MES-Connected Vision Inspection via Cloud for SME Defect Detection
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Solution Overview
Problem
Small and medium-sized companies face challenges in implementing smart factory technologies due to high costs and complexity in constructing machine vision defect detection systems, which are expensive and difficult to integrate with existing manufacturing processes, especially for those without extensive resources or production lines.
Innovation Solution
A manufacturing intelligence service system connected to a Manufacturing Execution System (MES) that utilizes a cloud server to provide product ID and defect information from a machine vision inspection system, allowing for real-time defect detection and data sharing across multiple manufacturing companies, leveraging deep learning algorithms and existing machine vision equipment to enhance defect detection accuracy without the need for extensive infrastructure changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine vision inspection system is implemented in small and medium-sized companies, then defect detection capability is improved, but system cost and implementation complexity increase significantly
Solution Approach 1:
The patent introduces a cloud server as an intermediary between the machine vision inspection system and the MES. The cloud server receives inspection data from the vision system, processes it using deep learning algorithms, and transmits results to the MES. This intermediary approach allows small and medium-sized companies to leverage advanced defect detection capabilities without bearing the full complexity of implementing and maintaining sophisticated vision inspection systems locally.
2Measurement precision
If a machine vision inspection system with deep learning algorithms is deployed, then defect detection accuracy is improved, but infrastructure requirements and costs increase
Solution Approach 1:
The system enables self-service through automated deep learning algorithms that continuously improve defect detection accuracy without requiring manual intervention. The cloud-based deep learning model automatically processes inspection data, learns from new defect patterns, and updates detection parameters, allowing the system to maintain high accuracy while reducing the need for specialized infrastructure and expert personnel at the client site.
3Loss of information
If multiple manufacturing companies share defect data through a centralized system, then overall defect detection intelligence is improved, but data security and system integration complexity increase
Solution Approach 1:
The cloud server acts as a secure intermediary that facilitates data sharing between multiple manufacturing companies while maintaining data security and privacy. It implements standardized data exchange protocols and integration interfaces that simplify connectivity to various MES systems, enabling collaborative defect detection intelligence without requiring complex direct integrations between companies' systems.
Data Source
AI summary
A manufacturing intelligence service system connected to an MES in smart factory is provided. The smart factory manufacturing intelligence service system connected to an MES includes a Manufacturing Execution System (MES) having a machine vision of a production line of each manufacturing company to provide the product ID and a product information and a defect information including scratch or defect of a product; a cloud server connected to the at least one Manufacturing Execution System (MES); and an agent server connected to the cloud server, and the cloud server provides the product ID and the product information and product defect information of a connected machine vision production line of a manufacturing company product to the user terminal through the agent server.