Artificial Intelligence is proving to be a game-changer, with a plethora of applications in practically every field. It is steadily making its way into production and manufacturing, allowing operators to leverage the power of deep learning. As a result, faster, cheaper, and superior automation is now fast replacing the traditional manual labor. The purpose of this blog is to provide a basic understanding of automated visual evaluation and how deep learning techniques can save time and effort.
What Exactly Is Visual Inspection?
Visual Inspection is the process of a human being analyzing a product, or a process carefully. If there are faults that are visually found they can be fixed accordingly. Visual inspection relies completely on human senses and is the most cost effective way of quality control.
It entails analyzing products on the production line for quality control purposes. Visual inspection can also be used to check the various equipment in a manufacturing plant, such as storage tanks, pressure vessels, pipes, and other equipment, both internally and externally.
It is a procedure that occurs after regular intervals, such as on a daily/monthly basis. Visual inspection has been a proven way to uncover the majority of hidden flaws during production time and time again.
Where Do We Use Visual Inspection?
While visual inspection is commonly used in manufacturing to assess quality or detect defects, it can also be used in non-production situations. For example, manufacturing operators can determine whether traits indicative of a “target quality” are present and thereby avoid potential negative consequences.
There are various industries where visual inspection is necessary and is regarded as a high-priority task. In such industries, the cost of the slightest inspection errors can be severe- such as injury, mortality, loss of expensive equipment, discarded items, rework, or a loss of clients.
Nuclear weapons, nuclear power, airport baggage screening, aviation maintenance, the food sector, medicine, and pharmaceuticals are all areas where visual inspection is prioritized and plays a crucial role.
Limitations of Manual Visual Inspection
Multiple visual inspections are usually performed at different stages of the production process. Because visual inspection is extremely manual and requires intended focus from human employees over long periods of time (something most people aren’t particularly good at). It often becomes a time-consuming and error-prone operation. Traditional visual inspection procedures have a number of issues as mentioned below:
- Traditional inspection technology, which is inflexible and difficult to adapt in fast-paced operations, must be reconfigured frequently to accommodate product changes.
- The current crop of manual inspectors differ from one another based on their experience and the limitations of human perception. This results in the inconsistency of quality control.
- Human inspectors’ error rates are believed to be between 20 and 30 percent. According to a McKinsey study, AI-based visual inspection can result in a 50% gain in productivity and a 90% improvement in flaw detection accuracy.
Why Are We Thinking About Alternatives To Manual Visual Inspection?
While it is true that old is gold, there are some drawbacks to employing an old-fashioned inspection method.
Manual inspection necessitates the presence of a person, an inspector, who assesses the entity in question and renders a judgment based on training or prior knowledge. Except for the skilled inspector’s naked sight, no equipment is necessary.
Visual inspection errors often range from 20% to 30%, according to study (Drury & Fox, 1975). Some flaws are the result of human error, while others are due to space constraints. Certain faults can be decreased but not totally eliminated via training and practice.
Visual inspection errors in manufacturing take one of two forms — missing an existing defect or incorrectly identifying a defect that does not exist (false positive). Misses tend to occur much more frequently than false alarms (See, 2012). Misses can lead to loss in quality, while false positives can cause unnecessary production costs and overall wastage.
New Ways Of AI Visual Inspection
Machine learning is used in AI-based visual inspection to automatically assess product quality by evaluating unstructured image and video data. Manufacturers may use AI and computer vision technology to automate product problem identification, saving both time and money while also enhancing quality control.
Here are some of the special advantages of combining traditional inspection methods with AI and ML techniques:
- Defect detection is improved by freeing up mental resources for manual inspectors.
- Automatically adapting to product changes without the need for extra programming.
- Inspection of tens or hundreds of product regions in real time.
Internal and external assessments of product facility equipment, such as storage tanks, pressure vessels, pipes, and more, can also be done with visual inspection and AI. During production, AI-based visual inspection allows for a more thorough and efficient discovery of concealed problems.
Furthermore, no-code AI application development solutions enable manufacturers to take advantage of this breakthrough technology. There is no need to hire technical expertise or invest considerable time and money.
For manufacturers, the bottom line is that visual inspection AI outperforms human operators in terms of speed, accuracy, and repeatability. Machine vision systems can check item characteristics that are undetectable to the human eye, faster and more accurately. On a production line, AI-based visual inspection can consistently and repeatedly scan hundreds or thousands of parts per minute, greatly surpassing the capabilities of human inspectors.
Real World Applications of AI Visual Inspection
In manufacturing and production, AI-based visual inspection automation is used for defect identification, product quality assurance, inventory management, and much more.
The following are some examples of real-world uses of AI-based visual inspection:
- Product fault detection, making product defect detection more automated and accurate (e.g., cosmetic issues, bad welds, assembly errors).
- Damage detection, automating the detection of damage to equipment or structures (e.g., surface cracks, water damage).
- Monitoring and detection of corrosion in boilers, pipes, storage tanks, vessels, and other manufacturing equipment precisely.
- Automated equipment inventory management to swiftly transcribe equipment tags and save them in a database ( also known as asset labeling).
Requirements Of AI Visual Inspection
To conduct visual inspection using AI and ML, you need a combination of complementary hardware and software that mimics human visual inspection with greater accuracy and efficiency.
Let’s take a look at some of the hardware and software that are required in order to carry out AI visual inspection:
Hardware
Feeding System
A feeding system distributes things equally and moves them at a steady speed so that the optical system may collect individual item frames.
Optical System
It consists of a sensor and a specially calibrated illumination source (usually, a digital camera). Images of inspected goods are captured by the optical system, which is then processed and analyzed by the software.
Separation System
The separation system removes defective goods and/or grades. Subsequently, it divides products into several quality categories.
Software
The software layer, which at its foundation is computer vision technology, helps inspect items or any object of interest. It looks for flaws and the absence/presence of certain target traits, to augment the process of visual inspection.
Advanced image processing methods and substantial programming are required for the software element of an automated visual inspection system. These algorithms improve the quality of photos, discover interesting points, traits, patterns, and then make decisions based on the findings.
The Bottom Line
Thanks to the emergence of accessible deep learning & computer vision techniques, developing intelligent systems capable of surpassing human-level precision in visual inspection is too easy now! As the AI system improvises itself with time, the AI-powered visual inspection system incorporates the capability of human assessment.
The operators can identify manufacturing anomalies and corresponding issues faster and as a result, profitability increases. If you are looking to develop and deploy a customized visual inspection AI solution within weeks, contact the Aidetic experts today.
