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Engine Defect Detection

Manufacturing
case study

Business Impacts

Reduced manual effort

95%

Model accuracy

Streamlined inspection process

Customer Key Facts

  • Location : North America
  • Industry : Automotive

Problem Context

Quality assurance in manufacturing is a critical, yet traditionally manual, cumbersome and time-consuming process. The customer, one of the largest automotive manufacturers in the world, had a quality analysis process which involved a combination of manual and computer-based decisions across the plant.

Manual inspection is necessary due to a high error rate that leads to higher costs, lost productivity, and increased time to production. Therefore, they wanted to empower their Quality Inspectors by augmenting their plants’ capabilities with a machine learning-based model that detects potential defects on a V6 engine casing after the final assembly.

Challenges

 

  • Multi-view point images
  • High imbalance in defective versus non-defective classes
  • Data augmentation through various techniques

Technologies Used

Python

Python

TensorFlow

TensorFlow

Google Cloud Platform

Google Cloud Platform

Implementing a Computer Vision Framework for Detecting Defects in Engines on the Assembly Line

Solution

Quantiphi developed a custom and highly accurate computer vision model that identifies the type of defect and localizes the region of the defect in the assembled engines. The model provides a real-time pass or fail test on defects on the V6 engine casing. An active learning pipeline was also built that allows the Quality Inspectors to label a sample set of data for incremental improvement of the model performance over time. The model retraining is scalable globally and enables plants to share information directly at a station level.

Result

  • Improvement in model accuracy from 50 percent to 95 percent
  • False Positive Rate (FPR) decreased by a factor of 5+ from an unreliable FPR of 50 percent to less than 10 percent
  • Increased accuracy per class (defective/non-defective), now within 99 percent to 99.8 percent

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