An abductive neural network approach to the design of runner dimensions for the minimization of wrappage in injection mouldings

Yen, Chiaming; Lin, Jui-Chang; Li, Wu-Jeng; Huang, Mingyue

doi:10.1016/j.jmatprotec.2005.02.153

Cited by 20 publications

(4 citation statements)

References 13 publications

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“…If the temperature distribution inside the cavity is not uniform, it will increase the possibility of warpage and residual stress for the injected parts. Yen et al [8] used finite element and neural networks to control the warpage of plastic parts by changing the diameter and length of the runner. Hassan et al [9] proposed a mold cooling system as another factor that effects the warpage of molded parts, and they pointed out that the position of the cooling system leads to changes in the shrinkage rate and the temperature of the final product.…”

Section: Wpływ Konstrukcji Formy I Parametrów Procesu Wtryskiwania Na Odkształcenia Cienkościennych Elementów Z Tworzyw Polimerowychmentioning

confidence: 99%

Influence of mold design and injection parameters on warpage deformation of thin-walled plastic parts

Wang

Cai

2021

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Thin-walled plastic parts are susceptible to deformation during injection molding. Using the example of a notebook battery cover, optimization of the injection mold design and injection process parameters was performed with Moldflow software, which resulted in about 69% reduction of the deformations. Moreover, the uneven material shrinkage during the injection process has been shown to be the main cause of deformations of thin-walled plastic parts.

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Section: Wpływ Konstrukcji Formy I Parametrów Procesu Wtryskiwania Na Odkształcenia Cienkościennych Elementów Z Tworzyw Polimerowychmentioning

confidence: 99%

Influence of mold design and injection parameters on warpage deformation of thin-walled plastic parts

Wang

Cai

2021

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“…A huge temperature difference will lead to uneven cooling on the surface of the mold and easily cause the warpage of the plastic parts. (15)(16)(17)(18)(19) Therefore, in this experiment, the indexes used to select the best cooling loop scheme are as follows: the shortest time to reach the ejection temperature, the smallest difference between the maximum and minimum surface temperatures of the parts after cooling, and the lowest maximum temperature of the pipe wall of the loop. The relevant measurement parameters of the three cooling loop schemes are shown in Table 6.…”

Section: Design Of Ultrasonic-assisted Mold Cooling System Based On Mmentioning

confidence: 99%

Design of Ultrasonic-assisted Microinjection Mold and Cavity Pressure Measurement System

Lin¹,

Yin²,

Chen³

et al. 2019

Sensors and Materials

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On the basis of the characteristics of thin-wall injection molding technology and the principle of ultrasonic vibration, an ultrasonic-assistant system consisting of an ultrasonic generator, an ultrasonic horn, and an ultrasonic transducer is developed in this study. A simulation analysis of the injection molding of thin-wall microinjection parts is conducted using MOLDFLOW to optimize the gate, runner, primary runner, and cooling system of the mold. A cavity pressure measurement system is built for ultrasonic-assisted microinjection molding process pressure detection, the optimization of process parameters, and the improvement of product quality. The development and processing of the ultrasonic-assistant thin-wall microinjection mold are also completed by combining the ultrasonic system, thin-wall microinjection mold, and cavity pressure measurement system.

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“…Saravanan et al (2003) proposed a GA and SA-based method to optimise machining parameters of turning cylindrical stock in a presence of a single response. Yen et al (2006) designed ANN and SA-based method to perform multiresponse optimisation in injection moulding. Yang et al (2009) used ANN and SA to optimise dual-response electric discharge machining process.…”

Section: Multiresponse Optimisation Based On Simulated Annealingmentioning

confidence: 99%

Review of Multiresponse Process Optimisation Methods

Šibalija

Majstorovic

2015

Advanced Multiresponse Process Optimisation

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The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

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An abductive neural network approach to the design of runner dimensions for the minimization of wrappage in injection mouldings

Cited by 20 publications

References 13 publications

Influence of mold design and injection parameters on warpage deformation of thin-walled plastic parts

Influence of mold design and injection parameters on warpage deformation of thin-walled plastic parts

Design of Ultrasonic-assisted Microinjection Mold and Cavity Pressure Measurement System

Review of Multiresponse Process Optimisation Methods

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