As industrial environments become increasingly digitized, manufacturers are considering how Artificial Intelligence (AI) can be implemented to automate processes that have previously required human operators. To this end, quality control is one of the most important processes within a manufacturing system, yet manual inspections often come with substantial drawbacks as defects can easily be overlooked by the human eye. Automating defect detection with AI-powered visual inspection can create a reliable means of mitigating quality control errors even in complex manufacturing environments.
Defect Classification & Manual Visual Inspection Limitations
Operator conducted Visual inspection is the method of looking for flaws or imperfections using the naked eye and non-specialized inspection equipment. This approach is commonly used by maintenance professionals in physical asset management across industries and often serves as the final step in the manufacturing.
However, defects can be easily missed by the naked eye, even for experienced operators. Even small oversights can have wide reaching and costly consequences, resulting in lower component quality or a defective final product that may be rejected. With more complicated manufacturing systems, the number of defects will likely increase.
Engineers are continuously developing new ways to automate repetitive manual activities and having an automatic system to detect abnormalities and discrepancies in elements is critical for the quality inspection process. AI is are the forefront of these efforts, as it enables the automation of sophisticated yet respective activities.
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The Concept of AI-based Visual Inspection Explained
While AI offers endless process automation potential, its initial benefits are often overstated as AI systems require careful implementation to ensure they operate successfully. In this case, AI is able to take on the task of automated visual inspection processes through the power of machine learning (ML), or learning by example In order to build an artificial neural network.
- Artificial Intelligence: Simulates human intelligence to automate a set behavioral patterns.
- Machine Learning: Algorithms that build models based on sample data in order to make predictions, decisions, or improvements without being explicitly programmed to do so.
- Deep learning: Artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data.
Through a process of extracting common patterns between labeled examples of specific data points. ML systems then translate case scenarios into a systemic equation, building a neural network with labeled examples and interconnected data points. AI systems rely their neural network to engage in deep learning, ultimately aiding in the classification of future data.
Benefits of AI Enabled Industrial Defect Detection
The following are some of the ROI that businesses can expect to see from implementing and AI powered defect detection:
- It improves production yield while lowering waste.
- Customer satisfaction rises as product quality rises.
- Warranty claims and after-sales defects are drastically minimized.
- Locating parts and ensuring that product assembly is completed efficiently.
- It is now possible to detect faults that are unpredictable and varied.
- Classifying problems and parts improved process control.
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How to Integrate/Implement AI Visual Inspection Systems
With enough data, the neural network will eventually detect defects without any additional instructions. Deep learning-based visual inspection systems are good at detecting defects that are complex in nature. They not only address complex surfaces and cosmetic flaws—but also generalize and conceptualize the parts’ surfaces.
These steps will help integrate and implement AI into a visual inspection system
1. Define the Problem
The needs of a visual inspection process are influenced by technical and business requirements. Standardizing a strict definition of successful process and optimum performance is necessary to determine what kinds of defects the system should be detecting.
For these reasons, it is important to consider what variables might influence process data. The environment in which the visual inspection system operating, whether or not inspection takes place in real time or will be deferred, or how extensive visual inspection should be in classifying separate data categories. Likewise, companies should research and see if there is existing software with a visual inspection feature, or if one will need to be built for your unique manufacturing processes.
Finally, it is important to define how an AI system should notify operators once a fault has been identified. This decision will influence whether or not the visual inspection system will track the number of defects found or if they will be identified and dealt with in sequence.
2. Collect Process Data
Each of the decisions in step 1 will influence what sort of data is used to build a machine learning model. Furthermore, data sources for visual inspection models are frequently contained in video records with included with images analyzed against the inspection model. Documenting good, excellent, and poor data points and the variety of flaws that may occur will assist in building a repository which the AI system will rely on.
Here are some common data collection strategies:
- Using existing video records as a starting point (can potentially be provided by a client)
- Using applicable open-source video recordings for defined purposes
- Gathering data from scratch to meet the needs of deep learning models
In each of these scenarios, the quality of the video recording is the most essential factor. Results will be more accurate if the data is of higher quality.
3. Build a Deep Learning Model
Before deep learning model creation can begin, data science engineers must gather and prepare the data needed to train a future model. It’s critical to use IoT data analytics in manufacturing processes. When it comes to visual inspection models, the data is frequently video records, with video frames included in the images analyzed by the model. There are a variety of data collection methods available, however the following are the most common:
- Existing Client Video Records: serves as an excellent starting point when you have small data sets, or if the client has relevant data points for your equipment.
- Open-source video recordings: useful for building a repository of common defects that are well documented by external sources.
- Building unique data sets: while video records from other sources are useful, but successful deep learning models require data collected from your production line in order to build a relevant knowledge base.
4. Trial & Evaluation
After constructing the visual inspection model, the following step is to train it. Data scientists validate and analyze the model’s performance and accuracy at this step. Test datasets are useful tools at this stage as the team will need to form and validate several hypotheticals before system will be up and running. This can be a group of video records from a visual inspection system that are either obsolete or similar to the ones that will be processed after deployment.
5. Deploy & Improve
It’s critical to think about how software and hardware system architectures correspond to model capabilities when deploying a visual inspection model.
Hardware
This is a list of devices required to implement a visual inspection system, but this may vary depending on the industry and automation processes:
- Cameras – Real-time video streaming is the most important feature.
- CPU & GPU: If the visual inspection system must provide real-time results, a GPU rather than a CPU will offer option, as the former has a quicker processing speed for image-based deep learning models. A CPU can be optimized for running the visual inspection model but cannot be optimized for training.
- Colorimeters [Optional] – Imaging colorimeters have consistently high spatial resolutions for detecting color and brightness in light sources, allowing for precise visual inspections.
- UAVs (Unmanned Aerial Vehicles) [optional] – Building interiors, gas pipelines, tanker visual inspection, and rocket/shuttle inspection are all examples of automated examination of hard-to-reach regions that could benefit from drones. Drones could be fitted with high-resolution cameras that can spot defects in real time.
- Thermal imaging camera (optional) – It is a good idea to have thermographic camera data in case of automated inspection of steam/water pipelines and facilities. Thermographic camera data is extremely useful for detecting heat, steam, and water leaks. Thermal camera data can also be used to inspect heat insulation.
Software
The structure of software that uses visual inspection is based on a combination of web-based data transfer and a Python framework for neural network processing.
The storage of data is the most important factor here. Data can be stored in three ways: on a local server, in a cloud streaming service, or in a serverless architecture.
The storage of video records it a vital piece of the visual inspection system. The functionality of a deep learning model often influences the choice of a data storage method. If a visual inspection system employs a large dataset, a cloud streaming service can be the best option.
Develop Your AI Visual Inspection System with Stefanini
Developing smart manufacturing capabilities requires a careful examination of the existing components and features that make a production line successful. While there may be existing programs that provide the services you need, often Visual inspection models may require a Customized Solution. Our Smart Manufacturing solutions provide manufacturers with a means of designing a visual inspection system that meets the unique needs of your factory.
Our team of experts will examine your processes and use their knowledge to find the technology that meets the unique concerns of any production line. Ready to get started? Contact us today to speak with an expert!
