Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network

Sandu, Ionut Laurentiu; Susac, Florin; Stan, Felicia; Fetecău, Cătălin

doi:10.37358/mp.20.3.5394

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Aside from physical injection moulding, published articles also focused on creation of credible simulation which could simulate the injection moulding process as close to the reality as possible. Publications [20][21][22][23] attempted to create a simulation in numerous softwares. There was also the work of Lucchetta et al [24], who focused on the influence of various coatings of the mould on melt flow in thin-walled products.…”

Section: Introductionmentioning

confidence: 99%

The influence of tool’s surface topography on mechanical properties of injection moulded product

Ovsík

Staněk

Dockal

et al. 2022

Surf. Topogr.: Metrol. Prop.

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This publication deals with influence of tool topography (injection mould) on properties of a product. Surface of the mould was machined by various finishing technologies (milling, grinding, polishing and electrical discharge machining) which resulted in varying surface quality of the tool. The tested topography had an effect on the flow length of polymer and topographical and mechanical properties of the specimen. Examined properties (surface topography and mechanical properties) were measured in several places along the length of the product (starting at the gate and finishing at the end of the specimen). The results show that increase of the tool’s surface roughness leads to longer flow length. These findings disprove the necessity for polishing of each and every shaping part of the mould when manufacturing non-visual products. Thus, from economical and manufacturing perspective the milled or grinded tool surfaces are sufficient. Furthermore, replication of the tool’s topography is non-homogenous, which results in varying mechanical properties throughout the product. The discrepancy in mechanical properties along the length of the product is caused by differing cooling speeds in the mould. In practice, guided cooling can be used to achieve varying mechanical properties in desired places of the injected article. For example, highly stressed parts can be manufactured with the goal of having improved mechanical properties in specific places of the product. Future application of these findings poses a significant benefit for industrial practice, as it could lower the manufacturing cost of the injection mould in order of tens of percent.

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Section: Introductionmentioning

confidence: 99%

The influence of tool’s surface topography on mechanical properties of injection moulded product

Ovsík

Staněk

Dockal

et al. 2022

Surf. Topogr.: Metrol. Prop.

View full text Add to dashboard Cite

show abstract

A method combining optimization algorithm and inverse-deformation design for improving the injection quality of box-shaped parts

Zhai,

Li,

Xiong

et al. 2023

Int J Adv Manuf Technol

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Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

Akiner,

Kartal,

Guzeler

et al. 2024

Earth Sci Inform

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The Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

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Prediction of Polymer Flow Length by Coupling Finite Element Simulation with Artificial Neural Network

Cited by 3 publications

References 25 publications

The influence of tool’s surface topography on mechanical properties of injection moulded product

The influence of tool’s surface topography on mechanical properties of injection moulded product

A method combining optimization algorithm and inverse-deformation design for improving the injection quality of box-shaped parts

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

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