A neural network-based approach for dynamic quality prediction in a plastic injection molding process

Chen, Wen‐Chin; Tai, Pei-Hao; Wang, Min-Wen; Deng, Wei‐Jaw; Chen, Chen-Tai

doi:10.1016/j.eswa.2007.07.037

Cited by 136 publications

(45 citation statements)

References 32 publications

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“…Sustainability 2018, 10, 85 2 of 14 neural networks (ANNs) and machine learning (ML) are two typical representatives of AI techniques, and have achieved successful application in manufacturing quality prediction, e.g., self-organizing neural networks [11], back propagation neural networks (BPNNs) [12], radial basis function neural networks [13], probability neural networks [14], support vector machines (SVMs) [15], and extreme learning machines [16]. Affected by multiple parameters from multi-stage manufacturing processes, ANN and ML modeling exhibit feature learning difficulties and network calculation complexities due to their "shallow" architecture, i.e., the model has one hidden layer or none at all (a traditional ANN has one hidden layer and classical ML is based on a kernel function without a hidden layer).…”

Section: Methodologiesmentioning

confidence: 99%

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

et al. 2017

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Abstract:Under the international background of the transformation and promotion of manufacturing, the Chinese government proposed the "Made in China 2025" strategy, which focused on the improvement of a quality-based innovation ability. Moreover, predicting manufacturing quality is one of the crucial measures for quality management. Accurate prediction is closely related to the feature learning of manufacturing processes. Therefore, two categories of intelligent learning approaches, i.e., shallow learning and deep learning, are investigated and compared for manufacturing quality prediction in this paper. Specifically, the feed forward neural network (FFNN) with one hidden layer and the least squares support vector machine (LSSVM) with no hidden layers are selected as the representatives for shallow learning, and the deep restricted Boltzmann machine (DRBM) and the stack autoencoder (SAE) are chosen as the representatives for deep learning. The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China. The experiments show that the deep framework overwhelms the shallow architecture in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. In addition, the prediction results also indicate that the performances depend on the length of the training data. That is, the bigger the sample size is, the better the performance is.

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Section: Methodologiesmentioning

confidence: 99%

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

et al. 2017

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“…In this process, hot molten polymer is forced into a cold empty cavity of a desired shape and is then allowed to solidify under a high holding pressure. The entire injection moulding cycle can be divided into three stages: filling, postfilling and mould-opening [4]. During moulding process, the plastic material undergoes temperature and pressure increases, significant shear deformation, followed by rapid drop of temperature and pressure in the mould cavity, leading to solidification and other properties that determine the characteristics of the moulded part.…”

Section: Introductionmentioning

confidence: 99%

Robust Part Quality by Controlling the Injection Moulding Process with 2<sup>4</sup> Fraction Factorial Design: A Case Study

Rajalingam

2011

AMR

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The author wants to use a case study to investigate the injection moulding machine parameters which will affect the horizontal length dimension of a plastic component used in digital camera. Currently the injection moulding machine process setting caused variations in the diameter exceeding the specification limit. Therefore the experiment is needed to identify the process factors that could be set to maintain the horizontal length dimension closest to the target value and smallest possible variation. The experimental model is used to investigate four factors to identify the factors having large effect by using the Full Factorial Design of Experiment (DOE). The experiment has emphasized the use of these designs in identifying the subset of factors that are active and provide some information about the interaction.

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“…Injection molding as one of the most important production processes getting plastic products has gotten enough attention in recent years [1], [2] . Nowadays, parts like optical grating elements and micro fluidic devices can be shaped in large quantities by Injection molding, yet many defects [3] like gas bubbles, gloss variations on textured surfaces appears in the molded parts under process conditions that fail to satisfy the requirements.…”

Section: Introductionmentioning

confidence: 99%

The research of UV curing injection molding

Xie

Chang

Song

et al. 2015

AIP Conference Proceedings

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A neural network-based approach for dynamic quality prediction in a plastic injection molding process

Cited by 136 publications

References 32 publications

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study

Robust Part Quality by Controlling the Injection Moulding Process with 2<sup>4</sup> Fraction Factorial Design: A Case Study

The research of UV curing injection molding

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