Machine vision is the eye of the industrial automation. Using cameras and sensors and computing power, machine vision techniques attempt to understand images and enable machines (robots or other industrial tools) to complete industrial tasks such as manufacturing and quality verification.
The working principle of machine vision consists of three different steps :
Machine vision helps industrial automation systems in numerous ways such as increasing efficiency by improving inventory and detecting faulty products and improving manufacturing quality.
How does Machine Vision work?
There are three main steps to learn how machine vision works.
Vision sensors, digital cameras, ultraviolet or infrared cameras are used to capture the image. This is a snapshot through one or more instances. The hardware captures the image and transforms it into digital information.
The digital data coming from the hardware can be analyzed by using image processing algorithms. There are three main steps in image processing in machine vision:
- Pre-processing: Pre-processing consists of noise removal and contrast enhancing.
- Image recognition:
- Segmentation: A threshold is applied and the edges of the image is determined in this process.
- Feature Extraction: Size, color, length, shape or combination of these features can be extracted in this process.
Based on the information extracted in the previous step, machine is instructed to do the necessary action.
What are the advantages of machine vision?
These systems allow manufacturers to work faster and more flawlessly in production process, increasing demand for machine vision systems. Machine vision enables a higher rate of automation: Improved machine vision systems enable machines to take a higher share of the industrial work. As work is automated (e.g. production control work), employees can be directed to more productive areas. Some examples of automation include:
- Fast and high-quality production control: Defective parts can be identified quickly thanks to the rapid processing capability of the machine vision. At the same time, the possibility of error is reduced by eliminating human error.
- Inventory control: Thanks to machine vision systems such as barcode scanners, products can be quickly and individually controlled during storage and distribution.
- Predictive maintenance: Visual data can be used to trigger predictive maintenance systems. However, these systems rely more on sensors such as heat and vibration detectors.
What are machine vision use cases/applications areas?
- Across industries: As mentioned above, production quality control and predictive maintenance are some common application areas in all industry.
- Retail: Barcode scanners are important part of inventory and store management in retail industry and it is one of the main applications of machine vision. Automatic checkout systems and customer service applications are the other examples of machine vision used in retail industry.
- Healthcare: Ultrasound scanning, surgical navigation and skin cancer detection are some of the examples of machine vision in healthcare industry.
- Logistics: Automated Data Capture (ADC) or Automated Inspection (AI) are important processes in logistics for verification and control of the product. These terms refers to automatically identifying objects, collecting data about them, and entering them directly into computer systems. QR codes, barcodes and RFIDs are some of the examples of ADC.
- Automotive: Dimensional gauging is a method in production process in automotive and machine vision helps by calculating the distances between points or geometric positions on an object and determines whether these measurements meet specifications. Machine vision allows robotic guidance which is needed to place parts onto a vehicle during the body-in-white stage of the assembly process. Presence-absence checking is also assisted by machine vision solutions in automotive plants.