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Commit ac17a341 authored by Jayant Khatkar's avatar Jayant Khatkar
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cleanup and organise code and add better temperature handling

parent d89cf8dd
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main.jl
View file @ ac17a341
using DataFrames
using CSV
using JSON
using LightGraphs
using NearestNeighbors
using Statistics
using BenchmarkTools
using Plots
using LinearAlgebra
include("utils.jl")
struct material
α::Number
E::Number
σ̄::AbstractArray{Number, 2} # Yield stress \sigma\bar
end
mutable struct contour
pos
time
end
struct contourdata
contours::Vector{contour}
G::SimpleDiGraph
layers::Vector
travel_dists::Dict
layer_height::Number
end
struct voxmap
seglen::Vector{Float64}
segcontours::Vector{Int}
c::Number
end
struct voxdata
voxels::DataFrame
maps::Vector{voxmap}
below::Vector{Int}
width::Number
end
function vecvec_to_matrix(vecvec)
# convert vector of vectors int a matrix
dim1 = length(vecvec)
dim2 = length(vecvec[1])
my_array = zeros(Float32, dim1, dim2)
for i in 1:dim1
for j in 1:dim2
my_array[i,j] = vecvec[i][j]
end
end
return my_array
end
function contour(d::Dict)
return contour(vecvec_to_matrix(d["pos"]), d["time"])
end
function contourdata(cons::Vector{contour}, max_layers::Int, min_dist::Number)
G = LightGraphs.SimpleDiGraph(0)
# separate contours into layers
layer_heights = sort(collect(Set([c.pos[end,3] for c in cons])))
layers = [[] for i in 1:length(layer_heights)]
clayeri = []
contour_trees = []
# place contours in layers and construct KDTree for each contour
for i in 1:length(cons)
l = searchsorted(layer_heights, cons[i].pos[1,3])[1]
push!(layers[l], i)
push!(clayeri, l)
add_vertex!(G)
push!(contour_trees, KDTree(transpose(cons[i].pos)))
end
# loop through contours from previous layer and compare waypoints
for i in 1:length(cons)
l = clayeri[i]
# add contours from max_layers below
if l > max_layers
for c in layers[l-max_layers]
add_edge!(G, c, i)
end
end
if l == 1 || max_layers == 1
continue
end
for c in layers[l-1]
# if any points in contour i within min_dist of any points in contour c
if any([length(b) > 0 for b in inrange(contour_trees[c], transpose(cons[i].pos), min_dist)])
add_edge!(G, c, i) # mark i dependent on c
end
end
end
return contourdata(cons, G, layers, Dict(), layer_heights[1])
end
function seg_helper_orientation(p,q,r)
val = (q[2]-p[2]) * (r[1]-q[1]) - (q[1]-p[1]) * (r[2]-q[2])
if val > 0
return 1 # clockwise
elseif val < 0
return 2 # anticlockwise
else
return 0 # colinear
end
end
function onseg(p,q,r)
# check if q lies on segment pr assuming 3 points are colinear
return ((q[1] <= max(p[1], r[1])) && (q[1] >= min(p[1], r[1])) &&
(q[2] <= max(p[2], r[2])) && (q[2] >= min(p[2], r[2])))
end
function seg_intersect(p1,q1,p2,q2)
o1 = seg_helper_orientation(p1, q1, p2)
o2 = seg_helper_orientation(p1, q1, q2)
o3 = seg_helper_orientation(p2, q2, p1)
o4 = seg_helper_orientation(p2, q2, q1)
if (o1 ≠ o2) && (o3 ≠ o4) ||
o1==0 && onseg(p1, p2, q1) ||
o2==0 && onseg(p1, q2, q1) ||
o3==0 && onseg(p2, p1, q2) ||
o4==0 && onseg(p2, q1, q2)
return true
end
return false
end
dist(p1, p2) = √sum((p1 -p2).^2)
interpolate(p1, p2, xi, axis) = p1 + (p2-p1)*(xi-p1[axis])/(p2[axis]-p1[axis])
interpolate(p1, p2, x1, xi, x2) = p1 + (p2-p1)*(xi-x1)/(x2-x1)
function voxmap(vox::Vector{Float64}, vox_d::Number, cdata::contourdata)
# for one vox, get all contours which pass through it
# only need to search contours in its layer
l = Int(round((vox[3] + cdata.layer_height/2)/cdata.layer_height))
voxx1 = vox[1] + vox_d/2
voxx2 = vox[1] - vox_d/2
voxy1 = vox[2] + vox_d/2
voxy2 = vox[2] - vox_d/2
seg_now = false
seglen = Vector{Number}()
segoffset = Vector{Number}()
segcontours = Vector{Int}()
seglen_sofar = 0
t_start = 0
if l > length(cdata.layers)
return voxmap(seglen, segcontours, 0)
end
for cid in cdata.layers[l]
c = cdata.contours[cid]
# check if contour passes thorough this vox
for i in 2:size(c.pos)[1]
# make sure it is a line segment, not a point
if c.pos[i-1,1:2] == c.pos[i,1:2]
continue
end
# is this line segment completely outside vox?
if c.pos[i, 1] > voxx1 && c.pos[i-1, 1] > voxx1 ||
c.pos[i,1] < voxx2 && c.pos[i-1, 1] < voxx2 ||
c.pos[i,2] < voxy2 && c.pos[i-1, 2] < voxy2 ||
c.pos[i,2] > voxy1 && c.pos[i-1, 2] > voxy1
# segment outside vox entirely
if seg_now
println("Something's gone wrong: segment entirely outside voxel, but last segment inside")
end
continue
end
p1inside = c.pos[i-1, 1] < voxx1 && c.pos[i-1, 1] > voxx2 && c.pos[i-1, 2] > voxy2 && c.pos[i-1, 2] < voxy1
p2inside = c.pos[i, 1] < voxx1 && c.pos[i,1] > voxx2 && c.pos[i,2] > voxy2 && c.pos[i,2] < voxy1
# is this line segment completely inside vox?
if p1inside && p2inside
seglen_sofar += dist(c.pos[i], c.pos[i-1]) # append to existing contour
if !seg_now # start new seg
t_start = 0 # 0 bc contour must be starting for this case
seg_now = true
if i!=2
println("Whole segment inside but something wrong")
end
continue
end
end
cross_side1 = seg_intersect(c.pos[i-1,:], c.pos[i,:], [voxx1, voxy1], [voxx1, voxy2])
cross_side2 = seg_intersect(c.pos[i-1,:], c.pos[i,:], [voxx1, voxy1], [voxx2, voxy1])
cross_side3 = seg_intersect(c.pos[i-1,:], c.pos[i,:], [voxx2, voxy1], [voxx2, voxy2])
cross_side4 = seg_intersect(c.pos[i-1,:], c.pos[i,:], [voxx2, voxy2], [voxx1, voxy2])
# does this line segment intersect with vox only once
if p1inside ⊻ p2inside
# find intersection point
if cross_side1 || cross_side3
# intersection with x
xi = [voxx1, voxx2][[cross_side1, cross_side3]][1]
p_i = interpolate(c.pos[i-1,:], c.pos[i,:], xi, 1)
t_i = interpolate(c.time[i-1], c.time[i], c.pos[i-1,1], xi, c.pos[i,1])
elseif cross_side2 || cross_side4
# intersection with y
yi = [voxy1, voxy2][[cross_side2, cross_side4]][1]
p_i = interpolate(c.pos[i-1,:], c.pos[i,:], yi, 2)
t_i = interpolate(c.time[i-1], c.time[i], c.pos[i-1,2], yi, c.pos[i,2])
end
if p1inside
# end existing segment
if !seg_now
# contour end on the first segment
t_start = 0
seglen_sofar = 0
end
seglen_sofar += dist(c.pos[i-1, :], p_i)
push!(segcontours, cid)
push!(seglen, seglen_sofar)
push!(segoffset, (t_i + t_start)/2)
seglen_sofar = 0
seg_now = false
else
# start new contour
t_start = t_i
seglen_sofar = dist(p_i, c.pos[i, :])
seg_now = true
end
continue
elseif sum([cross_side1, cross_side2, cross_side3, cross_side4]) >= 2
# intersects twice
p_is = []
t_is = []
if cross_side1
p = interpolate(c.pos[i-1,:], c.pos[i,:], voxx1, 1)
if !isnan(p[1])
push!(p_is, p)
push!(t_is, interpolate(c.time[i-1], c.time[i], c.pos[i-1,1], voxx1, c.pos[i,1]))
end
end
if cross_side2
p = interpolate(c.pos[i-1,:], c.pos[i,:], voxy1, 2)
if !isnan(p[1])
push!(p_is,p)
push!(t_is, interpolate(c.time[i-1], c.time[i], c.pos[i-1,2], voxy1, c.pos[i,2]))
end
end
if cross_side3
p = interpolate(c.pos[i-1,:], c.pos[i,:], voxx2, 1)
if !isnan(p[1])
push!(p_is,p)
push!(t_is, interpolate(c.time[i-1], c.time[i], c.pos[i-1,1], voxx2, c.pos[i,1]))
end
end
if cross_side4
p = interpolate(c.pos[i-1,:], c.pos[i,:], voxy2, 2)
if !isnan(p[1])
push!(p_is, p)
push!(t_is, interpolate(c.time[i-1], c.time[i], c.pos[i-1,2], voxy2, c.pos[i,2]))
end
end
if seg_now
print("Something's wrong")
end
if length(p_is) >= 2
push!(segoffset, mean(t_is))
push!(segcontours, cid)
if length(p_is) == 2
push!(seglen, dist(p_is[1], p_is[2]))
else
push!(seglen, dist(p_is[1], p_is[3]))
end
else
p1inside = c.pos[i-1, 1] <= voxx1 && c.pos[i-1, 1] >= voxx2 &&
c.pos[i-1, 2] >= voxy2 && c.pos[i-1, 2] <= voxy1
p = p1inside ? c.pos[i-1,:] : c.pos[i,:]
t = p1inside ? c.time[i-1] : c.time[i]
push!(segcontours, cid)
push!(segoffset, (t + t_is[1])/2)
push!(seglen, dist(p_is[1], p))
end
end
end
# if contour ends inside the voxel
if seg_now
# end segment
push!(segcontours, cid)
push!(seglen, seglen_sofar)
push!(segoffset, (t_start + last(c.time))/2)
seglen_sofar = 0
t_start = 0
seg_now = false
end
# for those contours find exact segments
end
c = Float64(segoffset ⋅ seglen)/sum(seglen) # constant used for cost calc
new_seglen = Vector{Float64}()
new_segcontours = Vector{Int64}()
for i in 1:length(segcontours)
if !(segcontours[i] in new_segcontours)
push!(new_segcontours, segcontours[i])
push!(new_seglen, sum(seglen[segcontours.==segcontours[i]]))
end
end
return voxmap(new_seglen./sum(new_seglen), new_segcontours, c)
end
function voxdata(fname::String, cdata::contourdata)
voxels = DataFrames.DataFrame(CSV.File(fname))
w = dist(Vector(voxels[1, ["x","y","z"]]), Vector(voxels[2, ["x","y","z"]]))
println("Assumed width ", w)
vpos = [[v.x, v.y, v.z] for v in eachrow(voxels)]
voxms = [voxmap(v, w, cdata) for v in vpos]
below = indexin([v - [0,0,cdata.layer_height] for v in vpos], vpos)
replace!(below, nothing=>0)
return voxdata(voxels, voxms, below, w)
end
function random_rollout(cdata::contourdata)
done_contours = Set{Int}()
avail_contours = Set(cdata.layers[1])
todo_contours = Set(1:length(cdata.contours))
rollout = Vector{Int}()
while length(avail_contours) > 0
c = rand(avail_contours)
push!(rollout, c)
# remove selected contour from todo and avail, add to done
delete!(avail_contours, c)
delete!(todo_contours, c)
push!(done_contours, c)
# update available contours
for i in todo_contours
if i in avail_contours
continue
elseif length(inneighbors(cdata.G, i)) == 0
push!(avail_contours, i)
continue
end
add = true
for j in inneighbors(cdata.G, i)
if !(j in done_contours)
add = false
break
end
end
if add
push!(avail_contours, i)
end
end
end
return rollout
end
function valid_swap(rollout::Vector{Int}, i::Int, j::Int, cdata::contourdata)
# would swapping indices i and j in rollout result in another valid rollout?
# NOTE THIS FUNCTION DOESNT WORK
# IT ONLY CHECKS DEPENDENCIES TO A DEPTH OF 1
# TODO, leave for now, use check_validity to double check at the end
if i>j
i,j = j,i
elseif i==j
return true
end
c1 = rollout[i]
c2 = rollout[j]
c2_dependson = inneighbors(cdata.G, c2)
if c1 in c2_dependson
return false
end
c1_dependents = outneighbors(cdata.G, c1)
c_between = rollout[i+1:j-1]
for c in c_between
if c in c1_dependents || c in c2_dependson
return false
end
end
return true
end
function check_validity(rollout::Vector{Int}, cdata::contourdata)
# make sure a given rollout is valid
done_contours = Set{Int}()
for c in rollout
c_dependson = inneighbors(cdata.G, c)
if !issubset(c_dependson, done_contours)
return false
end
push!(done_contours, c)
end
return true
end
function swap!(rollout::Vector{Int}, i::Int, j::Int)
# swap values at ith and jth indices
rollout[i], rollout[j] = rollout[j], rollout[i]
end
function test_voxmap()
# create vox
vox = [0,0,0.5]
vox_d = 2
pos1 = [[0.5, -0.5] ones(2)*0.5 ones(2)]
time1 = [0,1]
pos2 = [[1.5, 0.5, -0.5, -1.5] ones(4)*0.5 ones(4)]
time2 = Vector(0:3)
pos3 = [[-0.5, -0.5] [2, -2] ones(2)]
time3 = [0, 2.5]
pos4 = [[0.5, 2.5] [-1.5, -0.5] ones(2)]
time4 = [0,1]
pos5 = [[-2,2] [-2,2] ones(2)]
time5 = [0,1]
contour1 = contour(pos1, time1)
contour2 = contour(pos2,time2)
contour3 = contour(pos3,time3)
contour4 = contour(pos4,time4)
contour5 = contour(pos5,time5)
contours = [contour1, contour2, contour3, contour4, contour5]
cdata = contourdata(contours, 1, 1)
vm = voxmap(vox, vox_d, cdata)
return vm
end
# Temperature function
T0 = 215 # extrusion temp
Tc = 25 # room temp
Tcutoff = 100 # temperature above which strain isn't happening
k_temp = 0.01 #value unknown
Temp(t::Number) = Tc + (T0-Tc)*ℯ^(-k_temp*t)
# see shape of temp function
x = 1:210
y = Temp.(x)
plot(x,y)
function clean_contour(c::contour)
# remove first element of array if second element is the same
while c.pos[1,:] == c.pos[2,:]
c.pos = c.pos[2:end, :]
end
return c
end
function stress_multiplier!(a::DataFrame, mul::Number)
a.Sx = a.Sx*mul
a.Sy = a.Sy*mul
a.Sz = a.Sz*mul
a.Txy = a.Txy*mul
a.Tyz = a.Tyz*mul
a.Txz = a.Txz*mul
return
end
# ABS material properties
### ABS material properties
α_ABS = 63e-6
E_ABS = 2.5e9 # 1.19e9 - 2.9e9
σ̄ = 6e7 # mega
Tcutoff = 100 # temperature above which strain isn't happening
σ̄_ABS = [
[σ̄ σ̄*0.5 σ̄*0.375]
[σ̄*0.5 σ̄ σ̄*0.375]
[σ̄*0.375 σ̄*0.375 σ̄*0.75 ]
]
ABS = material(α_ABS, E_ABS, σ̄_ABS)
ABS = material(α_ABS, E_ABS, σ̄_ABS, Tcutoff)
td = tempdecay(215, 25, 0.04) # extrusion temp, room temp, decay rate
tdslow = tempdecay(215, 25, 0.01)
tdfast = tempdecay(215, 25, 0.08)
# visualise_tempdecay(tdfast)
### LOAD IN DATA
......@@ -528,81 +22,11 @@ obj = "/Users/jayant/phd/tempaware/" * "M1"
contours = clean_contour.(contour.(JSON.parse(open(obj * "contours.json"))))
cdata = contourdata(contours, 5, 5) # contour data
@time vd = voxdata(obj * "_voxels.csv", cdata)
stress_multiplier!(vd.voxels, 10)
rl = random_rollout(cdata)
#####################################
###### CONSTRUCT COST FUNCTION
mat = ABS
# store contour time lengths inside function
contour_times = [cdata.contours[c].time[end] for c in 1:length(cdata.contours)]
# considered voxels
not_empty_voxels = length.([m.seglen for m in vd.maps]) .>0
valid_voxels = (1:length(vd.below))[(vd.below.!=0) .& not_empty_voxels]
valid_voxels = valid_voxels[not_empty_voxels[vd.below[valid_voxels]]]
#stress_size = .√( vd.voxels.Sx.^2 + vd.voxels.Sy.^2 + vd.voxels.Sz.^2)
#top_voxes = Set(sortperm(stress_size)[1:Int(length(stress_size)/4)]) # top stressors only
#top_valid_voxels = sort(collect(Set(valid_voxels) ∩ top_voxes))
#rel_voxels = vd.voxels[top_valid_voxels,:]
#relmaps = sort(collect(Set(vd.below[top_valid_voxels]) ∪ Set(top_valid_voxels)))
#considered_voxels = indexin(top_valid_voxels, relmaps)
# relbelows = indexin(vd.below[top_valid_voxels], relmaps)
considered_voxels = valid_voxels
relbelows = vd.below[valid_voxels]
rel_voxels = vd.voxels[considered_voxels,:]
relmaps = vd.maps
# voxtimes vectorize
max_contours_per_voxel = maximum([length(r.seglen) for r in relmaps])
vox_contour_id = ones(Int64, length(relmaps), max_contours_per_voxel)
for i in 1:length(relmaps)
vox_contour_id[i, 1:length(relmaps[i].segcontours)] = relmaps[i].segcontours
end
vox_c = [Float64(v.c) for v in relmaps]
vox_seglen = zeros(length(relmaps), max_contours_per_voxel)
for i in 1:length(relmaps)
vox_seglen[i, 1:length(relmaps[i].seglen)] = relmaps[i].seglen
end
# calculate stresses at each voxel
F = (2/mat.σ̄[1,1]^2 - 1/mat.σ̄[3,3]^2)/2
G = 1/(2*mat.σ̄[3,3]^2)
L = 1/(2*mat.σ̄[1,2]^2)
M = 1/(2*mat.σ̄[1,3]^2)
a = quote
function cost_f(rl::Vector{Int})
# println("Cumulative Sum") # 800k
timestart = cumsum([$contour_times[c] for c in rl])
#println("Vox times") # 18k
voxtimes = sum(vox_seglen .* timestart[vox_contour_id], dims=2) .+ vox_c
# println("time diff") # 100k
Δt = voxtimes[considered_voxels] - voxtimes[relbelows]
#println("Temp diff") # 30k
ΔT = Tcutoff .- ($Tc .+ $(T0-Tc).*ℯ.^(-$k_temp.*Δt))
# println("Temp diff clean") # 500k
replace!(x-> x<0 ? 0 : x, ΔT)
# println("Stresses") # 6k
σ11 = rel_voxels.Sx + $(mat.E*mat.α)*ΔT
σ22 = rel_voxels.Sy + $(mat.E*mat.α)*ΔT
σ33 = rel_voxels.Sz
σ12 = rel_voxels.Txy
σ23 = rel_voxels.Tyz + $((cdata.layer_height/vd.width)*mat.E*mat.α)*ΔT
σ31 = rel_voxels.Txy + $((cdata.layer_height/vd.width)*mat.E*mat.α)*ΔT
return sum($F * (σ11 - σ22).^2 +
$G * ((σ33 - σ11).^2 + (σ33 - σ22).^2) +
$(2 * L) * (σ12).^2 +
$(2 * M) * (σ23 + σ31).^2)
end
end