哈呋结构

1. 哈呋结构

如楼上所答复的, 哈夫结构就是指的剖分结构,通常是分为两半.
哈夫结构脱模时先打开两半个剖分模腔,这个时候注塑件因为收缩一般包在凸模上, 接下来顶板(杆)将塑料件顶出完成脱模动作.


至于如何区分模具是几板结构的,主要看主流道(浇注系统)有无分型面. 也就是有两个分型面. 则是三板结构; 简单点的,只有一个分型面,则是两板结构.

如果您是新手还没有明白,你就看浇注完成后,主流道等浇道系统是人工由塑料件上切除, 多半是两板结构; 如果浇道系统在模具打开脱模后, 自动掉下来,通常没有和注塑件连接在一起,那就是三板结构.

2. 哈夫（霍夫）变换matlab程序

clc
clear
close  
%BW=imread('D:\picture\9dafa605d53eea243812bb29.jpg');  
rgb=imread('lena.jpg');
BW=rgb2gray(rgb);  
thresh=[0.01,0.17];  
sigma=2;%定义高斯参数  
f = edge(double(BW),'canny',thresh,sigma);  
figure(1),
imshow(f,[]);
title('canny 边缘检测');  
[H, theta, rho]= hough(f,'RhoResolution', 0.5);  
axis on,  
axis normal  
xlabel('\theta'),ylabel('rho');  
p=houghpeaks(H,5); 
hold on  
lines=houghlines(f,theta,rho,p);
figure,
imshow(f,[]),
title('Hough Transform Detect Result'),  
hold on  
for k=1:length(lines)   
    xy=[lines(k).point1;lines(k).point2];  
plot(xy(:,1),xy(:,2),'LineWidth',4,'Color',[.6 .6 .6]);  
end

3. 如何利用MATLAB实现哈夫变换检测圆

clc,clear all 
I = imread('yuan.tif'); 
[m,n,l] = size(I); 
if l>1 
    I = rgb2gray(I); 
end 
BW = edge(I,'sobel'); 
step_r = 1; 
step_angle = 0.1; 
r_min = 20; 
r_max = 30; 
thresh = 0.7; 
% %%%%%%%%%%%%%%%%%%%%%%%%%% 
% input 
% BW:二值图像； 
% step_r:检测的圆半径步长 
% step_angle:角度步长，单位为弧度 
% r_min:最小圆半径 
% r_max:最大圆半径 
% p:阈值，0，1之间的数 
 
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
% output 
% hough_space:参数空间，h(a,b,r)表示圆心在(a,b)半径为r的圆上的点数 
% hough_circl:二值图像，检测到的圆 
% para:检测到的圆的圆心、半径 
[m,n] = size(BW); 
size_r = round((r_max-r_min)/step_r)+1; 
size_angle = round(2*pi/step_angle); 
 
hough_space = zeros(m,n,size_r); 
 
[rows,cols] = find(BW); 
ecount = size(rows); 
 
% Hough变换 
% 将图像空间(x,y)对应到参数空间(a,b,r) 
% a = x-r*cos(angle) 
% b = y-r*sin(angle) 
for i=1:ecount 
    for r=1:size_r 
        for k=1:size_angle 
            a = round(rows(i)-(r_min+(r-1)*step_r)*cos(k*step_angle)); 
            b = round(cols(i)-(r_min+(r-1)*step_r)*sin(k*step_angle)); 
            if(a>0&a0&b<=n) 
                hough_space(a,b,r) = hough_space(a,b,r)+1; 
            end 
        end 
    end 
end 
 
% 搜索超过阈值的聚集点 
max_para = max(max(max(hough_space))); 
index = find(hough_space>=max_para*thresh ); 
length = size(index); 
hough_circle = false(m,n); 
for i=1:ecount 
    for k=1:length 
        par3 = floor(index(k)/(m*n))+1; 
        par2 = floor((index(k)-(par3-1)*(m*n))/m)+1; 
        par1 = index(k)-(par3-1)*(m*n)-(par2-1)*m; 
        if((rows(i)-par1)^2+(cols(i)-par2)^2<(r_min+(par3-1)*step_r)^2+5&... 
                (rows(i)-par1)^2+(cols(i)-par2)^2>(r_min+(par3-1)*step_r)^2-5) 
            hough_circle(rows(i),cols(i)) = true; 
        end 
    end 
end 
 
% 打印检测结果 
for k=1:length 
    par3 = floor(index(k)/(m*n))+1; 
    par2 = floor((index(k)-(par3-1)*(m*n))/m)+1; 
    par1 = index(k)-(par3-1)*(m*n)-(par2-1)*m; 
    par3 = r_min+(par3-1)*step_r; 
    fprintf(1,'Center %d %d radius %d\n',par1,par2,par3); 
    para(:,k) = [par1,par2,par3]; 
end 
subplot(221),imshow(I),title('原图') 
subplot(222),imshow(BW),title('边缘') 
subplot(223),imshow(hough_circle),title('检测结果')