Huawei > Case Studies > Unified Quality Inspection Standards for Brand Owners and Multiple Factories

Unified Quality Inspection Standards for Brand Owners and Multiple Factories

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 Unified Quality Inspection Standards for Brand Owners and Multiple Factories - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Machine Learning
Applicable Industries
  • Automotive
  • Electronics
Applicable Functions
  • Discrete Manufacturing
  • Logistics & Transportation
  • Quality Assurance
  • Software Design & Engineering Services
The Challenge

In the 3C industry and the automotive industry, the consistency of the measurement standards of upstream parts processing manufacturers and OEMs is important. How to quickly and efficiently sample the quality of parts according to the standards after OEM manufacturers receive parts, digitize the test results in real-time and share with other business systems and make them traceable, are common pain points in the industry.

The Solution

Based on the AI ​​quality inspection platform built by the OEM and the mobile standard inspection AI-Box, through the 5G low-latency and large-bandwidth connection, it will well solve the current quality control problems of small and medium-sized parts.

For example, for an automobile gear hub key dimension measurement and inspection equipment, an OEM has built an AI quality inspection platform, and jointly defined quality standards with component manufacturers, using a combination of deep learning and traditional algorithms, using AI-Box vision combined with 5G terminals testing equipment, AI-Box will send the testing results back to the AI ​​platform and related business systems through the 5G network after testing. By sharing testing data, it achieves high efficiency and high accuracy, and continuously improve the existing AI quality testing model to ensure OEM and upstream component manufacturers share a consistency of testing standards. AI-Box devices support custom configurations of light sources, cameras, and lenses, seamlessly connecting to the industrial vision AI training platform for data collection.

Operational Impact
  • [Data Management - Data Analysis]

    Realize a complete closed-loop of data labelling and model training on the industrial vision AI training cloud platform while supporting model delivery and local confirmatory testing.

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