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Edge-Based Computer Vision System for Real-Time Defect Detection in PVC Pipe Manufacturing

sphereinc.com

Manufacturing

Construction

Building Materials

Challenges with Quality Control and Production Efficiency

The client faces significant challenges with quality control in their PVC pipe manufacturing process. Manual inspection is slow and prone to error, leading to increased product waste due to surface cracks, uneven cuts, and diameter variations. These defects result in product returns, rework costs, and inefficiencies in machine calibration. The inability to quickly identify and address defects negatively impacts customer satisfaction and overall operational profitability.

About the Client

A regional manufacturer specializing in producing a diverse range of PVC pipes for various construction and infrastructure applications, including drainage, water supply, and conduit systems.

Project Goals

Automate quality control processes to reduce manual inspection effort.
Minimize product waste by detecting defects in real-time.
Improve product quality and consistency.
Reduce production costs associated with rework and returns.
Enable data-driven maintenance of manufacturing equipment.

System Functionality

Real-time defect detection using computer vision.
Automated defect classification and severity scoring.
Real-time alerts and visualizations on a dashboard.
Data logging of defect events (timestamp, type, severity, batch number).
Integration with existing manufacturing systems (e.g., production line control).
Ability to train and update defect detection models.

Technology Stack

NVIDIA Jetson Orin Edge AI device

OpenCV (Python)

TensorFlow Lite

PostgreSQL

Grafana

Docker

Industrial Cameras

System Integrations

Production Line Control System
Manufacturing Execution System (MES)
Database Systems (existing)

Non-Functional Requirements

Real-time processing capabilities with minimal latency.
High accuracy in defect detection.
Scalability to handle increasing production volume.
Robustness and reliability in industrial environments.
Secure data storage and access control.
Ease of deployment and maintenance.

Expected Business Value

The implementation of this edge-based computer vision system is expected to significantly improve the client's operational efficiency and profitability. By automating quality control, the client anticipates a reduction of over 2x in defect occurrences, resulting in an estimated annual material waste reduction of $45,000. The real-time data and insights will enable proactive machine maintenance, leading to reduced downtime and improved production yield. Furthermore, the improved product quality will enhance customer satisfaction and reduce warranty claims.