Detection method of filling imbalance in a multi-cavity mold for small lens

Gim, Jinsu; Tae, Joon-Sung; Jeon, Joohyeong; Choi, Jae-Hyuk; Rhee, Byong-Duk

doi:10.1007/s12541-015-0072-7

Cited by 20 publications

(10 citation statements)

References 5 publications

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“…Temperature is one of the two dominant factors that affect product consistency (the other one is pressure). And in the work reported by Gim et al [3], temperature shows a stronger correlation. Therefore, improving the stability of temperature control is very important to improve product quality consistency.…”

Section: Introductionmentioning

confidence: 81%

Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control

Ruan

Huang

2019

Advances in Polymer Technology

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Temperature stability is critical to the consistency of product quality in the injection molding process, and it is very necessary to improve the temperature control accuracy under dynamic conditions. However, due to the large time delay, strong coupling, and the dynamic characteristics existing in the system, it is not an easy task to achieve precise temperature control in the injection molding process. In this paper, a new intelligent temperature compensation control strategy for the injection molding process under dynamic conditions is proposed in order to solve two key problems in the compensation control strategy: the compensation time and compensation quantity. A data-based feedforward iterative learning control (ILC) algorithm is designed to learn the optimal compensation time. Once the optimal compensation time is learned, a deep Q-learning algorithm which combined Q-learning with an artificial neural network (ANN) is proposed to learn the optimal compensation quantity. An experimental platform is designed to validate the superiority of the proposed method. Experimental results show that the proposed method can effectively improve temperature control accuracy under dynamic conditions. Meanwhile, the product consistency has also been improved.

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Section: Introductionmentioning

confidence: 81%

Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control

Ruan

Huang

2019

Advances in Polymer Technology

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“…In recent studies, researchers have put forward some alternative methods for cavity pressure measurements in order to maintain the integrity of the part and mold. Gim et al [10] installed indirect pressure sensors under the lens Figure 2: (a) Conventional sensors used in online sensing of injection molding process [9]. (b) Illustration of an indirect pressure sensor [10].…”

Section: Conventional Methodsmentioning

confidence: 99%

“…Gim et al [10] installed indirect pressure sensors under the lens Figure 2: (a) Conventional sensors used in online sensing of injection molding process [9]. (b) Illustration of an indirect pressure sensor [10]. (c) e injection molding machine with respect to position and pressure sensors equipped [11].…”

Section: Conventional Methodsmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

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“…Recently, the filling imbalance has been investigated with advanced techniques. Gim et al [14] used a mold equipped with thermocouples and in-direct pressure sensors, and they observed that the filling imbalance may be inverse. Li et al [15] studied the phenomenon by direct in-mold observations using a mold with a sight glass.…”

Section: Introductionmentioning

confidence: 99%

A Strategy for Problem Solving of Filling Imbalance in Geometrically Balanced Injection Molds

Wilczyński¹,

Narowski²

2020

Polymers

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Simulation and experimental studies were performed on filling imbalance in geometrically balanced injection molds. An original strategy for problem solving was developed to optimize the imbalance phenomenon. The phenomenon was studied both by simulation and experimentation using several different runner systems at various thermo-rheological material parameters and process operating conditions. Three optimization procedures were applied, Response Surface Methodology (RSM), Taguchi method, and Artificial Neural Networks (ANN). Operating process parameters: the injection rate, melt temperature, and mold temperature, as well as the geometry of the runner system were optimized. The imbalance of mold filling as well as the process parameters: the injection pressure, injection time, and molding temperature were optimization criteria. It was concluded that all the optimization procedures improved filling imbalance. However, the Artificial Neural Networks approach seems to be the most efficient optimization procedure, and the Brain Construction Algorithm (BSM) is proposed for problem solving of the imbalance phenomenon.

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Detection method of filling imbalance in a multi-cavity mold for small lens

Cited by 20 publications

References 5 publications

Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control

Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control

Intelligent Injection Molding on Sensing, Optimization, and Control

A Strategy for Problem Solving of Filling Imbalance in Geometrically Balanced Injection Molds

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