Huawei > Case Studies > 3C Product Online Appearance Quality Inspection

3C Product Online Appearance Quality Inspection

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 3C Product Online Appearance Quality Inspection - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Computer Vision Software
  • Analytics & Modeling - Machine Learning
Applicable Industries
  • Electronics
Applicable Functions
  • Discrete Manufacturing
  • Quality Assurance
Use Cases
  • Computer Vision
  • Visual Quality Detection
  • System Integration
The Challenge

For a 3C product supply chain enterprise, the monthly labour cost of manual visual inspection exceeds 2 million, the proportion of quality inspection personnel accounts for 20%~40%, and there are quality problems such as missed inspections. At present, manual quality inspection is faced with the problems of quality, cost, response to special scenarios, and information integration. The details are as follows:

  • Quality: The subjective factor of manual quality inspection has a great influence on the judgment result, and there will be a problem of missed inspection based on visual fatigue; Production status of a 3C product supply chain enterprise.
  • Information integration: There is no effective accumulation and utilization of production data, and subsequent process reengineering and quality analysis cannot be promoted, and the adaptation to automated production processes is weak.
  • Cost: The turnover of personnel is high, resulting in high training and labour costs, and it is difficult to recruit workers.
  • Efficiency: Traditional machine vision uses programmed computing logic for visual inspection, which has poor anti-interference ability for such complex surface inspection, high false detection, and heavy workload for personnel re-judgment.
The Solution

The 5G+AI appearance quality inspection scheme, with the integration of AI depth visual inspection technology and 5G communication technology.

Operational Impact
  • [Process Optimization - Real Time Monitoring]

    Greatly reduce missed detection and false detection in the case of complex texture image classification and background interference, and the defect valve is precisely controllable and synchronized.

  • [Data Management - Data Integration]

    Real-time synchronization of test result data enables efficient and fast iteration of the data model to continuously improves the accuracy of the field model.

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