Injection moulding parameters influence on weight quality of complex parts by means of DOE application: Case study

López, Alicia; Aísa, J.; Martínez, A.; Mercado, Daniel

doi:10.1016/j.measurement.2016.04.072

Cited by 33 publications

(20 citation statements)

References 20 publications

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“…Many process parameters have large effect on product weight, including packing pressure, packing time, injection pressure, injection time, and injection temperature. e effect of these parameters depends on the type of the resin [13] and the structure of the product [14]. According to Yang, Hassan and López [11][12][13][14], the injection temperature and packing pressure are the two parameters which have the most significant influence on product weight.…”

Section: Experimental Designmentioning

confidence: 99%

“…e effect of these parameters depends on the type of the resin [13] and the structure of the product [14]. According to Yang, Hassan and López [11][12][13][14], the injection temperature and packing pressure are the two parameters which have the most significant influence on product weight. On purpose of demonstrating the optimization results more clearly, this study selects only these two parameters as optimization variables.…”

Section: Experimental Designmentioning

confidence: 99%

“…Hassan [13] determined that product weight has a positive correlation with packing pressure. López et al [14] applied the design of experiments (DOE) method to explore the influence of parameters on products with different geometries. Yang and Gao [11] found that the settings for packing pressure, barrel temperature, and mold temperature have the most significant effects on the product weight.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

Zhao

Dong

Zhang

et al. 2020

Advances in Polymer Technology

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Product weight is one of the most important properties for an injection-molded part. The determination of process parameters for obtaining an accurate weight is therefore essential. This study proposed a new optimization strategy for the injection-molding process in which the parameter optimization problem is converted to a weight classification problem. Injection-molded parts are produced under varying parameters and labeled as positive or negative compared with the standard weight, and the weight error of each sample is calculated. A support vector classifier (SVC) method is applied to construct a classification hyperplane in which the weight error is supposed to be zero. A particle swarm optimization (PSO) algorithm contributes to the tuning of the hyperparameters of the SVC model in order to minimize the error between the SVC prediction results and the experimental results. The proposed method is verified to be highly accurate, and its average weight error is 0.0212%. This method only requires a small amount of experiment samples and thus can reduce cost and time. This method has the potential to be widely promoted in the optimization of injection-molding process parameters.

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Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

Zhao

Dong

Zhang

et al. 2020

Advances in Polymer Technology

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“…Design of experiments helps to establish the cause and effect relationship between independent and response variable in an experiment. DOE has been used previously for applications such as optimizing die casting density and reducing porosity [7], studying the influence of injection parameters on weight and part quality [8], investigating relationship between quality of holes drilled and the manufacturing factors in EBM drilling process [9] and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Broaching Process Using Doe Approach

Valyan¹,

Rane²

2018

IEJ

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“…Design of experiment (DOE) methodology is powerful techniques to reduce the number of replicates. Central Composite Design (CCD), Taguchi and Box-Bohen (BBD) design have been widely used in engineering applications (Herrera et al 2015, Jacob and Banerjee 2016, López et al 2016. These techniques are ideal to have an effi cient and effective the experimental design with lower cost.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sanding parameters using response surface methodology

Hazır

Koç²,

Hızıroǧlu

2017

Maderas, Cienc. tecnol.

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The main objective of this work is to develop a mathematical model to evaluate optimum sanding conditions of Europen black pine (Pinus nigra). Samples were sanded using different of grit size, feed rate, cutting speed and depth of cut. Average surface roughness (R a ) values of each type of specimens were measured employing a stylus type of equipment. Interaction between sanding parameters and surface roughness of the species were analyzed using Minitab software and response surface methodology. Based on the fi ndings in the work feed rate, cutting speed, grit size and depth of cut values of 5,39 m/min, 19,75 m/sec, 220 (grit size) and 9 mm were determined as optimum sanding conditions.

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Injection moulding parameters influence on weight quality of complex parts by means of DOE application: Case study

Cited by 33 publications

References 20 publications

Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

Optimization of Injection-Molding Process Parameters for Weight Control: Converting Optimization Problem to Classification Problem

Optimization of Broaching Process Using Doe Approach

Optimization of sanding parameters using response surface methodology

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