A Novel Sensing Feature Extraction Based on Mold Temperature and Melt Pressure for Plastic Injection Molding Quality Assessment

Wang, Zhi-Hao; Wen, Fu-Chi; Li, Yi-Ting; Tsou, Hao-Hsuan

doi:10.1109/jsen.2023.3247597

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…The results demonstrate the feasibility of the constructed network 25 . Wang et al study utilized an ensemble machine learning model which consists of RFs and CNNs achieved a significant reduction in mean absolute error from 6.08 to 2.86 and prediction error rate from 4% to 1.1% 26 . Perera developed a robust data‐driven model, combining deep autoencoder and feedforward NN techniques, achieving accurate predictions of melt pressure 27…”

Section: Introductionmentioning

confidence: 89%

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Multi‐source data‐driven approach for prediction of melt density during polymer compounding

Zhang,

Chen,

Zhang

et al. 2024

Polymer Engineering & Sci

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Melt density is a crucial quality indicator for polymer composites, yet real‐time measurement remains challenging due to processing complexities. While existing machine learning methods offer solutions, they often fall short in complex compounding scenarios. This study presents a novel multi‐source data‐driven approach for measuring melt density in polycarbonate/acrylonitrile butadiene styrene blends. By incorporating ultrasonic, near‐infrared, and Raman spectra data acquired during melt processing, a deep separable convolutional neural network model is developed to predict melt density accurately. The model effectively fuses multi‐source data to establish the mapping relationship between input data and melt density output. Results demonstrate the model's ability to monitor melt density in real‐time, achieving a prediction accuracy with RMSE and R2 indexes of 0.005 g/cm3 and 0.9841, respectively. The proposed approach outperforms existing methods, showcasing its effectiveness and superiority in melt density prediction for polymer compounding processes.Highlights Establishment of the real‐time monitoring system for polymer extrusion processes. Conversion of multi‐sensor signals into time‐frequency images using wavelet decomposition. Fusion of sensor data into a three‐channel tensor‐image. Development of a data‐driven DSCNN model for predicting melt density. Implementation for online monitoring and prediction in PC/ABS compounding system.

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Section: Introductionmentioning

confidence: 89%

“…CNN model 26 is constructed for real‐time monitoring of melt viscosity in the polymer extrusion process. In this study, a CNN model with two convolutional layers is constructed.…”

Section: Methodsmentioning

confidence: 99%

Multi‐source data‐driven approach for prediction of melt density during polymer compounding

Zhang,

Chen,

Zhang

et al. 2024

Polymer Engineering & Sci

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“…Due to the complexity of process parameters, linear models are inadequate for describing them, and currently setting of injection molding process parameters relies heavily on the expert experience of engineers [8] . Machine learning is a data fitting optimization technique that includes various artificial intelligence algorithms such as decision trees [9] , random forests [10] , support vector machines [11,12] , Gaussian processes [13,14] and artificial neural networks [15,16] . These algorithms mainly involve gathering sample data, fitting data models, and an iterative optimization search for data relationship models.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

Ma,

Wang,

Dang

et al. 2023

Int J Adv Manuf Technol

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In this research, a recommendation system was designed for optimizing the injection molding process parameters. The system incorporates the utilization of process windows, XGBoost, and genetic algorithms. CAE simulations were conducted to generate process window data and simulation data. Automatic hyperparameter optimization of the XGBoost was performed using grid search and cross-validation methods. The system employs 5 injection molding feature parameters as input and one product feature as output, and SEGA was used for predicting the optimal injection molding process parameters. The performance of the prediction model was evaluated using an RMSE of 0.0202 and an R 2 of 0.9826. The accuracy of the system was verified by conducting real production. The deviation of the product weight obtained from real production from the desired weight is 0.22%, which means that the prediction model achieves a correct rate of 99.78%. This recommendation system has a significant application value in reducing production costs and cycle time, as it can provide initial injection process parameter suggestions solely through the mold's digital data.

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“…Based on vibration, pressure and image data, Kim and Lee (2023) proposed a 29-layer NN designed for classifying injection parts. Wang et al (2023) adopted random forest classifier and regressor to predict the quality of box-shaped injection parts. They are learning-based indirect methods for quality inspection and requires a lot of training data.…”

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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A Novel Sensing Feature Extraction Based on Mold Temperature and Melt Pressure for Plastic Injection Molding Quality Assessment

Cited by 11 publications

References 28 publications

Multi‐source data‐driven approach for prediction of melt density during polymer compounding

Multi‐source data‐driven approach for prediction of melt density during polymer compounding

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

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

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