Simulation Process of Injection Molding and Optimization for Automobile Instrument Parameter in Embedded System

Ramesh, S.; Nirmala, P; Ramkumar, G.; Sahoo, Satyajeet; Gnanasekar, A. K.; Lalvani, J. Isaac JoshuaRamesh

doi:10.1155/2021/9720297

Cited by 45 publications

(3 citation statements)

References 15 publications

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“…ey used MATLAB to build a function to solve the optimization problem and finally obtain the optimal combination of process parameters. Ramesh et al [9] used matrix design and parameter group algorithms to investigate the variety combination of parameters for the automotive instrumentation injection molding process and verified this using processing experiments.…”

Section: Introductionmentioning

confidence: 99%

Research on the Injection Mold Design and Molding Process Parameter Optimization of a Car Door Inner Panel

Yang

Wang

2022

Advances in Materials Science and Engineering

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Numerical simulation of the injection molding of the inner panel of the car door and the optimization of its process parameters were completed. The inner panel and its injection mold were designed by UG software and simulated by the filling and cooling module of the Moldflow software. Different gate schemes were selected to compare quality indicators such as filling time, air pockets, and weld lines to obtain the optimal number of gates. We established the objective function model with gate position as the independent variable, and used a multi-population genetic algorithm to solve the optimal position of the function model to get the best gate position. The mold temperature, melt temperature, cooling time, holding pressure, and holding time were selected as the influencing factors, and volume shrinkage and warpage deformation were selected as the evaluation indicators to design and complete the orthogonal test. The test data were simulated by Moldflow, and the optimal combination of process parameters was determined by range and variance analysis. The BP neural network model related to the molding process parameters, volume shrinkage, and warpage deformation was built, and the trained network model was optimized with the ant colony algorithm. The optimal parameter combination was: mold temperature 76 ° C , melt temperature 205 ° C , cooling time 23.8s, holding pressure 54.7Mpa, and holding time 22.1s. The simulation results showed that volume shrinkage was 13.32% and warpage deformation was 4.315 mm. The design of an injection mold for the car door inner panel was completed and its molding process parameters were optimized.

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Section: Introductionmentioning

confidence: 99%

Research on the Injection Mold Design and Molding Process Parameter Optimization of a Car Door Inner Panel

Yang

Wang

2022

Advances in Materials Science and Engineering

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“…(Mukras, 2020) employed the kriging technique, an optimization approach to minimize the product cycle time generated by volumetric shrinkage and warpage of injection molded products using an experimentalbased optimization approach. (Ramesh et al, 2021) employed Taguchi orthogonal parameter design and particle swarm optimization to measure the shrinkage volume and warpage amount rate for automobile molded parts. (Azdast & Hasanzadeh, 2019) comprehensively investigated the shrinkage behavior of polycarbonate parts using response surface methodology in different conditions based on the design of experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of experimental design techniques for modeling volumetric shrinkage in injection molding experiment using artificial intelligence

Abiodun

2022

Preprint

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The study examined two types of design of experiments (DoE) methods for injection molding of a molded part. It evaluated them using an artificial neural network (ANN) and a support vector machine (SVM) via cross-validation and holdout validation. The innovative goal is to identify the most efficient and successful ways for modeling varied DoE. The influence of four processing parameters on the volumetric shrinkage of a thin polystyrene plate sample is simulated using factorial design and orthogonal Taguchi arrays design. As measured by root mean square error (RMSE), the prediction performance revealed that DoE with eight experimental points as in \({2}^{4-1}\) for fractional factorial design and L8 for orthogonal Taguchi design is particularly efficient for this modeling simulation problem. Both design methods are beneficial and efficient because orthogonal Taguchi arrays play an essential role when the accuracy of fractional factorial designs is insufficient.

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“…Ramesh et al, [3] demonstrated the simulation of injection moulding development and the optimization of vehicle instrument characteristics using various optimization approaches. The shrinkage volume rate and warpage quantity rate are measured using Taguchi Orthogonal parameter design and particle swarm optimization (PSO) in this paper.…”

Section: Introductionmentioning

confidence: 99%

The Comparison of Medical Grade PP with Common Grade PP in Injection Moulding Process

Zaki

Rusdi

Xiao

et al. 2022

CFDL

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Polypropylene (PP) is used to make a syringe and its tray, and there are two varieties of PP: medical grade (LB6331) and common grade (TP340). Although these two materials are basically similar, their characteristics differ significantly and have an impact on overall performance when made using the injection moulding process. The traditional trial-and-error method for optimising PP syringe and tray manufacture is often costly and time-consuming. This research is being done to create a numerical study using ANSYS Fluent on finding the best material to mass-produce the syringe and its tray to come up with a solution to lower the cost and time it takes to make the much-needed virus-fighting equipment. The objectives of this study are to compare the two types of PP's viscosity during injection moulding with ANSYS Fluent, to determine any incomplete fillings of both types of PP viscosity during injection moulding in the mould with ANSYS Fluent, and to compare the time taken between the two types of PP to fill up the mould casing. In the simulation, the temperature for both materials are set as 190 °C, and the parameter inserted beforehand are followed using the cross-parameter table, which contains zero shear viscosity, power-law index, and time constant for the temperature. A constant gauge pressure of 38.22 MPa is inserted as the inlet boundary condition for the simulation, along with the temperature of 190 °C. The results revealed that the time taken for the molten LB6331 to be fully injected into the mould is 1.5 seconds while for molten TP340 is 4 seconds. The viscosity of LB6331 at 2500 s-1 is approximately 220 Pa.s while the viscosity of TP340 is approximately 350 Pa.s. Both PP are shear thinning because their viscosity decreases with increasing shear rate. The VOF for both simulation of LB6331 and TP340 showed that there are imperfections at the edge of the mould casing because that part still contains air.

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Simulation Process of Injection Molding and Optimization for Automobile Instrument Parameter in Embedded System

Cited by 45 publications

References 15 publications

Research on the Injection Mold Design and Molding Process Parameter Optimization of a Car Door Inner Panel

Research on the Injection Mold Design and Molding Process Parameter Optimization of a Car Door Inner Panel

Optimization of experimental design techniques for modeling volumetric shrinkage in injection molding experiment using artificial intelligence

The Comparison of Medical Grade PP with Common Grade PP in Injection Moulding Process

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