With the advent of Industry 4.0, machine vision is playing an increasingly important role in the field of intelligent manufacturing. This article explores how machine vision technology works and why it is the right choice for achieving process automation and quality improvement.
Machine vision technology is an interdisciplinary field involving artificial intelligence, neurobiology, psychophysics, computer science, image processing, pattern recognition, and many other areas. Machine vision primarily uses computers to simulate human visual functions, extracting information from images of objective objects, processing and understanding it, and ultimately using it for practical detection, measurement, and control. The biggest characteristics of machine vision technology are its high speed, large information capacity, and multiple functions.
What is machine vision?
Definition of machine vision: Machine vision is the use of optical non-contact sensing devices to automatically receive and interpret images of real-world scenes to obtain information for controlling machines or processes.
Machine vision is the automatic extraction of information from digital images for process control or inspection of manufactured products. See the example below:
▲Illustrated explanation of machine vision applications
To better understand machine vision, we will use a fill level detection system used in a brewery as an example:
▲Figure 1 Example of beer bottle filling level inspection
As each beer bottle moves past the detection sensor, the sensor triggers the vision system to emit a strobe light and capture an image of the bottle. After acquiring the image and saving it to memory, the vision software processes or analyzes the image and issues a pass/fail response based on the actual fill level of the bottle. If the vision system detects that a beer bottle is not fully filled (i.e., it fails the detection), it will signal a deflector to remove the bottle from the production line. The operator can view the rejected bottles and ongoing process statistics on the display screen.
In addition, machine vision systems can also measure objects, such as determining spark plug gaps or providing positional information to guide robots to align components during manufacturing and assembly. The example shown in Figure 2 mainly illustrates how machine vision systems can be used to detect whether an oil filter (right) passes or fails, and to measure the width of the central shaft head on the bracket (left).
▲Figure 2 shows that the machine vision system can perform real-time measurement and inspection on the production line, such as processing brackets (left) or oil filters (right).
In this application example, the fill level inspection system can only provide two results, which demonstrates the characteristics of a binary system:
1. If the product is qualified, the test result is "pass".
2. If the product is substandard, the test result will be "failed".
What are the advantages of machine vision?
While human vision excels at qualitatively interpreting complex, unstructured scenes, machine vision, with its advantages in speed, accuracy, and repeatability, is adept at quantitatively measuring structured scenes. For example, on a production line, machine vision systems can inspect hundreds or even thousands of components per minute. Equipped with cameras and optics of appropriate resolution, machine vision systems can easily examine details of objects too small for the human eye to see.
Furthermore, by eliminating direct contact between the inspection system and the inspected components, machine vision can prevent component damage and avoid the maintenance time and costs associated with mechanical component wear. By reducing human intervention in the manufacturing process, machine vision also offers additional safety and operational advantages. In addition, machine vision can prevent cleanroom contamination by human intervention and protect workers from hazardous environments.
Classification of machine vision systems
• Smart camera
•Based on embedded systems
• PC-based
Composition of machine vision system
• Image acquisition: light source, lens, camera, acquisition card, mechanical platform
• Image processing and analysis: Industrial control host, image processing and analysis software, graphical user interface.
• Judgment execution: Telephone unit, Mechanical unit
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