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

Computer Vision

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


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


  • 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.


  • 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
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