Keywords
Injection molding
Surface defect detection
Online inspection
Convolutional neural network
Quality control
Submitted : 2026-03-04
Accepted : 2026-03-19
Published : 2026-04-03
Abstract
The injection molding process underpins modern mass manufacturing, yet surface defects like sink marks and flash cause quality issues, material waste and production delays. Traditional manual inspection is labor-intensive, costly and inconsistent, unfit for automated lines. This paper presents an online machine vision detection system for such defects, integrating high-resolution cameras and LED lighting to capture in-line images. Its pipeline includes preprocessing, hybrid feature extraction with traditional analysis and a CNN model, and real-time analysis via PLC for defect flagging and ejection. Trials on polymer components show 99.2% recognition accuracy, 0.5% false positive rate and 180 parts/min processing speed, meeting cycle demands and boosting smart manufacturing quality control with lower operational costs.
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