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Quality Check (QC):
is an unnecessarily integral part of each manufacturing process.
There are multiple quality checks integrated both during and at the end of the production process.
For example, medical sensor production can have the following quality checks in place:
visual inspection at each step of the production (In Process Control or IPC)
visual inspection of the final product
In-use tests, i.e using the final product in a real-world environment to test accuracy, stability and detect possible defects
Problem with some quality checks (for example, in-use tests) is that they degrade the product quality, hence can’t be used extensively.
Visual Inspection:
On other hand, visual inspection doesn’t harm the inspected product and can be used abundantly. Therefore each produced product should, at least, be visually inspected.
We mainly inspect the followings visually in the production settings:
Product completeness: fill level, feature presence, assembly verification
Correct location of parts: orientation, skew
Product quality: part defects, surface inspection, contaminants
Main problem with visual inspection is that, in a lot of cases, it is difficult to automate it.
In other words, it requires substantial human labor to inspect the produced products visually.
Hence visual inspection is not done elaborately in a lot of cases.
Automated Visual Inspection
The good news is that, recent advances in Artificial Intelligence (AI) made a lot of visual inspection tasks possible to automate. Now, AI models can even surpass human performance in some visual inspection problems. On top of that, they
don’t get distracted
don’t get tired and can work 24/7
don’t get ill (flu, coronavirus, …)
Building Automated Visual Inspection (AVI) system can be divided into 6 parts:
Installing cameras. It includes choosing the correct camera and lens, having optimal lighting condition and good camera position.
Taking and storing images. Challenges of this step are having a big enough storage to store all the images and performing regular backups.
Annotating images. Image annotation is needed if the inspection is going to be done by an AI model, which will be developed using a supervised machine learning method.
Training and deploying models. This step is usually the most difficult step in the AVI pipeline and should produce a model with high enough accuracy.
Validating models. In case the model is developed to do a task, which is regulated by law, then the model needs to be validated. This requires creating a document about all the details of the AVI system and sending it to the responsible regulation office. You are allowed to use your automated visual inspection system after you get approval of regulators.
Process control software. The last step is building a dashboard, which will be used by the process control team to check the inspection results. For example, the dashboard can include percentage of defect products by time, how many defects there are in each defect category, etc.
Defect detection in manufacturing (demo):
The first step will be installing a camera at the end of the production line. It can be an ordinary camera as long as it’s not too hot, dirty or humid near the camera. Otherwise you’ll need to have a solid box around the camera to protect it from dirt, humidity or accidental damage. Apart from that, you’ll also need to install lights and adjust them so that you have crisp pictures of each steel piece.
Now you should look at the images, create a catalogue of defects with clear descriptions and edge cases.
Next, you need to analyse the defect catalogue and think about how you can detect those defects automatically and reliably. You need to choose one of the following ways:
Developing a rule based defect detection method yourself for each defect category. It involves writing software to extract information from the images using thresholding, morphological operations and other similar techniques. This method is recommended if the defects are relatively simple to detect. For example, if the defect is detectable solely by colour separation. If the defects come in different shapes and colours, developing software to detect each defect will probably produce an unaccurate and unstable result.
Annotating images and letting an Artificial Intelligence model learn from it about how each defect looks like and how to detect it. Today AI and machine learning methods have enormous power. AI is capable of learning almost any defect structure and can attain accuracy on par or better than a human expert.
Visual Inspection Use Cases
HEALTHCARE
In the fight against COVID-19, most airports and border crossings can now check passengers for signs of the disease
Baidu, the large Chinese tech company, developed a large-scale visual inspection system based on AI. The system consists of computer vision-based cameras and infrared sensors that predict the temperatures of passengers. Now the technology, operational in Beijing’s Qinghe Railway Station, can screen up to 200 people per minute. The AI algorithm detects anyone who has a temperature above 37.3 degrees.
Another real-life case is the deep learning-based system developed by the Alibaba company. The system can detect the coronavirus in chest CT scans with 96% accuracy. With access to data from 5,000 COVID-19 cases, the system performs the test in 20 seconds. Moreover, it can differentiate between ordinary viral pneumonia and the coronavirus.
AIRLINE
According to Boeing, 70% of the $2.6 trillion aerospace services market is now dedicated to quality and maintenance. In 2018, Airbus introduced a new automated, drone-based aircraft inspection system that accelerates and facilitates visual inspections. This development reduces aircraft downtime while simultaneously increasing the quality of inspection reports.
AUTOMOTIVE
Toyota has recently agreed to a $1.3 billion settlement due to a defect that caused cars to accelerate even when drivers attempted to slow down, resulting in 6 deaths in the U.S. Using the cognitive capabilities of visual inspection systems like Cognex ViDi, automotive manufacturers can analyze and identify quality issues much more accurately and resolve them before they occur.
COMPUTER EQUIPMENT MANUFACTURING
The demand for smaller circuit board designs is growing. Fujitsu Laboratories has been spearheading the development of AI-enabled recognition systems for the electronics industry. They report significant progress in quality, cost, and delivery.
TEXTILE
The implementation of automated visual inspection, along with a deep learning approach, can now detect issues of texture, weaving, stitching, and color matching.
For example, Datacolor’s AI system can consider historical data of past visual inspections to create custom tolerances that match more closely to the samples.
We will conclude with a quotation from the general manager we mentioned earlier: “It makes no difference to me whether the suggested technology is the best, but I do care how well it’s going to solve my problems.”
Ten basic steps to successful machine-vision-system design:
Step 1. Determine inspection goals
The cost budget and the application benefits.
system variations
human operator profile
Step 2. Estimate the inspection time
Calculating the overall system inspection time should include material handling, image acquisition, and data processing
Some techniques that can improve inspection-time performance:
Digital cameras
MMX-enabled vision software
Image-acquisition hardware
Step 3. Identify features or defects
Statistical techniques, such as ranking the defects from highest to lowest probability, can be used to help evaluate all the defects.
Creating an image database of defects and acceptable components is important in developing a vision strategy.
Step 4. Choose lighting and material-handling technique
Proper lighting and material-handling devices significantly reduce the software development time
Step 5. Choose the optics
The minimum resolution and field-of-view (FOV) requirements are two key parameters in selecting a lens and a camera.
Other important considerations involve the "working distance," or the distance from the lens to the object.
Step 6. Choose the image-acquisition hardware
Choosing a camera and calculating the required image-acquisition rate are based on whether the vision system will be positioned in-line with a manufacturing process or used as an off-line inspection system.
An important technical consideration for color image-acquisition hardware is to have RGB to hue-saturation-luminance (HSL) conversion capabilities.
Step 7. Develop a strategy
Building a vision application often involves clever experiments performed on the image.
An improved approach to developing vision applications is to use, in combination, a high-level prototyping tool, a low-level general-purpose vision software library, and an application-development environment for user interface and display functionality
Another imaging option is a linescan camera.
Step 8. Integrate acquisition and motion control
Triggering an acquisition when the object is within the field of view of the camera can be accomplished by using an inductive proximity switch or a photocell.
When the product is positioned correctly, an inductive loop or photocell drives a digital line to trigger the camera and the image-acquisition hardware.
Step 9. Calibrate and test the inspection strategy
Before running the test strategy in-line, developers must create a calibration code that quantifies the camera, lens, and lighting system, or other sources of parameter drifts.
If the inspection strategy proves deficient, first examine the calibration software to see if any parameters have drifted. The calibration software can quantify the average and the standard deviation of pixel values using a black body.
Next, image a black body and use horizontal and vertical line profiles to determine if the lighting is homogenous across the field of view.
Then, test the system in-line. If the application goal is 100% inspection, the inspection system might need to be tested through several manufacturing cycles.
Step 10. Develop an operator interface
Develop an operator interface that incorporates components for calibration, automatic system set-up, and system testing.
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