Beyond the Human Eye: How AI is Transforming Quality Control
Here’s how AI-powered Quality Control can boost efficiency
Figure 1: AI-powered Quality Control for airplane engine parts
In traditional Quality Control teams, specialists inspect various objects and identify defects. However, this workforce is shrinking as more workers retire, and fewer young people are interested in these roles. Additionally, manual inspection is very prone to human error, with error rates up to 30%.
AI–powered Quality Control can solve both the workforce shortage and human error by training AI models to mimic human inspectors. A well–trained AI can even achieve near-perfect accuracy, much faster than manual labor.
AI-powered Computer Vision goes beyond mere automation. Here is how AI-powered Quality Control differs from traditional automated Quality Control:
AI-powered Computer Vision
Uses Computer Vision expanded with deep learning and advanced algorithms
Adapts to changing environments by learning from the data it processes
Is better at inspecting complex defects with high variety that otherwise require human intelligence
Automation
Relies on simpler logic based on rules and relying on sensors
Requires human intervention to change its parameters and rules
More effective for simpler, easily quantifiable measurements
The process behind AI-powered Quality Control projects
Once you decide to implement AI-powered Quality Control in your company, there are several essential steps in the process that you need to consider, to ensure you will have the best possible solution.
Technical audit
A technical audit is the steppingstone to determine whether AI Vision Quality Control is beneficial for an organization. There are a few ways to do this:
- Conducting a feasibility study with a small dataset and using its preliminary results to assess whether a proposed project can be launched, with some modifications if necessary
- Visiting the client’s site to assess the available equipment, to estimate costs and the duration of the full-scale project
Once the technical audit is complete, a short report is provided that determines whether the project is feasible. With the technical audit’s success, it’s time to provide an extensive proposal and kickstart the project!
Data collection
After a successful technical audit, it’s time to collect data. Depending on the use case, the optimal hardware configuration is determined and set up at the client’s site. The amount of data required for this stage depends on the scope and complexity of the project.
AI model training
While generic AI models exist, they likely won’t meet your specific needs. The ideal solution will be determined by in-house experts. In general, the AI model will likely be based only on data collected on site or consist of a combination of pre-trained models that will be re-trained to suit the client’s specific situation.
Training an AI with diverse data related to your case generally results in the most robust AI model. In order for the AI to prove that it is robust, it will undergo a series of tests to ensure it outperforms humans and that it can be continuously improved.
Reports and dashboards
Once the AI model is ready, the next step is to present the findings in easy-to-understand, actionable insights that the client can easily access and implement.
Based on the client’s needs, the integration process is determined. Does the client prefer an app they can easily access via a tablet? Or maybe they prefer a dashboard integrated in their internal systems? The most suitable option is chosen in collaboration with the client.
Key factors for a successful project
Once you decide to implement AI-powered Quality Control in your company, there are several essential steps in the process that you need to consider, to ensure you will have the best possible solution.
Sufficient data
To train an AI model, a vast amount of data is required. Sometimes, there is simply no method to collect sufficient data for AI model training. But there are a few tricks that can help overcome this issue. One of them is combining AI with more conventional computer vision techniques. Computer vision can aid with AI model training because it can pre-process the data for the AI. For example, with the help of computer vision, the object that needs to be inspected can be singled out so that the AI ‘knows’ what exactly it needs to be trained on.
Collaboration
Collaboration is vital when operating at the bleeding edge of technology. As a multidisciplinary AI and Robotics engineering company, a lot of value can be added if the client gets involved in the process and acts as a collaborator. This is how true innovation can be achieved when working with cutting-edge technologies.
From conducting a technical audit to successfully implementing a solution into the client’s systems and processes, it is essential to keep the overarching goal main purpose in mind: contributing to a more sustainable and efficient future of business with the help of AI and Robotics.
This is what Cboost is all about. For over four years, we have worked with industry leaders across fields such as manufacturing, aviation, agriculture, and more. We delivered countless successful Quality Control projects that improved our client’s processes, reduced the need for manual labor and optimized resources.