Infotel, là où vous êtes
Case Studies > Automotive Industry > Quality control improvement using AI efficiency
Quality control improvement using AI efficiency
Large European Automotive Manufacturer | Automotive
Achieving a high defect recognition accuracy solution using deep learning training, based on AI visual system solutions.
| SHARE: |  |
5 MINUTE READ |
AUTOMOTIVE INDUSTRY | AI SOLUTIONS
The Client
For this challenge, our client was one of the UK’s largest car manufacturers, producing over 500,000 automobiles per year and employing 7000 staff members in the UK alone. Famous for their commitment to quality, innovation and reliability, their industry presence demonstrates decades of strong sales and distribution throughout Europe.
The Challenge:
One of our long-standing clients approached us to provide an AI solution for defect detection, prevention, and workflow optimisation.
Thanks to the previous work completed with our client as part of an ongoing, trustworthy supplier relationship, we were able to have an open and productive discussion about the main challenges surrounding digital transformation. By addressing concerns like false positives and external variables, a goal was set to create a stable, scalable and efficient new system.
We identified the need for sufficient test samples to provide meaningful data for deep learning training. This presented its own challenge of working within existing parameters.
Introducing a
point-and-click
solution to reduce
production time
Creation of adaptable,
mimicable software
to deploy on a
factory-wide scale
Introducing a system
focused on automating
defect recognition in
industrial QA
The Response:
We knew the answer to the client’s issue would lie in developing software that supports a reliable and adaptable system.
By taking action to identify and address requirements at the beginning of the process, we also identified opportunities to reduce costs for our client.
This lead to the team taking the following steps:
- Ensuring the software created was adaptable, and suited its industrial environment (factory stations) for QA monitoring and provisioning of test images.
- The AI visual system was built focusing on creation, training, and refinement of defect models.
- The application automation process was tested on a live environment.
- Measures were taken to automate the QA process, allowing detection and the flagging of defects up to 98% accuracy.
- All work was designed so it could be easily reproduced, and deployed on a factory-wide scale.
- By introducing point-and-click automation, we planned to reduce any bottlenecking in images being processed.
After outlining the most common applications of AI within the automotive sector, a well-structured and measurable plan needed to be introduced on a design level, as well as establishing algorithms to enhance the use of power units to build more efficient models.
98%
recognition achieved
70%
integrated data
POC
the estimated timeline
5
reduced for the client
Project Outcome
Infotel UK’s comprehensive audit provided an unbiased assessment of the client’s architecture and AI system, leading to successful adoption and system optimizations. Aligning IT resources with processes improved auditing, reporting, and accountability, while introducing plant-wide QA processes for long-term performance and continuity.
Starting with an in-depth health-check, we identified areas to optimize their existing architecture.
The software was created to:
- Adapt to different environments and station sensors
- Encompass a rage of defect types
- Apply alternative deep learning models
- Enable future potential for anomaly detection and optical character recognition
Our AI and deep machine learning expertise enabled the client to seamlessly transition to an automated defect recognition system, improving efficiency and accuracy in industrial QA.
Infotel UK are invaluable to us.
They’re a reliable source who consistently deliver. A huge part of this project was having an outcome that was easy to reproduce and deploy on a factory-wide scale. The quality and the delivery of their work has never been in question, and I wouldn’t hesitate to recommend working with the team.
Head of Insight
Looking for expert advice on your digital transformation project?
Start a conversation with our Automotive Consultants today.
