Shrinkage and warpage prediction of injection‐molded thin‐wall parts using artificial neural networks

Liao, S. J.; Hsieh, Wen‐Hsin; Wang, Tsung-Juang; Su, Y. C.

doi:10.1002/pen.20206

Cited by 33 publications

(18 citation statements)

References 15 publications

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“…Severe warpage can occur due to asymmetric residual stress distribution in the FIM part generated by the non-uniform temperature distribution. Many studies have been carried out to investigate the effect of processing conditions on the warpage of injection molded parts without any inserted film [6][7][8][9][10][11][12][13][14][15][16][17]. Huang and Tai [18] investigated the effective parameters in the warpage problem of an injection molded part.…”

Section: Introductionmentioning

confidence: 99%

Effect of processing conditions on warpage of film insert molded parts

et al. 2008

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In order to investigate effects of injection molding conditions on viscoelastic behavior and thermal deformation of film insert molded (FIM) parts, injection molding was performed with various conditions such as injection speed, melt temperature, and packing time. It was shown that variation of the warpage was decreased monotonically with increasing injection speed and exhibited a bell-shaped curve as a function of melt temperature. Warpage variation was not affected by the packing time significantly and the proportional relationship between warpage of the film insert molded part and shrinkage of the injection molded part without film was observed. The FIM specimens produced with unannealed films showed the warpage reversal phenomenon (WRP) during annealing and the magnitude of reversed warpage was affected significantly by the injection parameters and the extent of thermal shrinkage of the unannealed film. Warpage of the FIM specimen was predicted by three dimensional numerical flow and stress analyses and the predicted values showed a good agreement with the experimental results.

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Section: Introductionmentioning

confidence: 99%

Effect of processing conditions on warpage of film insert molded parts

et al. 2008

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“…However, it is difficult to assure the quality of parts because of the nonuniform material shrinkage throughout the cavity to cooling, letting alone other affecting factors such as packing, mould cooling, constraints of mould geometry, etc. Researchers have applied various kinds of methods, e.g., artificial neural network and/or fuzzy logic [10][11][12][13], genetic algorithm [11,14], design of experiments [8,15], and response surface method [16][17][18] to optimize the initial process parameter setting of plastic injection moulding. However, an optimized initial setting is unable to assure the final part quality because the final part quality is largely affected by the dynamic variables of three levels: machine, process, and quality [19].…”

Section: Plastic Injection Mouldingmentioning

confidence: 99%

Product quality prognosis in plastic injection moulding

Huang

Chen

Cheng

et al. 2010

Prod. Eng. Res. Devel.

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Plastic injection moulding is a typical complex manufacturing process. Its product quality is difficult to assure because of the nonuniform material shrinkage. This research introduces a prognostic concept for predicting the product quality (four edge shrinkages). The prognostic model is developed by path analysis through LISREL which can define the model's reliability when only few sensory data are retrieved from the smoothly running system. The prognostic model is validated by 25 test runs. In each run, each of the nine manufacturing conditions is randomly set at the extreme limit, i.e., either the lower limit or the upper limit. By comparing the actual edge shrinkages defined by the finite element software Moldflow with the results predicted by the prognostic model, this research concludes the prognostic model can successfully predict the quality of products and prevent the production of defective products.

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“…The quality characteristics are evaluated through the S/N ratio obtained in the Taguchi experimental plan. ANOVA then can be used to evaluate the experimental errors and test of significance to understand the effect of various factors [21].…”

Section: Taguchi Methods and Signal To Noise S/n Ratiomentioning

confidence: 99%

A Novel Statistical Analysis for Residual Stress in Injection Molding

Alkaabneh¹,

Barghash²,

Abdullat³

2016

AJOR

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Residual stresses can reduce the reliability of plastic injection molding parts. This work is an attempt to model the residual stresses as a function of injection molding parameters. More stress is placed on reducing the number of input factors and to include all possible interactions. For this purpose, two-stage experimentation is suggested: a factor screening stage and Response Surface optimization stage. In screening stage Taguchi 3 level experimental design is used to classify the input parameters as significant and non-significant factors. Eight input variables were classified into 3 non-significant and 5 significant factors using this screening stage. Thus for the Response Surface optimization stage: instead of doing 160 experiments in Central Composite, 56 are only needed after the screening stage in half Central Composite Design. The best subset and regression model fitting tools in addition to model verification using randomly selected input setting were used to select a model for predicting residual stresses with a verified Root Mean Square Error (RSME) of nearly 0.93 MPa.

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Shrinkage and warpage prediction of injection‐molded thin‐wall parts using artificial neural networks

Cited by 33 publications

References 15 publications

Effect of processing conditions on warpage of film insert molded parts

Effect of processing conditions on warpage of film insert molded parts

Product quality prognosis in plastic injection moulding

A Novel Statistical Analysis for Residual Stress in Injection Molding

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