Dimension Measurement
维度测量
Dimension inspection is a type of appearance inspection. It plays an important role in making pass/fail judgments of whether parts or products have been worked or assembled according to specifications. These dimension inspections can be automated with image processing:
- Measuring maximum/minimum dimensions of parts or products
- Measuring maximum/minimum/average inner or outer diameters or center coordinates of O rings
- Measuring roundness or angle of the tips of metal parts
- Measuring positions of edges and labels
- Measuring widths of sheet/film products
尺寸测量是检查的基础;但是，很难引入，因为它需要大量的时间和努力。随着工厂自动化（FA）的扩展，使用图像处理的尺寸测量增加了。本页介绍了使用图像处理的基本原理，优点和实际应用。
Basic principle of dimension measurement for judging whether products have been worked in accordance with specifications
Advantages of introducing image processing
通常，零件和产品的尺寸用微血管或卡钳测量或用检查夹具检查，以确保无需变化。欧宝官网开户由于工人和测量条件之间的个体差异，使用微血管或卡钳等仪器的测量不可避免地涉及误差。使用检查夹具的检查结果可用于通过/未通过判断产品是否在公差范围内。但是，它没有显示精确的测量值。
Other methods of dimension measurement include optical comparators, GD&T and profile measurement systems, and 3D measurement systems. They share the problems of requiring manual operation, and being both time consuming and expensive.
With image processing, various dimensions can be obtained from captured images. It is easy to measure the dimensions of various sections of parts and products based on this data and judge whether they are within tolerances. Another advantage is that you can measure angles or circle roundness simultaneously with the lengths of various sections, and save them as numerical data.
100% inspection for preventing oversight and outflow of defective workpieces
Dimension inspection using various measurement systems and jigs tends to require many offline processes. Consequently, 100% inspection incurs significant labor and costs. On the other hand, sample inspection has the possibility of inspection omission or outflow of defective workpieces. Dimension inspection using an image processing system can achieve inline dimension measurement of all targets. This ensures stable product quality while keeping costs down.
Faster inspection for improved productivity
Inline dimension measurement of parts and products allows significant reduction of inspection time. Image processing systems make inspection faster and contribute to productivity improvement.
Storage and management of quality information
基于JIG的尺寸检查不提供准确的测量数据。它只检查维度是否在公差范围内。使用图像处理的尺寸检查不仅提供了通过/失败判断结果，还提供了多个部分的准确尺寸的数值数据，并且可以轻松地保存和管理该数据。此类信息也可以有效地用于可追溯性管理或过程改进。
Basics of dimension measurement: Measurement based on edge detection
基于边缘检测的测量通常用于使用图像处理的尺寸检查。本节介绍像素分辨率，子像素处理的基础，以及边缘检测原理，以帮助理解边缘检测。
图像分辨率与判断容差之间的关系
The image pickup device of machine vision consists of an array of pixels. In dimension measurement, you can calculate the dimension tolerance based on this number of pixels and the field of view. An important factor for the calculation is pixel resolution, which is the actual length that corresponds to a single pixel of the image pickup device. The pixel resolution can be calculated with this formula:
像素分辨率= y方向上的视野[mm] /图像拾取器件的像素数在y方向上的[像素]
For example, assume you use a 0.31 megapixel camera and a 2 megapixel camera. The number of pixels in the Y direction is 480 for the 0.31 megapixel camera and 1200 for the 2 megapixel camera.
当视野为100 mm时：
- 【0.31 megapixel】
- 像素分辨率= 100 mm / 480像素= 0.208 mm /像素
- 【2百万像素】
- Pixel resolution = 100 mm / 1200 pixels = 0.083 mm/pixel
- 【21 megapixel】
- Pixel resolution = 100 mm / 4092 pixels = 0.024 mm/pixel
The pixel resolution can be calculated in this way. The table below shows reference pixel resolution values of cameras used in typical image processing systems, from 0.31 to 21 megapixels.
像素分辨率参考值
| Field of view (Y direction)［mm］ | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Number of pixels of the camera | 1 | 5 | 10 | 20 | 30 | 50 | 100 | 200 | 500. |
| 0.31百万像素 | 0.002 | 0.01 | 0.021 | 0.042 | 0.063 | 0.104 | 0.208 | 0.417 | 1.042 |
| 2 megapixel | 0.0008 | 0.004 | 0.008 | 0.017 | 0.025 | 0.042 | 0.083 | 0.167 | 0.417 |
| 500万像素 | 0.0005 | 0.002 | 0.005 | 0.01 | 0.015 | 0.024 | 0.049 | 0.098 | 0.244 |
| 21 megapixel | 0.0002 | 0.001 | 0.002 | 0.005 | 0.007 | 0.012 | 0.024 | 0.049 | 0.122 |
- *Number of pixels in the Y direction of the CCD of a 0.31 megapixel sensor = 480 pixels
- * 2万像素传感器的CCD的Y方向上的像素数= 1200像素
- *Number of pixels in the Y direction of the CCD of a 5 megapixel sensor = 2050 pixels
- *Number of pixels of the image pickup device in the Y direction of a 21 megapixel camera = 4092 pixels
在尺寸检查中，用作区分良好和缺陷工件的阈值的公差通常以±5像素为单位计算。这是基于假设，以确保稳定的公差判断的像素数约为重复性的10倍。由于典型机器视觉的可重复性在理想条件下约为0.1像素，因此实际的重复性被认为是0.5像素，其中包括一些边距。将此数字乘以10产生±5像素，并且该值可被视为公差设置的最小单位。您可以使用此值来计算此公式的实际维度公差：
Actual dimension tolerance [mm] = Pixel resolution (field of view in the Y direction) [mm] / Number of pixels of CCD in the Y direction x 5 pixels
- 【0.31 megapixel】
- 尺寸可容许= 0.208 mm /像素x 5像素= 1.04 mm
- 【2百万像素】
- Dimension tolerance = 0.083 mm/pixel x 5 pixels = 0.415 mm
- 【21 megapixel】
- 尺寸可容许= 0.024 mm /像素x 5像素= 0.12 mm
你需要选择机器视觉和竞争领域w that provides the required accuracy.
耐受判断价值
| Field of view (Y direction)［mm］ | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Number of pixels of the camera | 1 | 5 | 10 | 20 | 30 | 50 | 100 | 200 | 500. |
| 0.31百万像素 | 0.01 | 0.05 | 0.1 | 0.2 | 0.3 | 0.5 | 1.0 | 2.1 | 5.2 |
| 2 megapixel | 0.004 | 0.02 | 0.04 | 0.08 | 0.1 | 0.2 | 0.4 | 0.8 | 2.1 |
| 500万像素 | 0.002 | 0.01 | 0.02 | 0.05 | 0.07 | 0.1 | 0.2 | 0.5 | 1.2 |
| 21 megapixel | 0.001 | 0.006 | 0.01 | 0.02 | 0.04 | 0.06 | 0.1 | 0.2 | 0.6 |
Basics of sub-pixel processing
As explained above, the pixel resolution is the actual dimension value of one pixel. In reality, however, dimension measurement using image processing can show dimensions to units less than a pixel through approximate calculation. This is called sub-pixel processing.
A sub-pixel is a unit smaller than a pixel. For example, KEYENCE's image processing offers dimension data in units of 1/1000 pixel.
子像素处理原理
The sub-pixel processing detects an edge where the contrast changes between bright and dark, converts it into a projection waveform, and then performs differential processing. The peak of the differential waveform is determined as an edge point.
维度测量based on edge detection
边缘检测对于使用高像素分辨率（高精度）的尺寸测量尤其重要。本节介绍边缘检测的基本流程。
1. Projection
Scan the target perpendicularly to the detecting direction to obtain the average shade of the projection lines. The average shade waveform of the projection lines is called a projection waveform.
2. Differentiation
The differentiation of the projection waveform shows larger differential values for points that might be edges. Differentiation is a process for finding changes in shades (gradation levels).
3.校正以使最大差值100％
Apply correction so that the maximum differential value and absolute value become 100% to stabilize the edge. The peak of the differential waveform that exceeds the specified edge sensitivity is determined as an edge point.
4. Sub-pixel processing
Calculate the waveform of three pixels, one at the peak of the differential waveform and two adjacent pixels, and measure the edge position in units of 0.001 pixel.
Practical applications
As factory automation increases, saving time in inspection has become a challenge, and the same holds for dimension measurement. Measuring using image processing has become the main technique for this kind of process improvement.
Inspection of various dimensions of chip capacitors
除了电容器主体的直径和长度之外，其部件可以被分成段以执行边缘位置检测，并获得狭窄部分的最小直径或引线的长度或弯曲。可以精确测量每个部分的尺寸。
维度测量of bottle mouths
Dimensions can be measured accurately even when used inline. For example, you can capture an image of a PET bottle moving on a line from the side and measure the dimension of the bottle neck ring to detect defective molding, incorrect type, or assembly failure. This kind of Inline dimension inspection improves efficiency.
