// transpose the returned value. The solution is computed using QR factorization. If `pivot` is set to true (the default) then
// pivoting is used in the QR factorization, which is slower but expected to be more accurate.
// Usage:
// A = Matrix describing the linear system, which need not be square
// b = right hand side for linear system, which can be a matrix to solve several cases simultaneously. Must be consistent with A.
// pivot = if true use pivoting when computing the QR factorization. Default: true
function linear_solve(A,b,pivot=true) =
assert(is_matrix(A), "Input should be a matrix.")
let(
@ -448,11 +453,14 @@ function linear_solve(A,b,pivot=true) =
// Function: linear_solve3()
// Usage:
// x = linear_solve3(A,b)
// Desription:
// Description:
// Fast solution to a 3x3 linear system using Cramer's rule (which appears to be the fastest
// method in OpenSCAD). The input `A` must be a 3x3 matrix. Returns undef if `A` is singular.
// The input `b` must be a 3-vector. Note that Cramer's rule is not a stable algorithm, so for
// the highest accuracy on ill-conditioned problems you may want to use the general solver, which is about ten times slower.
// Arguments:
// A = 3x3 matrix for linear system
// b = length 3 vector, right hand side of linear system
function linear_solve3(A,b) =
// Arg sanity checking adds 7% overhead
assert(b*0==[0,0,0], "Input b must be a 3-vector")

7
screw_drive.scad
View file

@ -353,12 +353,13 @@ module robertson_mask(size, extra=1) {
F = (Fmin + Fmax) / 2 * INCH;
ang = 4;
h = T + extra;
Mslop=M+2*$slop;
down(T) {
intersection(){
Mtop = M + 2*adj_ang_to_opp(F+extra,ang);
Mbot = M - 2*adj_ang_to_opp(T-F,ang);
Mtop = Mslop + 2*adj_ang_to_opp(F+extra,ang);
Mbot = Mslop - 2*adj_ang_to_opp(T-F,ang);
prismoid([Mbot,Mbot],[Mtop,Mtop],h=h,anchor=BOT);
cyl(d1=0, d2=M/(T-F)*sqrt(2)*h, h=h, anchor=BOT);
cyl(d1=0, d2=Mslop/(T-F)*sqrt(2)*h, h=h, anchor=BOT);
}
}
}