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Artificial Intelligence-Based Smart Quality Inspection for Manufacturing.

Sarvesh Sundaram¹, Abe Zeid¹

¹College of Engineering, Northeastern University, Boston, MA 02135, USA.

|March 29, 2023

Summary

This summary is machine-generated.

This study introduces an Artificial Intelligence (AI) approach using Deep Learning (DL) to enhance manufacturing quality control. The developed system achieves 99.86% accuracy in visual inspection, significantly improving upon manual methods.

Keywords:

artificial intelligence deep learning defect detection image recognition industry 4.0 quality control smart manufacturing visual inspection

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Area of Science:

Manufacturing Engineering
Artificial Intelligence
Computer Vision

Background:

Real-time monitoring of manufacturing environments is crucial for preventing downtime and detecting defects.
Traditional manual visual inspection has limitations, including subjectivity and an average accuracy of only 80%.

Purpose of the Study:

To develop an Artificial Intelligence (AI) based system for automated visual inspection in manufacturing.
To improve the accuracy and efficiency of quality control processes beyond manual capabilities.

Main Methods:

Implementation of a custom Convolutional Neural Network (CNN) model for image-based defect detection.
Development of a user-friendly computer application for shop floor deployment.

Main Results:

The proposed AI-based visual inspection system achieved a high accuracy rate of 99.86%.
The system demonstrated effectiveness on image data of casting products.

Conclusions:

The AI-driven approach significantly enhances visual inspection accuracy in manufacturing.
The developed system offers a practical and user-friendly solution for achieving near-perfect quality control.