Computer Vision-Based Defect Identification for One of the Leading Steel Manufacturer

The Challenge

Steelmaking is an inherently energy-intensive process. Defects in steel products can lead to significant waste, higher energy consumption, and increased greenhouse gas emissions, all of which impact the environmental and economic efficiency of the manufacturing process. One of the leading steel manufacturers faced specific challenges in its steel production:

  • Detection of Surface Defects: Identifying various types of surface defects and assessing their severity was challenging, especially in a high-speed production line.
  • Early Prediction of Defects: Predicting potential defects before they occur would allow for timely intervention and correction, thus reducing waste.
  • Root Cause Analysis: Understanding the contributing factors to defects, including the quality of raw materials and production processes, was essential to improving overall product quality.

The Objective

This steel manufacturer aimed to leverage advanced technologies to enhance the quality control of their steel products, reduce waste, and optimize energy consumption. The primary goals included:

  • Implementing real-time defect detection and alert mechanisms.
  • Predicting and preventing defects early in the manufacturing process.
  • Gaining actionable insights into the quality of raw materials and key production processes to enable continuous improvement.

Key Value to Business

The Solution

DAIPL provided company with a comprehensive Computer Vision-based Edge Application powered by AI. The solution was designed to address the specific needs of company’s manufacturing operations through the following components:

  • Real-Time Defect Tracking: The edge application enabled continuous monitoring and identification of surface defects on steel products. The system employed AI-driven algorithms to classify defects, determine their severity, and alert operators in real time.
  • Predictive Analytics: Using historical and real-time data, the AI model could predict potential defects early in the production process. This allowed the client’s team to intervene proactively, preventing the formation of defects that could lead to scrap.
  • Root Cause Analysis: By analyzing the correlation between defects, raw material quality, and process parameters, the system provided valuable insights. This helped identify the root causes of defects and enabled process optimizations to reduce their occurrence.

The Impact

The Computer Vision-based defect identification solution provided measurable improvements to steel manufacturer’s production process and quality assurance efforts:

  • Real-Time Defect Tracking: The system’s real-time alert mechanism enabled immediate action on detected defects, reducing downtime and minimizing waste.
  • Enhanced Process Insights: The insights generated from defect data and root cause analysis empowered company to improve the quality of raw materials and refine production processes, ultimately leading to better product quality.
  • Reduction in Scrap and Energy Waste: By detecting and addressing defects early, the company was able to decrease the amount of scrap generated, leading to lower energy consumption and a more environmentally sustainable production process.

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