Case Study: AI-Driven Quality Control in Automotive Parts Manufacturing

Case Study: AI-Driven Quality Control in Automotive Parts Manufacturing
Client:
A leading automotive parts manufacturer headquartered in the United States, our client has over 30 years of industry experience and supplies high-quality components to major car manufacturers globally. Known for its commitment to innovation, the company holds several patents in materials science and engineering processes. Additionally, the client is ISO-certified and has received numerous awards for its dedication to quality and sustainability.
Business Problem:
- The client faced challenges in maintaining consistent quality across its range of automotive parts.
- Manual inspection was not only time-consuming but also prone to errors.
- The client aimed to reduce the size of its quality control team and automate the inspection process.
- Due to recent labor shortage , client faced challenges in hiring qualified QA engineers to work onsite
Solution:
- We developed an AI-powered solution using Azure Computer Vision and Azure Machine Learning to automate the quality control process.
- Around 200 images for each type of defect commonly found in automotive parts were used to train the AI model.
- The AI model analyzes live feeds from high-resolution cameras installed on the manufacturing line to detect and localize defects.
- The model can identify and classify up to 12 different types of defects with an accuracy rate of over 95%.
- Azure IoT Hub was used to collect data from the manufacturing line and integrate it with the AI model for real-time analysis.
- Power BI was utilized to create dashboards for monitoring the performance of the quality control system.
Outcome:
- The AI-driven solution has enabled the client to fully automate its quality control process.
- The client was able to reduce the size of its quality control team by 50%, resulting in significant operational cost savings.
- The automated process has improved the overall quality of the automotive parts, leading to higher customer satisfaction and fewer returns.
Technology Used:
- Azure Computer Vision
- Azure Machine Learning
- Azure IoT Hub
- Power BI
- .NET Core for RESTful APIs