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5G+AI: Production Process Bottleneck Analysis

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 5G+AI: Production Process Bottleneck Analysis - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Computer Vision Software
Applicable Industries
  • Automotive
  • Electronics
Applicable Functions
  • Discrete Manufacturing
Use Cases
  • Computer Vision
  • Hardware Design & Engineering Services
The Challenge

In the product assembly operation involving people in the factory production line, the general actions usually include fixed operation procedures such as: reaching out, grasping, moving objects, assembling. After the operator works for a long time or is disturbed, the assembly action will be omitted and it might cause misoperation. It's also necessary to identify the problems existing in the action through action analysis so that the sequence and method of the action can be improved.

The Solution

Based on IPC industrial cameras, through the video behaviour recognition detection method, it can automatically identify whether the procedures in the manual assembly process are correct, and give real-time alarm prompts for erroneous operations to prevent non-compliant operations and discover and optimize assembly actions. Among them, video-based behaviour recognition refers to extracting salient visual features describing action behaviour patterns from video sequences and then classifying and understanding them through algorithms such as machine learning and pattern recognition to realize the recognition of behaviour patterns of video targets.

Operational Impact
  • [Efficiency Improvement - Production]

    The 5G+AI-based video manual assembly motion detection solution transmits the video back to the cloud platform with powerful computing power through the 5G network and uses the deep learning method to solve the pain points of complex and changeable assembly motions and low detection success rate. At the same time, based on 5G The flexible deployment scheme of the wireless network enables the solution to be extended to more industrial applications.

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