AI in the manufacturing industry has improved the safety and efficiency of all its operations. One of the popular technologies that manufacturing industries use is Machine vision. It performs automated visual inspection on the manufactured objects in thousands every day.
With the advancements made in Artificial Intelligence, the process of algorithms has become superior. Deep learning-based inspection models often work with machine vision systems. You can easily strain these systems before implementing them.
Let’s see how machine vision applications are changing the world of manufacturing.
What is Machine Vision?
This technology uses methods to provide automatic imaging-based inspection. In the industries, personnel use it for visual inspection of goods and parts. It also has uses in defect detection, positioning and measuring parts and identifying, tracking and sorting products.
Machine vision applications are more reliable, robust and stable. They have high mechanical and temperature stability even at their low cost. These applications are highly accurate.
There are quite a few different uses of this application. However, the major one is inspection.
Identifying Irregularities, Defects and Other Manufacturing Flaws
A machine vision system for inspection detects defects, functional flaws, contaminants and other irregularities.
For instance, it inspects tablets of medicine for flaws. This system assesses displays to verify the icons confirm pixel presence or even measure the level of backlight contrast.
Machine vision applications can also inspect products to assess for completeness. It ensures a match between product and package in the food and pharma industry. For example, it checks on safety seals, caps and rings on bottles.
The Versatility of Machine Vision Applications
Usually, machine vision applications have a library of software tools. These tools perform different types of inspections. You can also incorporate multiple inspections from captured images. Machine vision inspection is versatile. You can see it from the following list of tasks:
- Determine the position of an object, for instance, to verify proper label placement
- Maintain the package integrity of the medical products. Check the medicine vials to ensure they are fully closed. Secure them with tamper-proof seals.
- Verify that an object’s properties meet quality standards. For instance, the position and volume of glue beads used in automobile transmissions.
- It inspects manufactured goods. This helps to identify flaws like surface scratches, bent needle tips and incomplete solder traces.
- Do a thorough counting of items like blister packs, bottles in a case and components of any kit.
- Check on the characteristics of completed assemblies like inclusion of consumer safety interest and the completing assembly operation.
- Detection of tool wear in machine operations before their shelf life ends.
- It helps to measure dimensions on a microscopic level, thus keeping you alert at all times.
While the machine vision applications do all this work, do you know exactly how they do it? What is the inspection process they use? Let’s find out.
Role of Machine Vision Inspection: How Does it Work
As the machine vision tolls inspects and analyses an object, it performs the following procedures:
1. Image Acquisition
As the incoming object approaches the system, it is detected by a sensor. Light sources and cameras influence the sensor’s input by lighting up the area and capturing an image of the object at exactly the right time.
A successful image acquisition is necessary. Using the right optics and lighting techniques ensures high image quality. This is fundamental to carrying out an accurate and reliable analysis helping in quality control.
2. Image Processing and Analysis
The acquired image is pre-processed as technicians apply algorithms and several specific filters. They do so to detect the product in the image and highlight the key features of interest.
Depending on the filtered image, you can use the algorithms to generate a data object. This contains the characteristics of the inspected object’s highlighted features.
The design of the algorithm impacts the efficiency of the image processing and analysis. When the algorithms are powerful, they facilitate accurate, high-speed vision inspection. It maximises product quality and throughput.
3. Result Evaluation
Another one of the machine vision applications is how it evaluates data. The software extracts data by comparing characteristics of the object’s highlighted key features with a predefined set of tolerances. These tolerances define the target quality level expected from the producer.
4. Decision Making
Subsequently, the system makes decisions based on the comparison between the processing results and the set tolerances. It classifies the inspected object, such as passed or failed. Additionally, certain machine vision systems may have the capability to reject the product or trigger warnings, alarms, and similar actions.
5. Process Control
Some machine vision systems use the gathered inspection data to identify trends in production. This allows them to give recommendations to the machine operators for production optimisation. They can do it via data platforms that provide production data overviews while highlighting the crucial information.
They also support them in identifying root causes. Also, you can monitor and evaluate the adjustment of process parameters to optimise the overall production. They can compare it with the performance of different shifts, production lines and facilities to align the optimisation procedure.
6. Object Detection
This is a use case where the machine vision applications look for individual objects rather than the entire image. The goal of this exercise is to identify multiple objects inside an image. In this way, it can eliminate the irrelevant objects and only focus on the relevant ones.
Such systems use a variety of techniques to ensure object detection is as efficient as possible. The manufacturing industry also uses object identification for functions like sorting, inventory, quality management, etc.
For instance,for the gearbox assembly chain, machine vision analyses the image for specific parts. They do it to confirm the presence or absence of those specific parts.
7. Surface Detection Identification
This is another machine vision application that is crucial for quality control. Manual identification of surface defects is a tedious task and can lead to missing out on some defects.
With machine vision, companies have the accuracy and efficiency of surface inspection in an easy-to-train model. In manufacturing industries, it can detect defects in casting components, different metal surfaces and bearings. For instance, packaging defect identification can scan and identify bad packaging which may cause damage to the product during transport.
8. Print Defect identification
It detects anomalies in the prints like inconsistencies in colour, text or pattern. Manual inspection by human operators might lead to oversight that may lead to deterioration in quality. However, machine vision can perform print defect identification using AI. They do not miss out on any defects, especially the minor ones. Such technology benefits the production of prints, labels, and packaging prints.
9. Barcode Scanning
Industrial robots can read barcodes and data matrix codes. It helps to categorise products which the AI identifies. This can be error-proof production and packaging processes.
Machine vision barcode scanners are more efficient than the manual categorisation process. This is because machine vision applications have the potential to be error-prone. Barcode scanners can separate products based on their attributes or features.
10. Locating
Pattern recognition and machine learning can locate an object and scan its coordinates. This relieves the operator looking for it. Since it can locate an object at any point, it helps supply chain management and logistics to streamline their functions.
Such technology can find misplaced items. This ensures that the inflow and outflow of various parts from one section into another is smooth. Machine vision identifies and keeps track of all such objects through various means.
FAQs: Use of Machine Vision Applications in Industrial Inspection
How does machine vision review quality issues in industrial equipment?
These technologies use cameras and sensors with sophisticated image-processing algorithms to automate the inspection process.
How are AI-based surveillance technologies cutting costs?
Such advanced technologies help to cut down on the costs of on-site security personnel. AI solutions provide real-time analysis and alerting, which can detect and alert authorities only when necessary.
What is the accuracy of machine vision systems?
Such technology has 99.4% of successful results.
What are the applications of machine vision cameras?
It helps in quality control, inspection of items and guiding objects through an assembly line. These systems collect a lot of data about these objects to fully analyse them.
Conclusion
Machine vision applications have revolutionised the manufacturing landscape. It assures product quality and operational efficiency. Its versatility extends to barcode scanning, parts counting, and object detection, streamlining processes and enhancing productivity. Head over to Qodenext to learn more about the latest technologies in the supply chain world.
