Machine vision or industrial automation’s eyes attempt to understand images and enable machines to complete industrial tasks using sensors, cameras, and computing power. There are three steps included in the working principle of machine vision- capture, process, and action. The critical element of industry 4.0 will be machine vision. It helps industrial automation in systems increase efficiency and improve manufacturing quality. Machine vision can help in detecting faulty products and improving inventory. Businesses use digital input to determine action and become upgrade operations.
Machine vision technology has proven beneficial for those businesses that relied on consistent quality control by a human inspector. Highly sophisticated technologies go into creating a machine vision system. Every business leader understands the benefit it is bringing to the industrial sectors. Machine vision surpasses human sensory capabilities in all sorts of ways, from refereeing decisions in competitive sports to quality assurance in factory manufacture. Technically, machine vision brings together image processing, high-resolution cameras, and big data. It is currently used in manufacturing and other industries that rely on gauging, guidance, and identification processes. In this article, we look at how machine vision can transform businesses and drive efficiency.
Learning of the Blog
- What is Machine Vision?
- How does machine vision work?
- Machine Vision and businesses
Machine vision is a deep-learning (DL) technique, and DL (stemmed from Machine Learning) is a part of the ever-growing technology-Artificial Intelligence. For more insights, you can check out AI certificate programs and AI ML certification.
What is Machine Vision?
As mentioned above, machine vision or MV is an image-based automatic analysis and inspection based technology. It is used in process control, automatic inspection, and robotic guidance. In the industrial field, machine vision is a tool that offers autonomous control to machines. It is often used interchangeably with computer vision, but actually, it includes computer vision. Computer vision is the technology that enables images to be processed and understood. In the case of an industrial robot specially programmed and equipped to detect faulty products on the production line, computer vision works with algorithms that identify the visual defect. Whereas, the machine vision system is the entire system that identifies and removes defects from the production line.
How does Machine Vision Work?
The use of machine vision systems in manufacturing facilities traces back to the 1950s, expanding in the 1980s. Regardless of the application, machine vision systems are made possible by a combination of hardware and software. Sensors, cameras, analyzing software, pattern identifying algorithms, and frame grabber are some of the typical components involved. These components work together to inspect a product in a manufacturing operation. There are three main steps involved in the working of machine vision:
- Capturing– The image is captured using digital cameras, vision sensors, and infrared or ultraviolet cameras. The image is captured by the hardware and transformed into a digital one.
- Processing– Image processing algorithms are used to analyze the digital data coming from the hardware. Image processing is further divided into these steps:
- Pre-processing: This step involves contrast-enhancing and noise removal.
- Recognition– One part is segmentation (applying threshold and determining the edges of the image). The other is feature extraction(extracting features such as length, color, shape, size, etc.)
- System action– The machine is instructed to perform the necessary action based on the extracted information.
Machine Vision and Businesses
Machine vision has grown from a computer science concept to a critical feature of manufacturing. The latest systems help sort products, identify defects, and offer incredibly flexible solutions to various tasks—businesses relying on production benefit the most from machine vision. In cases where there is no room for error, such as creating a piece of intricate equipment in medical devices or sensors used in aircraft or cars, pin-point precision and reliability are required, which is not possible by humans. Heavily regulated industries now rely on machine vision to detect defects in their medicinal products. In the future, personal inspection will not be accepted. Risk minimization plays an increasingly central role in the pharmaceutical sector, and machine vision exhibits paramount capability.
Machine vision is also helping businesses in other ways:
- Farming– The location of grapes on the vine can be detected by harvesting machines using machine vision. This way, robotic harvesting machines pick up bunches without destroying any grapes. Another use of machine vision in farming is to monitor crops and detect diseases on plants.
- Inventory management and control– The process of reading labels and barcodes on products and components is carried out by machine vision. This is imperative for inventory control and ensures that correct components are added as products move down an assembly line. In warehouses, machine vision is critical for robots in bin-picking.
- Calibration and measurements– Machine vision makes the process of measuring quite efficient. Whether the need is to identify a gauge for calibration or to ensure that a gap in the spark plug fits the specifications, automation can be carried out.
- Rectifying production line benefits– Along with identifying defective products, machine vision helps determine at what point problems are introduced in a production line so that corrective action can be taken.
- Safety– Machine vision can effectively improve safety at a construction site with heavy equipment or track food supplies.
- Product traceability and tracking– It is essential to track ingredients, serial numbers of products, and monitor expiration dates in heavily regulated industries like pharmaceuticals. Machine vision makes this extraordinarily easier.
Machine vision has come a long way since it was only used for inspection because of connectivity, big data analytics, and data storage. It is now a part of large business intelligence networks helping them understand complex manufacturing processes and driving more effective collaborations between an artificial intelligence expert, a production worker, and business management. If you wish to explore this future technology, you can check out the available certificate course in artificial intelligence.