Injection Molding Inspection System Based on Machine Vision

Asadi, Mohammadreza; Hashemi, Seyedeh Sogand; Sadeghi, Mohammad Taghi

doi:10.1109/icrom54204.2021.9663455

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Hu et al (2022) enhanced the VGG16 CNN, achieving a 96.67% accuracy in quality inspection. Asadi et al (2021) used local binary patterns algorithm for feature extraction and used support vector machine for detecting whether the injection part adheres to the mold. Kim et al (2021) combined MLP, gated recurrent unit and CNN through multimodal early fusion, achieving an impressive 99% accuracy in fault detection during subsequent experiments on windshield side molding.…”

Section: Introductionmentioning

confidence: 99%

In-situ quality inspection system of injection parts based on transfer learning

Yang,

Shan,

Jin

et al. 2024

RIA

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Purpose This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology. Design/methodology/approach The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach. Findings Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%. Originality/value The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

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Section: Introductionmentioning

confidence: 99%