Injection molding process modeling using back propagation neural network method

Salamoni, Thenny Daus; Wahjudi, Arif

doi:10.1063/1.5046266

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The selection of the structure, activation function, and training function of the BPNN network used in this study has been discussed and proposed in previous studies, namely using a 4-9-9-2 network architecture consisting of 4 input layers, 2 hidden layers with 9 neurons, and 2 neurons in the output layer. The activation function used is "tansig" and the training function is "trainrp" [14].…”

Section: Backpropagation Neural Network (Bpnn) Methodsmentioning

confidence: 99%

Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method

Wahjudi

Salamoni²,

Batan

et al. 2021

JMES Int. Journal of Mech. Eng. Sc.

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The polymer matrix composite (PMC) in use today is generally made of synthetic fibers which are expensive and not environmentally friendly. The use of synthetic fibers can be replaced with natural fibers, which are more environmentally friendly at a lower price. The natural fiber material used in this study is made from husks, with a particle size of 500 µm (mesh 35). In the PMC manufacturing process, rice husks are mixed with polypropylene (PP) and maleic anhydride polypropylene (MAPP) with a composition of 10 wt% RH, 85 wt% PP and 5 wt% MAPP. PMC materials using natural fibers are called biocomposite materials. The result of mixing PMC with natural fibers in the form of pellets is then carried out by the injection process using an injection molding machine. The printed results are in the form of tensile test specimens based on ASTM D 638-03 type V testing standards and impact test specimens based on ASTM D 256-04 testing standards. The research was conducted by optimizing the responses i.e. tensile strength and impact strength of the biocomposite material in the injection molding machine process, whereas varied process parameters, namely barrel temperature, injection pressure, holding pressure, injection velocity were selected as process parameters. The backpropagation neural network (BPNN) training method is used to recognize the pattern of the relationship between process parameters and response parameters based on the previous experiment, while the genetic algorithm (GA) optimization method is to determine the variation settings for process parameters that can optimize tensile and impact strength. The results of the BPNN training have a 4-9-9-2 network architecture consisting of 4 input layers, 2 hidden layers with 9 neurons, and 2 neurons in the output layer. Optimization with GA produces a combination of variable process parameters barrel temperature 217•C, injection pressure 55 Bar, holding pressure 41 Bar and injection velocity 65 mm/sec. The results of statistical validation using one sample T test show that the average value of tensile strength and impact strength from the results of the confirmation experiment is the same as the value of the tensile strength and impact strength of the optimization prediction.

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Section: Backpropagation Neural Network (Bpnn) Methodsmentioning

confidence: 99%

Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method

Wahjudi

Salamoni²,

Batan

et al. 2021

JMES Int. Journal of Mech. Eng. Sc.

Self Cite

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show abstract

“…Some attempt has also been made to predict short shot [38] or weld lines [39,40], taking as input parameter gate location. Other research has been conducted to develop prediction models related to material parameters such as mechanical properties [41][42][43][44], fiber orientation [45], or even the selection of the thermoplastic material itself by transfer learning [46]. Regarding output related to process conditions, less research is found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Predictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks

Fernández,

Clavería,

Pina

et al. 2023

Polymers

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The use of recycled polypropylene in industry to reduce environmental impact is increasing. Design for manufacturing and process simulation is a key stage in the development of plastic parts. Traditionally, a trial-and-error methodology is followed to eliminate uncertainties regarding geometry and process. A new proposal is presented, combining simulation with the design of experiments and creating prediction models for seven different process and part quality output features. These models are used to optimize the design without developing additional time-consuming simulations. The study aims to compare the precision and correlation of these models. The methods used are linear regression and artificial neural network (ANN) fitting. A wide range of eight injection parameters and geometry variations are used as inputs. The predictability of nonlinear behavior and compensatory effects due to the complex relationships between this wide set of parameter combinations is analyzed further in the state of the art. Results show that only Back Propagation Neural Networks (BPNN) are suitable for correlating all quality features in a single formula. The use of prediction models accelerates the optimization of part design, applying multiple criteria to support decision-making. The methodology is applied to the design of a plastic support for induction hobs. Furthermore, this methodology has demonstrated that a weight reduction of 27% is feasible. However, it is necessary to combine process parameters that differ from the standard ones with a non-uniform thickness distribution so that the remaining injection parameters, material properties, and dimensions fall within tolerances.

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Numerical Study on Dynamic Stabilities Prediction in a Small Unmanned Aerial Vehicle

Wahjudi

Susanto

2022

Lecture Notes in Mechanical Engineering

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Injection molding process modeling using back propagation neural network method

Cited by 3 publications

References 7 publications

Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method

Determination of Injection Molding Process Parameters using Combination of Backpropagation Neural Network and Genetic Algorithm Optimization Method

Predictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks

Numerical Study on Dynamic Stabilities Prediction in a Small Unmanned Aerial Vehicle

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