Artificial intelligence (AI) is proving to be a game-changer, with numerous implementations in nearly every domain.
It is now finding its way into the field of production and manufacturing, enabling it to leverage the power of deep learning and, as a result, provide automation that is quicker, cheaper, and more superior.
Table of Contents
- When is AI- based Visual Inspection required?
- Limitations of Manual Inspection
- Impact of Deep Learning in Visual Inspection
- Requirements for AI-based Visual Inspection
- Steps to integrate AI-Based Visual Inspection System
- Benefits of AI-Based Visual Inspection
What is AI-based Visual Inspection?
It entails analyzing products on the manufacturing line for quality management purposes.
Visual Inspection may also determine the internal and external equipment condition in a manufacturing plant, such as storage tanks, pressure vessels, piping, and other machinery.
It is a mechanism that occurs at regular periods. It repeatedly shows that visual inspection results in the detection of the majority of concealed defects during development.
When is AI- based Visual Inspection required?
Although the Visual Inspection is used in manufacturing to evaluate quality or defects, it may also operate in non-production environments to decide if the features indicative of a “target” are present and avoid possible negative consequences.
Because of the extreme cost of any errors during the Inspection, such as accident, fatality, loss of costly machinery, scrapped products, rework, or customer loss, many companies consider visual Inspection to be of very high consequence a top priority operation.
Nuclear arms, nuclear power, airport baggage processing, aircraft maintenance, the food industry, medicine, and pharmaceuticals are only a few examples of areas where visual Inspection prioritized.
Limitations of Manual Inspection
Manual Inspection necessitates the presence of an individual, an inspector, who assesses the object in question and renders a judgment based on training or prior knowledge. Except for the qualified inspector’s naked eye, no equipment needed.
Some flaws are the result of human error, while others are due to space constraints. Specific errors can be minimized but not eliminated by training and practice.
Optical illusions like the ones seen to the left show how inaccurate the human eye can be. This isn’t to say that manual Inspection is entirely useless, but it’s also not a good idea to rely solely on it.
The human eye, particularly on a tiny scale, is incapable of making precise measurements.
Even when comparing two identical items, the watch can miss the fact that one is slightly smaller or larger.
This definition also refers to characteristics like surface roughness, scale, and every other measurable factor.
Due to the hiring of (multiple) qualified persons, manual Inspection remains a costly endeavour.
These issues can be solved using automated visual Inspection, eliminating the need for any human intervention in the visual inspection process.
The level of manual review usually exceeds by using computerized systems.
It is possible and feasible to create smart systems that perform detailed quality checks down to minor information using deep learning and machine vision.
To automate manufacturing, such as Inspection, we don’t need walking talking android robots.
Deep Learning is used to make the algorithm smarter. Image acquisition, pre-processing, feature extraction, classification, and other steps usually include this method.
Impact of Deep Learning in Visual Inspection
Artificial neural networks are used to power deep learning, a form of machine learning technology.
Deep learning technology works on the concept of teaching machines to learn by example.
It is possible to extract common patterns between labeled examples of specific data types and then turn them into a math equation by supplying a neural network with labelled models.
Integrating deep learning algorithms into visual inspection technology allows for identifying parts, anomalies, and characters, simulating a human visual inspection while operating a computerized device.
Build a deep learning-based algorithm and train it with examples of defects to detect if you’re making visual inspection software for the automotive industry.
With enough data, the neural network will gradually identify weaknesses without the need for any further instructions.
Visual inspection systems focused on deep learning are capable of detecting complex defects. They discuss complex surfaces and cosmetic flaws and generalize and visualize the characters of the pieces.
Requirements for AI-based Visual Inspection
Visual Inspection does not necessitate a lot of physical equipment—the hardware and software resources required to automate visual Inspection.
This includes primary equipment like a camera, photometer, and colorimeter. And optional secondary equipment like grading or sorting equipment, based on the industry and automation processes.
At its core of Visual Inspection, the software layer helps inspect products or any item of interest for defects and the absence/presence of some components.
Advanced image analysis algorithms and heavy programming need for the software part of a Visual Inspection system.
These algorithms change the quality of images, identify interesting points and regions, and then make decisions based on the features found in these areas.
Steps to integrate AI-Based Visual Inspection System
1. State the Problem
A market and technical review is often the first step in the implementation of visual inspections.
The aim is to figure out what kinds of flaws the device should be able to detect. Based on their responses, data science engineers select the best technological approach and flow to follow.
2. Collect and prepare data
Until deep learning model creation may begin, data scientists must collect and prepare the data needed to train a future model.
It is essential to integrate IoT data analytics in manufacturing processes. The data used by visual inspection models are frequently video recordings, with video frames included in the images processed by the model.
The accuracy of the video recording is the most significant factor here. Results would be more reliable if the data is of higher quality.
We prepare the data for modelling by cleaning it, checking for anomalies, and ensuring its relevance.
3. Develop Deep Learning model
The choice of a deep learning model development method determined by the task’s difficulty, expected delivery time, and budget constraints.
4. Train and Estimate
After creating the visual inspection model, the next step is to train it. At this point, data scientists validate and test the model’s output and result inaccuracy.
A test dataset is helpful in this case. For a visual inspection device, it may be a set of video records that are either old or close to those we want to process after deployment.
5. Deploy and Update
It is essential to understand how software and hardware device architectures correspond to model capability when deploying a visual inspection model.
Benefits of AI-Based Visual Inspection
It commits much more productivity.
Since visual Inspection is repetitive, it can be a tiresome task for humans. Machines, without a doubt, will function indefinitely without being affected by emotions or the need for a break. The production would be far superior to that of humans.
The error margin is negligible.
Machines are improbable to make a mistake once the data sets are ready to go. Error, however, is human. Detecting the error and reaching a conclusion after hours/days of poring over manuals and papers is prone to errors.
Saves time and money
AI-based visual inspections are absolute necessities for any company. In the case of a computer, detecting the anomaly and documenting it would take far less time. You need to invest once in automation set up to enjoy the fast performance.
AI-powered visual Inspection is already used in the airline industry, healthcare, automobile, equipment manufacturing, and textile industries.
Even after implementation, there is space for improvement in automation.
The model’s accuracy can improve over time by gathering new data and re-training the model.
If your business activity necessitates a significant investment in human visual Inspection, it is time to implement AI-based Visual Inspection