Mold temperature control of a rubber injection-molding machine by TSK-type recurrent neural fuzzy network

Juang, Chia‐Feng; Huang, Shing Jong; Duh, Fun-Bin

doi:10.1016/j.neucom.2005.11.003

Cited by 26 publications

(8 citation statements)

References 20 publications

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“…Furthermore, many recent control theories and techniques have been proposed for designing the temperature controllers of such plastic injection molding processes. Those control approaches can be divided into two technical methodologies 29–39; one is controlled typically by single‐loop controllers and one other by multivariable controllers. The proposed control algorithm was shown to be more powerful than the single‐loop control policy 31 and the multivariable control strategies 32, 35, 37 operating at the slow sampling periods of 10, 20, or 60 seconds.…”

Section: Illustrative Examplesmentioning

confidence: 99%

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

Lu¹,

Tsai²,

Liu³

et al. 2010

Asian Journal of Control

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This paper presents a neural‐network‐based predictive control (NPC) method for a class of discrete‐time multi‐input multi‐output (MIMO) systems. A discrete‐time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real‐time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31 from Texas Instruments. Two examples, including the control of a MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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Section: Illustrative Examplesmentioning

confidence: 99%

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

Lu¹,

Tsai²,

Liu³

et al. 2010

Asian Journal of Control

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“…A finite element heat transfer analysis was performed to determine the effects of the heater arrangement on the temperature distribution of the mold. 11 Table 1 lists the properties of the rubber injection mold used in the heat transfer analysis. One-quarter of the symmetrical rubber mold was modeled.…”

Section: Finite Element Analysis and Experiments Of Molding For A Squamentioning

confidence: 99%

“…[7][8][9] Since the temperature of a rubber mold significantly affects product quality, researches have been conducted on temperature maintenance of rubber injection molds. 10,11 In practice, the arrangement of cartridge-type electric heaters has largely relied on the experience of factory workers. Therefore, for the manufacture of high-precision rubber products, research is needed on methods to efficiently control the temperature distribution of rubber molds by proper arrangement of the heaters.…”

Section: Introductionmentioning

confidence: 99%

Optimal locations of heaters in a shoe outsole rubber injection mold using FEA and parametric analysis

Kim

Bae

Han

et al. 2010

Int. J. Precis. Eng. Manuf.

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NOMENCLATURE ℓ = the length of a mold t = the thickness of a mold p = the interval between heaters in a mold d = the distance between heater and surface of a mold Temperature control of a rubber injection mold is important for the dimensional accuracy of the product. In this study, the arrangement of mold heater cartridges was optimized by using finite element analysis (FEA) and experiments. First, a heat transfer analysis was performed about the rubber mold currently used in a factory. The locations of heater cartridges were modified based on the analytic results. A FE analysis was then performed to improve the rubber mold and experiments were conducted to validate the FE analysis. The FE analysis was in good agreement with the experimental results. Design factors were selected for the arrangement of heater cartridges. Based on parametric analysis, we propose an optimal design method for the arrangement of heater cartridges in the rubber injection mold, thereby improving the outsole mold design.

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“…H. M. Sadeghi 4 studied the possibility of predicting the quality or soundness of the injected parts through an ANN model and based on computer-aided engineering software simulations. C. F. Juang et al 5 developed a Takagi-SugenoKang-type recurrent fuzzy neural networks for the control of the mold temperature. C. Karatas et al 6 developed an ANN model to determine the yield length in the plastic molding of commercial plastics.…”

Section: Introductionmentioning

confidence: 99%

Predictive model and optimization of processing parameters for plastic injection moulding

Kramar¹,

Cica²

2017

Mater. Tehnol.

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Injection molding is one of the most widely used processes for producing engineered parts in the plastics industry. The objective of this study is to propose a fuzzy expert system for the prediction of mechanical properties of injection-molded parts where the fuzzy system is optimized using particle-swarm optimization. The input process parameters were the mold temperature, melt temperature, injection velocity, packing pressure, cooling time and packing time. The predicted values were in good agreement with the experimental ones, which indicates that the developed particle-swarm-optimization-based fuzzy expert system can be effectively used to predict the mechanical properties of molded parts. In addition, optimization based on a particle-swarmoptimization algorithm was carried out to obtain the optimum process parameters based on the objective to maximize the tensile strength of the molded product. Keywords: plastics, injection molding, particle-swarm optimization, tensile strength Brizganje je eden izmed najpogosteje uporabljenih postopkov za izdelavo in`enirskih delov v industriji plastike. Cilj te raziskave je predlagati enostavni ekspertni sistem za napovedovanje mehanskih lastnosti delov brizganih kosov, kjer je enostavni sistem optimiziran z uporabo optimizacije z rojem delcev. Vhodni parametri procesa so temperature orodja, temperatura taline, hitrost brizganja, zapiralni pritisk,~as hlajenja in zapiralni~as. Napovedane vrednosti se dobro ujemajo z eksperimentalnimi, kar ka`e, da se razvit enostavni ekspertni sistem na osnovi optimizacije z rojem delcev lahko u~inkovito uporablja za napovedovanje mehanskih lastnosti brizganih delov. Algoritem optimizacije z rojem delcev je podal tudi optimalne procesne parametre za doseganje~im vi{je natezne trdnosti brizganega izdelka. Klju~ne besede: plastika, injekcijsko brizganje, optimizacija z roji delcev, natezna trdnost

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Mold temperature control of a rubber injection-molding machine by TSK-type recurrent neural fuzzy network

Cited by 26 publications

References 20 publications

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process

Optimal locations of heaters in a shoe outsole rubber injection mold using FEA and parametric analysis

Predictive model and optimization of processing parameters for plastic injection moulding

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