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AI-Powered Visual Inspection System for Manufacturing Quality Control

osedea.com

Manufacturing

Aerospace

Medical

Defense

Transportation

Manual Inspection Inefficiencies and Quality Concerns

Promark Electronics currently relies on manual visual inspection processes, which are time-consuming (several minutes per part) and prone to human error. This leads to slow throughput, increased labor costs, and potential inconsistencies in product quality. The need for component traceability and adherence to strict quality standards further exacerbates these challenges.

About the Client

A leading global supplier of wire harnesses, electrical distribution systems, and engineered components for critical industries.

Project Objectives

Reduce visual inspection time by at least 75%.
Improve the accuracy and consistency of quality control.
Enhance component traceability through timestamped images and data logging.
Implement a scalable AI-driven inspection system capable of handling increasing production volumes and diverse components.
Reduce operational costs associated with manual inspection.
Minimize false positives and ensure efficient processing of acceptable parts.

Functional Requirements

Automated defect detection using computer vision and AI.
Real-time analysis of video streams from inspection stations.
User-friendly interface for creating and managing inspection points (Wizard).
Validation interface for final quality inspectors (Inspector).
Client administration panel for managing inspection requests and user roles.
System administration panel for model management and overall system control.
Timestamped image capture and storage for traceability.
AI model training and continuous learning capabilities.

Preferred Technologies

Python

Machine Learning

Computer Vision

Serverless Architecture

Google Cloud Platform (GCP)

MLOps

CI/CD

Integrations Required

Existing ERP system (potential for data synchronization)
Database systems for storing inspection data and images

Key Non-Functional Requirements

Scalability to support a growing number of inspection points and AI models.
High performance and low latency for real-time inspection.
Data security and privacy in compliance with relevant regulations.
Cost-effectiveness compared to traditional solutions.
Robustness and reliability to ensure continuous operation.

Estimated Business Impact

The implementation of this AI-powered visual inspection system is expected to significantly reduce inspection time, improve product quality, lower labor costs, and enhance operational efficiency. Increased traceability will improve product reliability and reduce potential warranty claims. The scalable architecture will enable Promark to adapt to future growth and evolving quality standards.