Thin Shell Plastic Parts Research Articles

Hybrid mesh generation for the thin shell of thin-shell plastic parts for mold flow analysis

Hybrid mesh generation for the thin shell of thin-shell plastic parts for mold flow analysis

Automatic recognition and decomposition of rib features in thin-shell parts for mold flow analysis

In mold flow analysis, for greater accuracy, convergence, and application specificity, it is beneficial to apply a hybrid combination of pyramidal, prismatic, and hexahedral meshes to replace traditional tetrahedral meshes. This, however, requires the recognition of specific features from the computer-aided design (CAD) model and decomposition into regions, so that high-quality meshes can be applied. The purpose of this study is to propose a rib recognition and decomposition algorithm for detecting all the types of ribs on thin-shell plastic parts, and decomposing them into rib regions. Conventional rib recognition methods emphasizing only the recognition of the constructing faces are not suitable for high-quality mesh generation, because they cannot provide sufficient information for region decomposition. We employ a concept of parallel pairs of edges to identify regions of regular shape on a rib structure. This not only recognizes the constructing faces, but also evaluates the constructing edges on each regular region. With the edge data on regular regions, the transition regions are recognized and the associated face and edge data are evaluated. This study focuses on the development of a feasible algorithm that can be used to deal with industrial CAD models with various kinds of geometric conditions. Several CAD examples are presented to demonstrate the feasibility of the proposed algorithm.

Recognition and decomposition of rib features in thin-shell plastic parts for finite element analysis

Feature recognition has been extensively studied for applications in computer-aided design (CAD), computer-aided manufacturing, and computer-aided process planning. However, its application in fini...

Study on Injection Molding Parameters for Thin-Shell Plastic Parts Using a Neural Network–Based Approach

This study analyzed variation of warpage and tensile properties depending on injection molding parameters during production of thin-shell plastic components. A hybrid method integrating back-propagation neural network (BPNN), genetic algorithm (GA), and simulated annealing algorithm (SAA) are proposed to determine an optimal parameter setting of the injection-molding process. The results of 18 experimental runs were utilized to train the BPNN predicting warpage and tensile properties at various injection-molding conditions and then the GA and SAA approaches were applied to individual search for an optimal setting. The results show that the combinations of BPNN/GA and BPNN/SAA methods are effective tool for the optimization of injection molding parameter.

Modeling and Optimization of Injection Molding Process Parameters for Thin-Shell Plastic Parts

This paper deals with the application response surface methodology (RSM) integrating with statistical technique to discuss variation of warpage and tensile stress properties depended on injection molding parameters during production of thin-shell plastic components. By applying RSM analysis, a mathematical predictive model of the warpage and tensile stress properties was developed in terms of the injection molding parameters. The trim operation has been optimized for a given injection molding condition by desirability function approach and the response surface contours were constructed for determining the optimum conditions. Additionally, the analysis of variance is also applied to identify the most significant factor.

Simulation Study on Optimization of Injection Molding Process for Thin-shell Plastic Parts via the Taguchi Method and Grey Relational Analysis

Abstract This paper deals with the application computer-aided engineering integrated with statistical techniques to reduce warpage variation due to injection molding process parameters during the production of thin-shell plastic components. For this purpose, Moldflow simulation runs are carried out by utilizing the combination of process parameters based on a three-level L18 orthogonal array table. An optimal parameter combination of the injection molding process is obtained via grey relational analysis (GRA). By analyzing the grey relational grade matrix, the degree of influence for each controllable process factor onto warpage can be found. Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor; the melt temperature and the packing pressure are found to be the most significant factors in the simulation process for an injection molding process of thin-shell plastic parts.

Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis

This paper deals with the application computer-aided engineering integrating with statistical technique to reduce warpage variation depended on injection molding process parameters during production of thin-shell plastic components. For this purpose, a number of Mold-Flow analyses are carried out by utilizing the combination of process parameters based on three level of L 18 orthogonal array table. In the meantime, apply the design of experiments (DOE) approach to determine an optimal parameter setting. In addition, a side-by-side comparison of two different approaches of simulation and experimental is provided. In this study, regression models that link the controlled parameters and the targeted outputs are developed, and the identified models can be utilized to predict the warpage at various injection molding conditions. The melt temperature and the packing pressure are found to be the most significant factors in both the simulation and the experimental for an injection molding process of thin-shell plastic parts.

Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm

During the production of thin shell plastic parts by injection molding, warpage depending on the process conditions is often encountered. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element (FE) analysis, statistical design of experiment method, response surface methodology (RSM), and genetic algorithm (GA) is investigated. A bus ceiling lamp base is considered as a thin shell plastic part example. To achieve the minimum warpage, optimum process condition parameters are determined. Mold temperature, melt temperature, packing pressure, packing time, and cooling time are considered as process condition parameters. FE analyses are conducted for a combination of process parameters organized using statistical three-level full factorial experimental design. The most important process parameters influencing warpage are determined using FE analysis results based on analysis of variance (ANOVA) method. A predictive response surface model for warpage data is created using RSM. The response surface (RS) model is interfaced with an effective GA to find the optimum process parameter values.

Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm

Decreasing warpage is a very significant topic to improve the quality of injection-molded parts. Dimensional stability is an important factor for the minimum warpage of thin shell plastic part. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element analysis, statistical design of experiment method, response surface methodology and genetic algorithm is investigated. A thin shell plastic part model is considered as an example. To achieve the minimum warpage, optimum process condition dimensional parameters are determined. X dimension, Y dimension, and Z dimension are considered as model variables. Another parameters of effecting minimum warpage are taken into consideration as constant, such as mold temperature, melt temperature, injection time, injection pressure, etc. Finite element analyses are conducted for combination of process parameters organized using statistical three-level full factorial experimental design. A predictive model for warpage is created using response surface methodology exploiting finite element analysis results. Response surface model is interfaced with an effective genetic algorithm to find the optimum process parameter values.