Adaptive quality prediction in injection molding based on ensemble learning

Struchtrup, Alexander Schulze; Kvaktun, Dimitri; Schiffers, Reinhard

doi:10.1016/j.procir.2021.03.045

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Struchtrupa et al [23] demonstrated that ensemble models outperform single models in terms of quality prediction for injection molding. They evaluated various single models, such as ANN, SVM, DT, and KNN, and compared them to ensemble models based on DT and gaussian process regression (GPR).…”

Section: Literature Review a ML Studies On Injection Moldingmentioning

confidence: 99%

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Double Ensemble Technique for Improving the Weight Defect Prediction of Injection Molding in Smart Factories

Koo,

Choi,

Yoo

2023

IEEE Access

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The growing move toward smart factories can leverage industrial big data to enhance productivity. In particular, research is being conducted on injection molding and utilizing machine learning techniques to analyze molding process data, discover optimal molding conditions, and predict and improve product quality. This study aims to identify the key factors influencing the weight defects of injection-molded products and demonstrate the potential use of the double ensemble technique for better prediction accuracy of weight defects. We obtain the key factors influencing weight defects prediction, barrel H2 temp real, metering time, and fill time using gain ratio analysis. Subsequently, we develop single models using machine learning algorithms, including decision tree, random forest, logistic regression, the Bayesian network, and the artificial neural network. Ensemble models, including bagging and boosting and double ensemble models are developed to compare their performance with that of single models. The findings indicate that ensemble models outperform the prediction accuracy of the single models. The double ensemble technique demonstrates the greatest improvements in prediction accuracy over the single models. These results showcase the potential of applying the double ensemble technique to other injection molding areas and suggest that adopting this technique will contribute to establishing other smart factories that will enhance both productivity and cost competitiveness.

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Section: Literature Review a ML Studies On Injection Moldingmentioning

confidence: 99%

“…Previous research in injection molding has often involved developing single ML models for quality prediction or process optimization [12][13][14][15][16][17][18][19][20]. However, the current trend is toward utilizing ensemble model and, combining multiple models to enhance overall prediction accuracy [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Double Ensemble Technique for Improving the Weight Defect Prediction of Injection Molding in Smart Factories

Koo,

Choi,

Yoo

2023

IEEE Access

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“…In [15] the author alexander et.al, proposed a comprehensive method that automated the necessary data pre-processing processes for seamless part quality prediction. The selected ensemble hyper parameters have a considerable impact on the group performance.…”

Section: IImentioning

confidence: 99%

Quality and Defect Prediction in Plastic Injection Molding using Machine Learning Algorithms based Gating Systems and Its Mathematical Models

Mehta

Padhi

2023

IJRITCC

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To achieve high quality products from Plastic Injection Molding (PIM) process it is very essential to identify the defective operations in automatic manner which is most challenging task. This paper proposes a Machine Learning (ML) approach to detect the complex faults occurrence during the PIM process. During initial sampling process of molding to achieve high quality and low time consumption it is essential to concentrate on the suitable determination of parameter values by considering the properties of injection molding process. For that purpose, a novel machine learning algorithms based gating system is introduced in PIM (MLGS-PIM). Technical evaluation can be done using simulation which combines the CATIA and MATLAB. Therefore in MLGS-PIM, a holistic approach is introduced to improve and predict the process quality of the parameters which is based on machine learning approaches. The considered machine learning approaches for this process are Artificial Neural Network (ANN) and Support Vector Machine (SVM). This two learning models are combined to achieve high quality under various conditions. Such novel ML based technique helps to increase the quality characteristics of the injection molding process and it is predicted with various parameter values where the simulation data and measurements are handled in an intelligent manner. The materials which are considered in the PIM process are thermoplastic polystyrene, thermoplastic acrylonitrile butadiene styrene and thermoplastic polyvinyl chloride where three types are gating systems are applied with it and consists of 3, 4 and 5 gates and as well the parameters which are measured for the output analysis are sum rate, bit error rate and convergence plot. The results show that the performance of the proposed MLGS-PIM approach significantly increases the performance when compared with the earlier approaches such as AntLion Optimization and PSO-MSQPA.

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“…Still there are influences on the injection molding process, such as variations in the material properties or environmental conditions, which may negatively affect the quality of the molded parts. To address these issues, the research focus over the last few years has moved to improving control techniques [14,18] and analyzing process data [16,17].…”

Section: Case Studymentioning

confidence: 99%

Providing Meaningful Data Summarizations Using Exemplar-based Clustering in Industry 4.0

Honysz,

Schulze-Struchtrup,

Buschjäger

et al. 2021

Preprint

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Data summarizations are a valuable tool to derive knowledge from large data streams and have proven their usefulness in a great number of applications. Summaries can be found by optimizing submodular functions. These functions map subsets of data to real values, which indicate their "representativeness" and which should be maximized to find a diverse summary of the underlying data. In this paper, we studied Exemplar-based clustering as a submodular function and provide a GPU algorithm to cope with its high computational complexity. We show, that our GPU implementation provides speedups of up to 72x using singleprecision and up to 452x using half-precision computation compared to conventional CPU algorithms. We also show, that the GPU algorithm not only provides remarkable runtime benefits with workstation-grade GPUs but also with low-power embedded computation units for which speedups of up to 35x are possible. Furthermore, we apply our algorithm to realworld data from injection molding manufacturing processes and discuss how found summaries help with steering this specific process to cut costs and reduce the manufacturing of bad parts. Beyond pure speedup considerations, we show, that our approach can provide summaries within reasonable time frames for this kind of industrial, real-world data.

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Adaptive quality prediction in injection molding based on ensemble learning

Cited by 8 publications

References 15 publications

Double Ensemble Technique for Improving the Weight Defect Prediction of Injection Molding in Smart Factories

Double Ensemble Technique for Improving the Weight Defect Prediction of Injection Molding in Smart Factories

Quality and Defect Prediction in Plastic Injection Molding using Machine Learning Algorithms based Gating Systems and Its Mathematical Models

Providing Meaningful Data Summarizations Using Exemplar-based Clustering in Industry 4.0

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