Transfer Learning Applied to Characteristic Prediction of Injection Molded Products

Huang, Yan-Mao; Jong, Wen-Ren; Chen, Shia‐Chung

doi:10.3390/polym13223874

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…These six process variables are widely recognized as representative factors in injection-molding processes. They are commonly applied as process conditions in actual injection-molding analyses or experiments and are considered key process variables in various studies for optimizing injection-molding processes [3,4,6,8,9,[11][12][13][15][16][17][18]. The melt temperature and mold temperature ranges for the injection-molding experiments were set from 200 • C to 240 • C at three levels, as shown in Table 4, based on the recommended conditions provided by the resin manufacturer (LG Chem, Seoul, Republic of Korea) and using material data from Autodesk Moldflow Insight 2023 (45.1.117), an injection-molding analysis software.…”

Section: Experimental Conditionsmentioning

confidence: 99%

A Study on the Architecture of Artificial Neural Network Considering Injection-Molding Process Steps

Lee,

Kim,

Kim

2023

Polymers

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In this study, an artificial neural network (ANN) was established to predict product properties (mass, diameter, height) using six process conditions of the injection-molding process (melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time) as input parameters. The injection-molding process consists of continuous sequential stages, including the injection stage, packing stage, and cooling stage. However, the related research tends to have an insufficient incorporation of structural characteristics based on these basic process stages. Therefore, in order to incorporate these process stages and characteristics into the ANN, a process-based multi-task learning technique was applied to the connection between the input parameters and the front-end of the hidden layer. This resulted in the construction of two network structures, and their performance was evaluated by comparing them with the typical network structure. The results showed that a multi-task learning architecture that incorporated process-level specific structures in the connections between the input parameters and the front end of the hidden layer yielded relatively better root mean square errors (RMSEs) values than a conventional neural network architecture, by as much as two orders of magnitude. Based on these results, this study has provided guidance for the construction of artificial neural networks for injection-molding processes that incorporates process-stage specific features and structures in the architecture.

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Section: Experimental Conditionsmentioning

confidence: 99%

A Study on the Architecture of Artificial Neural Network Considering Injection-Molding Process Steps

Lee,

Kim,

Kim

2023

Polymers

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“…Tercan et al (2022) designed a two-layer MLP for quality inspection of injection parts, combing TL and continual learning to prevent catastrophic forgetting. Huang et al (2021) trained a backpropagation NN via TL from a computer-aided engineering environment to practical conditions, demonstrating that TL in quality inspection requires only half the data set with maintaining the same inspection accuracy as training from scratch.…”

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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“…The results show an improvement in model prediction accuracy ranging from 2% to 14.2%. Huang et al [20] mentioned that while CAE analysis helps address some issues affecting injection-molded products, on-site adjustments of parameters still rely on human expertise. Therefore, they proposed a Back Propagation Neural Network (BPNN) network model and used CAE simulation data as the training data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Short-Shot Defects in Injection Molding by Transfer Learning

Zhou,

Yang,

et al. 2023

Applied Sciences

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For a long time, the traditional injection molding industry has faced challenges in improving production efficiency and product quality. With advancements in Computer-Aided Engineering (CAE) technology, many factors that could lead to product defects have been eliminated, reducing the costs associated with trial runs during the manufacturing process. However, despite the progress made in CAE simulation results, there still exists a slight deviation from actual conditions. Therefore, relying solely on CAE simulations cannot entirely prevent product defects, and businesses still need to implement real-time quality checks during the production process. In this study, we developed a Back Propagation Neural Network (BPNN) model to predict the occurrence of short-shots defects in the injection molding process using various process states as inputs. We developed a Back Propagation Neural Network (BPNN) model that takes injection molding process states as input to predict the occurrence of short-shot defects during the injection molding process. Additionally, we investigated the effectiveness of two different transfer learning methods. The first method involved training the neural network model using CAE simulation data for products with length–thickness ratios (LT) of 60 and then applying transfer learning with real process data. The second method trained the neural network model using real process data for products with LT60 and then applied transfer learning with real process data from products with LT100. From the results, we have inferred that transfer learning, as compared to conventional neural network training methods, can prevent overfitting with the same amount of training data. The short-shot prediction models trained using transfer learning achieved accuracies of 90.2% and 94.4% on the validation datasets of products with LT60 and LT100, respectively. Through integration with the injection molding machine, this enables production personnel to determine whether a product will experience a short-shot before the mold is opened, thereby increasing troubleshooting time.

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Transfer Learning Applied to Characteristic Prediction of Injection Molded Products

Cited by 12 publications

References 22 publications

A Study on the Architecture of Artificial Neural Network Considering Injection-Molding Process Steps

A Study on the Architecture of Artificial Neural Network Considering Injection-Molding Process Steps

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

Prediction of Short-Shot Defects in Injection Molding by Transfer Learning

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