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Development of Computer Vision-Based Motherboard Defect Analysis System

n-ix.com

Manufacturing

Telecommunications

Medical Technology

Business Challenges in Manual Motherboard Analysis

The client faces inefficiencies in their manual laptop motherboard troubleshooting process, which requires significant human effort, lacks standardized defect identification, and results in prolonged repair times. Current methods fail to quickly determine root causes of defects or leverage thermal data for enhanced diagnostics.

About the Client

Global provider of technology repair and maintenance services with a focus on telecom and medtech industries

Project Goals for Automated Defect Detection

Reduce average troubleshooting time for laptop motherboards from 30 to under 10 minutes
Improve repair process cost-effectiveness through automation
Develop accurate defect identification across 2,000+ motherboard models
Integrate thermal imaging analysis with visual defect detection

Core System Capabilities

Automated motherboard model identification (2,000+ models)
Defect detection from operator-taken photos
Root cause analysis with 3 probable failure sources
Thermal imaging comparison with SVG component templates
Real-time defect visualization interface

Technology Stack Requirements

Computer Vision (CV)

Neural Networks

KMeans Clustering

Flask

Docker

Catboost

Keras

TensorFlow

System Integration Needs

Thermal camera API
Existing repair workflow management system
Defect database

Performance & Scalability Expectations

Support for 2,000+ motherboard models
Real-time processing (<2s response time)
99.9% defect detection accuracy
Data security compliance

Expected Business Impact of Automated Analysis

Implementation of the computer vision solution is projected to reduce manual labor requirements by 60%, decrease repair costs through error reduction, and enable scalable processing of motherboard diagnostics across global operations while maintaining high defect detection accuracy.