Mould data management in plastic injection mould industries

Low, Maria L. H.; Lee, K. S.

doi:10.1080/00207540701305522

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…Currently, an abundant amount of durable consumer products such as medical, electronic, and automobile plastic parts are produced in large quantities utilizing injection molding [1]. Before a newly designed polymeric part is mass-produced by injection molding, prototypes are required to assess the visual appearance, functionality, and mechanical performance of the new product.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Sustainability of the Hybrid Mold Technique through Combined Insert Polymeric Material and Additive Manufacturing Method Design

et al. 2022

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To reduce plastic waste generation from failed product batches during industrial injection molding, the sustainable production of representative prototypes is essential. Interesting is the more recent hybrid injection molding (HM) technique, in which a polymeric mold core and cavity are produced via additive manufacturing (AM) and are both placed in an overall metal housing for the final polymeric part production. HM requires less material waste and energy compared to conventional subtractive injection molding, at least if its process parameters are properly tuned. In the present work, several options of AM insert production are compared with full metal/steel mold inserts, selecting isotactic polypropylene as the injected polymer. These options are defined by both the AM method and the material considered and are evaluated with respect to the insert mechanical and conductive properties, also considering Moldex3D simulations. These simulations are conducted with inputted measured temperature-dependent AM material properties to identify in silico indicators for wear and to perform cooling cycle time minimization. It is shown that PolyJetted Digital acrylonitrile-butadiene-styrene (ABS) polymer and Multi jet fusioned (MJF) polyamide 11 (PA11) are the most promising. The former option has the best durability for thinner injection molded parts, and the latter option the best cooling cycle times at any thickness, highlighting the need to further develop AM options.

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Section: Introductionmentioning

confidence: 99%

Increasing the Sustainability of the Hybrid Mold Technique through Combined Insert Polymeric Material and Additive Manufacturing Method Design

et al. 2022

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“…As the injection molding industry supplies the base product for other manufacturing industries, it is considered a "root" industry, and the industry size is growing. With its importance growing every year, many studies have focused on designing the manufacturing process of injection molding and how to efficiently test the molding [7]. While the literature on methods to effectively improve the manufacturing process of injection molding is growing, relatively little attention has been given to how to employ modern techniques, such as machine learning or deep learning models, to predict the quality of injection molding products.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…More than 1/3 of all thermoplastic materials are injection moulded and more than half of all polymer processing equipments are for injection moulding [1]. Nowadays, injection moulding bears the responsibility of massproducing plastic components to meet the rapidly rising market demand as a multitude of different types of consumer products including medical, electronics, and automobile products are made of injection-moulded plastic parts [2]. Moreover, the final products, which exhibit good dimensional accuracy and excellent surface finish, have further proven the value of injection moulding process.…”

Section: Introductionmentioning

confidence: 99%

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

Fei

Mehat²,

Kamaruddin

2013

ISRN Industrial Engineering

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Determining the optimal processing parameter is routinely performed in the plastic injection moulding industry as it has a direct and dramatic influence on product quality and costs. In this volatile and fiercely competitive market, traditional trial-and-error is no longer sufficient to meet the challenges of globalization. This paper aims to review the research of the practical use of Taguchi method in the optimization of processing parameters for injection moulding. Taguchi method has been employed with great success in experimental designs for problems with multiple parameters due to its practicality and robustness. However, it is realized that there is no single technique that appears to be superior in solving different kinds of problem. Improvements are to be expected by integrating the practical use of the Taguchi method into other optimization approaches to enhance the efficiency of the optimization process. The review will shed light on the standalone Taguchi method and integration of Taguchi method with various approaches including numerical simulation, grey relational analysis (GRA), principal component analysis (PCA), artificial neural network (ANN), and genetic algorithm (GA). All the features, advantages, and connection of the Taguchi-based optimization approaches are discussed.

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Mould data management in plastic injection mould industries

Cited by 8 publications

References 42 publications

Increasing the Sustainability of the Hybrid Mold Technique through Combined Insert Polymeric Material and Additive Manufacturing Method Design

Increasing the Sustainability of the Hybrid Mold Technique through Combined Insert Polymeric Material and Additive Manufacturing Method Design

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

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

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