Quality prediction in injection molding

Nagorny, Pierre; Pillet, Maurice; Pairel, Éric; Goff, Ronan Le; Loureaux, Jerome; Wali, Marlene; Kiener, Patrice

doi:10.1109/civemsa.2017.7995316

Cited by 28 publications

(15 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many more researchers proposed ANN-based models of the injection molding process for a subsequent optimization of the product warpage [17][18][19][20][21][22], mechanical properties [23][24][25], or even a combination of several quality parameters together in a single model [26][27][28]. Each of the above described research works refer to an explicitly generated database, introducing an iterative data generation process and therefore costs into the optimization.…”

Section: Artificial Neural Network In Injection Moldingmentioning

confidence: 99%

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

Lockner

Hopmann

2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

show abstract

Section: Artificial Neural Network In Injection Moldingmentioning

confidence: 99%

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

Lockner

Hopmann

2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…The author compared linear and kernel SVM classifiers in datasets corresponding to product faults in an industrial environment with a plastic injection molding machine. Another study used images of injection-molding products and applied deep learning algorithms [32]. The study found that long short-term memory (LSTM) fitted better than convolutional neural network (CNN) models in defect classification problems using image data.…”

Section: Machine Learningmentioning

confidence: 99%

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

et al. 2021

View full text Add to dashboard Cite

With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

show abstract

“…Earlier work on raw signal data processing has shown good results using LSTM (Long Short Term Memory) networks (Nagorny et al 2017) and CNN (Convolutional Neural Networks) (Ordóñez and Roggen 2016).…”

Section: Deep Learningmentioning

confidence: 99%

An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

Rønsch

Külahçı

Dybdahl

2021

International Journal of Production Research

View full text Add to dashboard Cite

This work focuses on the effective utilisation of varying data sources in injection moulding for process improvement through a close collaboration with an industrial partner. The aim is to improve productivity in an injection moulding process consisting of more than 100 injection moulding machines. It has been identified that predicting quality through Machine Process Data is the key to increase productivity by reducing scrap. The scope of this work is to investigate whether a sufficient prediction accuracy (less than 10% of the specification spread) can be achieved by using readily available Machine Process Data or additional sensor signals obtained at a higher cost are needed. The latter comprises Machine Profile and Cavity Profile Data. One of the conclusions is that the available Machine Process Data does not capture the variation in the raw material that impacts element quality and therefore fails to meet the required prediction accuracy. Utilising Machine Profiles or Cavity Profiles have shown similar results in reducing the prediction error. Since the cost of implementing cavity sensors in the entire production is higher than utilising the Machine Profiles, further exploration around improving the utilisation of Machine Profile Data in a setting where process variation and labelled data are limited is proposed.

show abstract

Quality prediction in injection molding

Cited by 28 publications

References 20 publications

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

Contact Info

Product

Resources

About