AI-visual inspection and image processing are offering a solution to many repetitive tasks. This new technology is especially valuable in the manufacturing industry. In the manufacturing unit, there are various tedious processes of grading, sorting, and processing materials. You will have a more thorough understanding of the benefits of automation. But the automatic systems haven’t been implemented properly due to limitations of sensory. This condition is starting to change as computer technology evolves through the use of artificial intelligence advances and machine learning. Now, let’s look into the reasons why AI image processing is useful for developing and establishing businesses.
Visual inspection is a challenging stage in any manufacturing process. This is especially true for products with difficult characteristics, such as highly viscous parenteral solutions where air bubbles cannot be completely eliminated, making it problematic to differentiate them from particles. Those cases usually require long development and optimization times for vision algorithms before achieving a balanced operational level of detection versus false reject rates. Artificial Intelligence has the potential of shortening this development period and optimizing the desired results more quickly—a classic win-win situation for both manufacturers and the consumers, who ultimately receive high-quality products. With visual inspection technology, the integration of deep learning algorithms allows differentiating parts, anomalies, and characters, which imitate a human visual inspection while running a computerized system.
Its main advantage is that it enables machines to learn by example rather than explicitly programming. As a result, it becomes a valuable tool for difficult-to-automate tasks.
Deep learning is based on teaching a machine to recognize specific patterns by feeding labeled examples into a neural network. After learning those patterns, the device can apply them to new data to identify defects.
The combination of machine vision and deep learning technology has resulted in a new method of inspecting products and services. While machine learning is better suited for precision alignment. Deep learning technology employs neural networks to mimic the ability of the human brain to learn by example.
Using Faststream Technologies’ deep learning-based computer vision technology powered by AI in the Manufacturing units the production cycle can be optimized by automating material quality inspection. The main purpose of it is to minimize human intervention and at the same time reach human-level accuracy or more as well as optimize factory capacity, labor cost, etc. Our usage of deep learning has varied, from object detection in self-driving cars to disease detection with medical imaging. Deep learning has proved to achieve human-level accuracy & better. Overall, Faststream’s Automatic optical inspection solutions based on image analysis are finding production environments where quality inspections are required.
AI visual inspection systems are gaining traction in Production and Manufacturing. Deep learning is used by AI solutions for this task to automate inspections with high accuracy and improve decision-making processes. They can do everything from image classification to defect detection.
A set of training and test images serve as the foundation for image classification models. Images of various products without defects can be found in the training set, while images of defective products can be found in the testing set. A CNN model for inspection enables sophisticated learning with a large number of defect images. This method allows AI vision inspection systems to find and categorize defects in different environments.
In the last few years, various industries have used cameras to monitor and sort produce such as fruit, vegetables optically, and fish. However, The whole system required a certain level of human involvement as the previous image analysis algorithms were dependent on tuning that had to be performed by human operators. With the use of Faststream Technologies AI image processing, optical grading, and sorting can become exponentially faster, operate autonomously, and become more accurate.
These systems are good for a range of high-quality inspection processes, allowing them to grade and sort products accurately. Here are some examples:
The advances in AI image processing and objective tracking can be implemented with the help of Robotic vision. Robotic vision helps in navigation and mobility, like moving materials around a warehouse. Faststream Technologies is working on Robotic image processing technology using machine learning software, which helps the Robots to study accurately the environment and make the correct reactions to the various events and features they encounter.
| Industry | Targets | Defects |
| Automobile Parts | Material Parts, Resin Parts, Fabrics | Scratch, Cracks, Dirt, Dent |
| Electronic Parts | PCB, Electronics Parts, Electronic Components, Panel | Scratch, Crack, Burr/Chip |
| Building Material | Wood Board, Sash, Metal Fitting, Tile | Surface Pattern, Scratch Crack, Dirt, Dent |
| Non-Ferrous Metals | Wire, Cable, Aluminium, Stainless Steel | Scratch, Cracks, Dirt, Dent |
| Raw Material | Chemical Fibre, Rubber, Glass, Paper, Pulp, | cratch, Cracks, Dirt, Dent |
| Medical | Medicine | Foreign Object, Wrong Print, Crack |
| Food | Processing Food, Beverages | Foreign Object, Wrong Print, Leak |
| Others | Material Parts, Resin Parts | Defect Classification, Shape Check |
Imprecision of Eyesight
The human eye is incapable of making precise measurements, especially on a very tiny scale. Even while comparing two similar objects, the eye might not notice that one is slightly smaller or larger than the other. This concept also applies to characteristics such as surface roughness, size, and any other factor that needs to be measured.
Cost of labor
Machine Vision has a very high optical resolution which depends upon the technology and equipment used for image acquisition. Compared to human sight, machine vision has a ‘wider’ spectrum of visual perception with the ability to perform observations in the Ultraviolet, XRay, and Infrared regions of the spectrum as well.
Better Perception Optimisation
Manual inspection remains a costly venture due to the appointment of (multiple) trained individuals.
Faster
Observations, as well as conclusions, are made extremely fast, with the speed of a computer’s speed as measured in FLOPs, and also, they result in precise calculations.
Imprecision of Eyesight
The human eye is incapable of making precise measurements, especially on a very tiny scale. Even while comparing two similar objects, the eye might not notice that one is slightly smaller or larger than the other. This concept also applies to characteristics such as surface roughness, size, and any other factor that needs to be measured.
Cost of Labour
Manual inspection remains a costly venture due to the appointment of (multiple) trained individuals.
Better Perception Optimisation
Machine Vision has a very high optical resolution which depends upon the technology and equipment used for image acquisition. Compared to human sight, machine vision has a ‘wider’ spectrum of visual perception with the ability to perform observations in the Ultraviolet, XRay, and Infrared regions of the spectrum as well.
AI-based visual inspection automation is used in manufacturing and production for defect detection, product quality assurance, inventory management, and other purposes.
Real-world applications of AI-based visual inspection are:
AI vision-based inspection solutions, such as AI vision inspection, can help reduce production costs and increase productivity by up to 90%.
Advantages
The benefits of incorporating AI in vision-based inspection appear unrivaled, and its accuracy and efficiency can be improved over time as it feeds on new data and deep learning. If your company requires AI-powered visual inspection solutions, look no further than Faststream Technologies.
The semiconductor industry’s routine manufacturing pipeline now includes the automatic wafer detection and elimination of defective parts. During the imprinting process, flaws may appear due to pressure, uncleanliness of the template, bubble formation, air contaminating the vacuum chamber, and several other factors. We offer the answer by gathering wafer images, importing them into the dataset, and labeling the images to indicate the presence of flaws like scratches and discoloration. Once deployed, the model can operate independently both on-site and in the cloud after being trained using the dataset.
In the automotive industry, robotic inspection systems can be used to identify defective parts, measure parts, or make sure that everything is assembled properly. The images of the car are gathered and imported into the dataset for automatic inspection of the assembly. For dents, scratches, and missing pieces, boundary boxes are used to label the images. The model is trained using the dataset, and once it is deployed, it can operate independently both on-site and in the cloud.
A robotic arm includes:
