AI-based Visual Inspection
Increase the efficiency of your quality control with AI-based visual inspection!
What we do
AI-based defect detection or classification is a further development of machine vision inspection solutions. It uses an aspect of machine learning, called deep learning. Just like a human eye, visual inspection AI solutions capture an image and process it. The system learns from examples and understands differentiations of characteristics and anomalies. Due to its repetitive nature, human visual inspection is error-prone. The advantage of an AI-based system is the reliability and high accuracy. Furthermore, these systems can support humans in their decision making and thereby reduce time, costs and security.
SUCCESS STORY
AI-based visual inspection for industrial parts
Our client is an international automotive supplier producing different car components. Manufacturing companies, especially in the automotive suppliers, must meet very high-quality standards. In this use case we improved the efficiency of the automated visual inspection system of metal components at the end of the production line.
Challenge
The client uses a state-of-the-art quality control visual inspection devices for the final inspection of the product at its high volume production line. The system is able to detect quality issues such as missing glue or misalignments. Although the current inspection solution detects issues very well, it also has a high pseudo-scrap rate. Thus, quality inspectors still have to inspect a large number of product manually, which lowers the full efficiency that the system potentially offers.
Solution
Our AI-based visual inspection software uses images from industrial cameras and detects defects on components using a semi-supervised approach, meaning a minimal number of labeled images were required to train the model. It processes images in real-time and classifies between good parts and bad parts. Furthermore, the system can also learn different types of defects to classify even more accurately between types.
Result
The system supports automated quality control and could significantly improve the pseudo-scrap rate to a minimum. This does not only save the quality inspectors time but also makes the production line more efficient and cost-effective.
The Process
Together with craftworks, the automotive supplier defined the specific use case as well as software and hardware requirements. Subsequently, the solution was developed in several phases. First, the feasibility of the project was evaluated by creating an image preprocessing pipeline and we trained a simple model that constituted a baseline for further optimization and development. In the second phase, the model was further developed. The solution uses a semi-supervised learning approach, meaning that in its core it uses an anomaly detection approach to identify abnormalities. Moreover, it uses some human-labeled images to validate the anomalies. After several tests in the production, the model was ready for further optimization and continuous increase of performance and accuracy.
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How you can work with us…
Consultancy
You would like to be advised on the basic setup of an industrial AI approach in your company? You need guidance in how big data, machine learning and artificial intelligence can help you make your processes more efficient? Through tailor made workshops we will help you find the right approach for your company.
Co-Development
You already have a functional team in place, but you are short on manpower or specific experience? Our team is used to working closely with our customers on new solutions and facilitates through knowledge sharing.
Full Service
You are looking for a trusted partner to develop a robust customised solution to your specific needs and requirements? Well, you found us!
… and how we make you independent
Would you like to independently create your own models but need a way to ship machine learning straight to production? With navio you can deploy, manage and monitor machine learning models. Seamlessly integrate with your existing workflow via the API. Simply focus on training machine learning models and navio takes care of the rest with a one-click deployment, saving you time and money.