In today’s supply chains, vision systems play a crucial role in enhancing efficiency and accuracy. These systems use advanced technology such as cameras, sensors and machine learning algorithms to interpret and analyse visual data.
Companies can seamlessly integrate them into supply chains, from manufacturing to distribution; these systems enable automated quality control, inventory management and even package sorting.
So, today we will delve into how these systems work within the supply chain. You will get an idea of how they play a role in optimising the processes, minimising errors and improving productivity and customer satisfaction.
Shall we take a detailed look at this phenomenon?
How Do Vision Systems Work?
The basic components are:
- Lighting
- Lens
- Image Sensor
- Vision Processing, and,
- Communications
Now let’s see how the above components interact when you check a product’s manufacturing process and how technology affects it.
- The sensor detects the presence of a product and the procedure begins.
- Then the sensor activates a light source to illuminate the region. There is also a camera that will picture the product or one of its components.
- Then the image that the camera captures gets converted into digital data by the frame-grabber. This is a digitising device that converts the image that the camera captures into digital data
- Then the digital file is stored in the computer system ready for evaluation by the software.
The program analyses the file against a set of specific criteria to find flaws. The product inspection will fail if there is a defect.
Read more about Machine vision and its applications
There are quite a few varieties of these systems as well.
8 Types of Machine Vision Systems
These systems can operate across various dimensions depending on the specific needs and requirements of a specific application. Here are some of the common types of systems:
- 2D vision
- 3D vision
- Smart camera-based vision-
- Compact vision
- PC-based vision
- Multispectral imaging
- Hyperspectral imaging
- Variable magnification lenses
Computer Vision Systems-The Better One?
When it comes to vision systems, there are two different types that companies can choose from-machine and computer vision. Let’s have a brief look at what makes them different if at all.
Do you know the difference between Computer Vision and Machine Vision?
Machine vision is a subcategory of computer vision. Both terms are interchangeable. The operation of machine vision uses a computer and specific software but computer vision does not require a machine.
Computer vision is adept in scanning digital web photographs and videos and it can also analyse “images” from motion detectors, infrared sensors and other sources. As per the Artificial Intelligence Global Surveillance Index, at least 75 out of 176 countries are globally active using AI-based surveillance technologies.
So, with all the latest technological advancements that these systems have, what do they do? Come, let’s see some of the solutions that these systems offer:
5 Functions of Machine Vision Systems
Machine systems can give quick and innovative solutions by automating tasks that are usually done in an industrial process. Let’s take a look at some of the tasks this system automates:
1. Presence Inspection
This refers to the process of confirming quantity and the presence or absence of parts. It is one of the basic operations that the system performs. It also happens to be one of the most idly performed tasks across industries.
Practical applications of presence inspection are counting countable products like bottles and screws, it checks the presence of labels on food packaging, electronic components on PCBs, adhesive application and screws/washers in fastened parts.
There are quite a few image processing methods that machine vision systems provide. Check them out below:
Binary Processing
This processing system has a monochrome camera that captures the image. It then converts the image into pixels with two shade levels, black and white, making vision processing and decision-making easier. The conversion of each pixel depends on a specific threshold.
Blob Analysis
The digitised image produced by binary processing undergoes further analysis. This is the Blob analysis. “Blob” refers to the cluster of pixels in the same shade. The digitised image is plotted on a coordinated system and the X and Y coordinates of each lump undergo analysis.
Experts use blob analysis in various states like counting (depending on the area), measuring length and area, assessing the target’s position in the space, distinguishing the orientation of targets, inspecting defects and others.
But wait, this is not all. There are a few other image processing and analysing techniques. Shall we see what they are?:
Configuring a predefined target in the vision processing unit enables the computer to search for similar targets in images. This analysis relies on colour differentiation, enhancing judgement accuracy.
2. Practical Positioning
Positioning involves comparing the part’s location and orientation to a specified spatial tolerance. A robot or machine gets the information about the part in 2D and 3D space which it then aligns and places the target in the proper position or orientation.
Machine vision positioning systems offer enhanced speed and accuracy than manual inspection, alignment, and positioning. Practical positioning applications consist of robotic pick-up and placement of parts and off the conveyor belt, positioning of glass elements, checking the barcodes and label alignment, and arrangement of parts packed.
3. Machine Vision Identification
Vision systems identification scans and reads barcodes, direct part marks, 2D codes, printed characters, labels and even packages. These markings have the name of the product, manufacturer, date code, lot number and expiration date.
With identification, companies can improve the traceability of parts, inventory control and verification system of products. Companies can achieve identification by an optical character recognition (OCR) or an optical character verification (OCV) system.
When it comes to the OCR systems, the machine vision reads the printed alphanumeric characters on the target with any prior knowledge of the characters to look for. However, in the OCV systems, the machine vision first verifies the presence of the character strings.
4. Flaw Detection
Quality control is very important in supply chain and manufacturing processes. One of the aspects of it is flaw detection. Manufacturing industries use it to search for defects in the products.
The machine vision can detect cracks, blemishes, gaps, contaminants, and discolouration on the product that hampers its functionality and reliability. These defects may appear randomly, so the algorithm tries to detect pattern changes in colour or texture, discontinuities or connected structures.
The next step is to monitor those defects. The system then categorises it by type, colour and size. It even sorts out the defective parts which fail to meet the criteria.
The best thing about this system is that it can detect even the tiniest of microscopic flaws. Usually, these are invisible to the human eye.
Uses of Flaw Detection
This technology has found widespread use in:
- Inspection of semiconductor and electronic devices
- Appliances
- Food products packaging
- Tooling conditions
- Materials made in continuous web-like paper, plastics, metals, etc
Flaw detection in online inspections stops processes upon detecting faults, correcting them immediately, and separating faulty parts. It’s integrated into machine vision systems with presence inspection, measurement, and positioning functions.
Here is the last use of vision technology in the industrial space.
5. Detection via Measurement
This refers to checking of dimensional accuracy and geometric tolerances of parts. The machine system assesses the distances between two or more points. The location of the targeted items features in the object helps to determine whether the measurement is in line with specifications.
You need to optimise the lighting and optical system of the vision system. This allows for highly accurate, precise and repeatable measurements. This function can measure features as small as 25.4 microns.. You can also use it to calculate the volume of parts.
FAQs: How Do Vision Systems Work in Supply Chains?
What are the advantages of a machine vision system?
You get automated inspection, increased accuracy, high-speed processing, consistency, cost reduction and enhanced quality control in manufacturing, ensuring product quality, minimising eros and improving overall efficiency.
Can vision applications detect possible supply chain hiccups?
Yes- they can do so by monitoring inventory levels, tracking production processes and identifying potential issues like bottlenecks or quality defects, enabling proactive problem-solving and smoother operations.
Which technologies will rule the supply chain industry in 2025?
Ai-driven predictive analysis, blockchain for transparency and security, and IoT sensors for real-time tracking will front the 2025 innovations.
Conclusion
Vision systems have become integral to modern supply chains, offering enhanced efficiency and accuracy through advanced technology like cameras and machine learning algorithms.
These systems automate tasks, minimising errors, and improving productivity and customer satisfaction. Qodenext, as a leading supply chain player, predicts that innovations like AI-driven predictive analytics and blockchain will continue to revolutionise the supply chain industry in 2025 and beyond.
