The Use of Modern Information Technology in Predicting the Process of Impregnating Composite Preforms with Polymer Resins

Golovatov, Dmitriy A.; Tatarkanov, Aslan A.; Shavaev, Azamat A.; Гусев, С. А.

doi:10.1109/itqmis.2019.8928415

Cited by 6 publications

(3 citation statements)

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“…Each of these factors has a direct impact on the technological parameters of the impregnation process, and lack of their consideration in the simulation leads to a wide disagreement between the results of the simulation and actual values, as shown in works [8,15]. As can be seen from the above, the application of existing software solutions to automate the preproduction engineering of products from composite materials can be ineffective without conducting preliminary experimental studies to determine the full range of characteristics of the moldable materials, as is proven in paper [16].…”

Section: Methodsmentioning

confidence: 99%

Application of neural simulation methods for technological parameters identification of composite products injection molding process

Alexandrov¹,

Maxim²,

Andrey³

2020

J Appl Eng Science

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The article presents the research results of technological parameters of composite material products manufacturing by the injection transfer molding method using automated tools of preproduction engineering. For the verifi cation of computer simulation results, experimental studies have been carried out on the injection-molding machine for products from fi brous polymer composite materials. The conducted comparative analysis of calculated and actual values of impregnation technological parameters has led us to the conclusion that for the effective application of the modern software for preproduction engineering of composite products it may be necessary to make a joint correction of the input data used for the calculation obtained from the preliminary independent experimental research of the complex of properties for each used component of the composite material. For the joint correction of the input data used for the calculation and improvement of the effi ciency of automation systems for preproduction engineering of composite products, the concept of the neural simulation tools application for the technological process of composite products manufacturing by injection molding methods has been proposed. For training and testing of the neural model, experimental studies of the impregnation of the products with the surface curvature of the second order have been conducted. The optimization problem was solved by forecasting the front movement of the technical fl uid in the volume of preforms during transfer molding.

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Section: Methodsmentioning

confidence: 99%

Application of neural simulation methods for technological parameters identification of composite products injection molding process

Alexandrov¹,

Maxim²,

Andrey³

2020

J Appl Eng Science

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show abstract

“…Using the data provided by a set of pressure sensors, Zhu et al 161 implemented a neural network model for the prediction of flow-front patterns at any impregnation time. Similar predictive models were also presented for forecasting resin cure 162 and flow front progression 163 . Stieber et al presented neural network based models FlowFrontNet 164 and PermeabilityNets 165 for the prediction of dry spot formation and permeability maps from a sequence of flow front images respectively.…”

Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 95%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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One of the potential applications of 2D materials is to enhance multi-functionality of structures and components used in aerospace, automotive, civil and defense industries. These multi-functional attributes include sensing, energy storage, EMI shielding and property enhancement. In this article, we have explored the potential of using graphene and its variants as data generating sensory elements in Industry 4.0. We have presented a complete roadmap to cover three emerging technologies i.e. advance materials, artificial intelligence and block-chain technology. The utility of 2D materials such as graphene nanoparticles is yet to be explored as an interface for digitalization of a modern smart factory i.e. “factory-of-the-future”. In this article, we have explored how 2D material enhanced composites can act as an interface between physical and cyber spaces. An overview of employing graphene-based smart embedded sensors at various stages of composites manufacturing processes and their application in real-time structural health monitoring is presented. The technical challenges associated with interfacing graphene-based sensing networks with digital space are discussed. Additionally, an overview of the integration of associated tools such as artificial intelligence, machine learning and block-chain technology with graphene-based devices and structures is also presented.

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“…Over recent decades, artificial NNs have gained wide practical applications in completely different interdisciplinary and nontrivial areas, namely: identification of the composition and prediction of the properties of new compounds and materials; management of technological processes and product quality control; environmental assessment and management of natural resources; assessment and forecasting of economic parameters, both at the level of an individual product entering the market, and within the operation of an entire enterprise, or a group of enterprises; sociodynamic and econometric modeling; and predictive medicine [1][2][3][4][5][6][7][8]. The combination of such distinguishing factors such as structural flexibility, resistance to noise in the input parameters, ability to generalize and isolate hidden, nontrivial dependencies of the input and output parameters, learning ability, and adaptability to changes in external factors make it possible to use these factors to solve fundamentally different problems by combining components of the NNs [43].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

Tatarkanov¹,

Alexandrov²,

Glashev³

2021

J Appl Eng Science

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This paper proposes an algorithm for synthesizing a neural network (NN) structure to analyze complex structured, low entropy, ocular fundus images, characterized by iterative tuning of the adaptive model’s solver modules. This algorithm will assist in synthesizing models of NNs that meet the predetermined characteristics of the classification quality. The relevance of automating the process of ocular diagnostics of fundus pathologies is due to the need to develop domestic medical decision-making systems. Because of using the developed algorithm, the NN structure is synthesized, which will include two solver modules, and is intended to classify the dual-alternative information. Automated hybrid NN structures for intelligent segmentation of complex structured, low entropy, retinal images should provide increased efficiency of ocular diagnostics of fundus pathologies, reduce the burden on specialists, and decrease the negative impact of the human factor in diagnosis.

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The Use of Modern Information Technology in Predicting the Process of Impregnating Composite Preforms with Polymer Resins

Cited by 6 publications

References 0 publications

Application of neural simulation methods for technological parameters identification of composite products injection molding process

Application of neural simulation methods for technological parameters identification of composite products injection molding process

Graphene nanoparticles as data generating digital materials in industry 4.0

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

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