Prediction of sink depths using nonlinear modeling of injection molding variables

Mathivanan, D.; Parthasarathy, N. S.

doi:10.1007/s00170-008-1749-1

Cited by 43 publications

(18 citation statements)

References 3 publications

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“…This result in line with Ozcelik et al [16] which found that the melt temperature and packing pressure have the greatest effect on the weld line and the tensile strength of the moulded parts. This statement also agreed by Mathiavanan & Parthasaraty [17] which reported that melt temperature and packing pressure is the most influence on the strength of the moulded parts. In addition, Gou et al [18] also reported that melt temperature has a significant effect on strength of the moulded parts.…”

Section: Resultssupporting

confidence: 89%

Analysis of Strength on Thick Plate Part using Genetic Algorithm Optimisation Method

et al. 2016

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Abstract. This study focuses on the optimisation of the injection moulding parameters to maximise the strength ofmoulded parts using a simulation software. The moulded parts were injected with AcrylonitrileButadiene-Styrene (ABS) whereas mould temperature, melt temperature, packing pressure and packing time were selected as variable process parameters. The polynomial model obtained using Design of Experiment (DOE) was integrated with the Response Surface Methodology (RSM) and Centre Composite Design (CCD). The RSM was supported with Genetic Algorithm (GA) to anticipate the optimum value of processing parameters with the highest strength. It was found that strength of the parts can be improved 2.2% using the methodology reported herein.

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Section: Resultssupporting

confidence: 89%

Analysis of Strength on Thick Plate Part using Genetic Algorithm Optimisation Method

et al. 2016

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“…The fractional factorial design, for example, has been applied as an initial screening approach to determine which process variables are important to control relevant PMs in IM (Mathivanan, & Parthasarathy, 2008; Kramschuster, Cavitt, Ermer, Chen, & Turng, 2005; Zheng, Wu, Xia, & Chen, 2005), as well as in several other manufacturing processes (Santilli, Puente, & Tanco, 2011; Kim, Son, Yang, & Yaragada, 2003). To the same end, the orthogonal array design has been used by the practitioners of Taguchi methods in IM (Ozcelik, & Erzurulumlu, 2006; Chen, Chuang, Hsiao, Yang, & Tsai, 2009; Kuo, & Su, 2007; Chen, Fu, Tai, & Deng, 2009; Barzegari, & Rodrigue, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The effect of different processing conditions on shrinkage, warpage, cycle time and weight of the part has been reviewed in (Ozcelik & Erzurumlu, 2006; Chen, Chuang, Hsiao, Yang & Tsai, 2009; Chen W.C, Lai, Fu, & Chen C.T., 2008; Kuo & Su, 2007; Chen, Fu, Tai, Deng, 2009; Zhou, Zhao, & Li, 2009; Chang, Hioe, Villarreal-Marroquín, & Castro,2009; Mathivanan, & Parthasarathy, 2008). These works primarily consider a single criterion for process improvement.…”

Section: Introductionmentioning

confidence: 99%

Simulation-based process windows simultaneously considering two and three conflicting criteria in injection molding

Rodríguez-Yáñez

Mendez-Vazquez

Cabrera-Ríos

2014

Production & Manufacturing Research

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Process windows in injection molding are habitually built with only one performance measure in mind. In reality, a more realistic picture can be obtained when considering multiple performance measures at a time, especially in the presence of conflict. In this work, the construction of process windows for injection molding (IM) is undertaken considering two and three performance measures in conflict simultaneously. The best compromises between the criteria involved are identified through the direct application of the concept of Pareto-dominance in multiple criteria optimization. The aim is to provide a formal and realistic strategy to set processing conditions in IM operations. The resulting optimization approach is easily implementable in MS Excel. The solutions are presented graphically to facilitate their use in manufacturing plants.

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“…In many cases, the outcome of the injection molding process is difficult to predict and the definition of the most suitable parameters is obtained empirically. Nowadays, computer-aided engineering (CAE) software can be used to assist the plastics industry, to reduce the costs and the cycle time and improving the product quality [1,2] by assisting in two of the production phases: i) Simulating the plastic product: In CAE, defects in the plastic parts, such as, welding lines and shrinkage, can be simulated [3] . CAE can also be used to detect critical regions with heat accumulation, sink marks, residual stress built-up and product warpage [4,5] .…”

Section: Introductionmentioning

confidence: 99%

Influences of the mesh in the CAE simulation for plastic injection molding

et al. 2019

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Although computer-aided engineering (CAE) software has been used for many years in the plastic industry, identifying the most appropriate mesh geometry and density remains a challenge. It can affect the accuracy of the simulation, the time and the costs. The evaluation of the most suitable mesh is not easy because the difficulties to obtain the real the values of the pressure and temperature inside the mold. The current work investigates this issue. A mold was manufactured and sensors were installed in its interior. CAE simulations using different mesh geometries and densities were evaluated against the experimental data. The results showed that the computational time was mostly influenced by the mesh geometry. The use of 2D mesh and lower density can lead to a faster and more precise simulation of pressure inside the mold and 3D mesh with lower density can provide a faster and precise simulation of the temperature.

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Prediction of sink depths using nonlinear modeling of injection molding variables

Cited by 43 publications

References 3 publications

Analysis of Strength on Thick Plate Part using Genetic Algorithm Optimisation Method

Analysis of Strength on Thick Plate Part using Genetic Algorithm Optimisation Method

Simulation-based process windows simultaneously considering two and three conflicting criteria in injection molding

Influences of the mesh in the CAE simulation for plastic injection molding

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