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Welding QC inspection

 


User Story: AI-Powered Automated Welding Quality Inspection

ID: TMBS-WELD-01 Title: Real-time Welding Defect Identification Priority: High

User Story Statement:

As a Quality Control Engineer at TMBS, I want the AI inspection system to automatically analyze live video feeds from the welding stations, So that I can identify and categorize weld defects instantly and ensure only high-quality modules proceed to the next assembly stage.

Acceptance Criteria (AC):

  • AC 1: The system must accurately classify the welding output into the following categories: burn_through, crack, porosity, undercut, overlap, or good_weld.

  • AC 2: The system must provide real-time inference during the welding process, triggering an alert if a defect is detected.

  • AC 3: If a defect (0 through 4) is identified, the system must log the specific defect type and timestamp to the central tracking database.

  • AC 4: The inspection UI must clearly highlight the detected defect on the video feed to assist the operator in manual verification.

  • AC 5: The system must distinguish a good_weld from the identified defect classes with a minimum defined confidence threshold (e.g., >90%).


User Stories: Our Roadmap

1. The Quality Control (QC) Engineer

"As a QC Engineer at TMBS, I want the AI inspection system to automatically analyze live video feeds from the welding stations so that I can identify and categorize weld defects instantly and ensure only high-quality modules proceed to the next assembly stage."


The Vision: AI-Powered "Quality 4.0"

We are developing a dedicated inspection service that leverages high-speed industrial vision to classify weld quality using a defined taxonomy: {0: "burn_through", 1: "crack", 2: "porosity", 3: "undercut", 4: "overlap", 5: "good_weld"}.

To align with our Agile development process, we have broken down this transformation into clear user-focused objectives. 

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