Determining the Mechanical Properties of a New Composite Material using Artificial Neural Networks

Ciupan, Emilia; Ciupan, Mihai; Jucan, Daniela-Corina

doi:10.14445/22315381/ijett-v66p218

Cited by 5 publications

(3 citation statements)

References 8 publications

(21 reference statements)

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“…Studies regarding the properties and applications of composite materials obtained using hemp fibres as reinforcing agents and various matrices, such as polyethylene [ 23 , 24 , 25 ], cement [ 26 , 27 ], polyurethane [ 28 ], polylactic acid [ 29 ] and polypropylene [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], are presented in the literature. Different authors have highlighted compression molding, extrusion, and injection molding as technologies suitable for processing the composites [ 37 , 38 , 39 , 40 , 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Characterisation of Hemp Fibres Reinforced Composites Using Thermoplastic Polymers as Matrices

Stelea

Filip²,

Lisă

et al. 2022

Polymers

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Hemp fibres used as a reinforcing agent and three polymeric matrices (polypropylene, bicomponent, recycled polyester) were used to obtain composite materials by needle punching and heat pressing. The influence of the hemp/matrix ratio and the nature of the matrix on the properties of the composites were analysed. The obtained composites were characterised by physical–mechanical indices, thermal analysis (thermogravimetry (TG), differential thermogravimetry (DTG) and Differential Scanning Calorimetry (DSC)), Fourier Transform Infrared Spectroscopy (FTIR-ATR) analysis, Scanning Electron Microscopy (SEM) and Chromatic measurements. The mechanical properties of composites are influenced by both the hemp/matrix ratio and the nature of the matrix. The thermal stability of composites decreased as the amount of hemp increased (for the same mass losses, the decomposition temperature decreased significantly for composites containing a quantity of hemp greater than 50%). Regarding the nature of the matrix, for the same mass loss, the highest decomposition temperature was presented by the composites containing recycled polyester as matrix, and the lowest one was presented by composites containing polypropylene fibres as matrix. The FTIR and SEM analyses highlight the changes that occurred in the structure of the composite, changes determined both by the amount of hemp in the composite and by the nature of the matrix.

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Section: Introductionmentioning

confidence: 99%

Characterisation of Hemp Fibres Reinforced Composites Using Thermoplastic Polymers as Matrices

Stelea

Filip²,

Lisă

et al. 2022

Polymers

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“…In recent decades, rapid development of information technologies as well as automation of data collection and storage processes have contributed to the accumulation and systematization of information about the physical and mechanical properties of bulk amorphous metal alloys glasses [29][30][31][32]. The methods of machine learning operate with large arrays of the data and allow us to determine the relationship between composition and properties of alloys both already known and not previously known [33][34][35][36]. For example, Xiong and co-authors have been developed a machine learning model that can predict the glass-forming ability and elastic moduli of bulk metallic glasses based on the fundamental atomic properties, chemical and physical properties obtained from experiments or density functional theory simulations [37].…”

Section: Introductionmentioning

confidence: 99%

Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

Galimzyanov

Doronina

Мокшин

2023

Metals

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The development and implementation of the methods for designing amorphous metal alloys with desired mechanical properties is one of the most promising areas of modern materials science. Here, the machine learning methods appear to be a suitable complement to empirical methods related to the synthesis and testing of amorphous alloys of various compositions. In the present work, it is proposed a method to determine amorphous metal alloys with mechanical properties closest to those required. More than 50,000 amorphous alloys of different compositions have been considered, and the Young’s modulus E and the yield strength σy have been evaluated for them by the machine learning model trained on the fundamental physical properties of the chemical elements. Statistical treatment of the obtained results reveals that the fundamental physical properties of the chemical element with the largest mass fraction are the most significant factors, whose values correlate with the values of the mechanical properties of the alloys, in which this element is involved. It is shown that the values of the Young’s modulus E and the yield strength σy are higher for amorphous alloys based on Cr, Fe, Co, Ni, Nb, Mo and W formed by the addition of semimetals (e.g., Be, B, Al, Sn), nonmetals (e.g., Si and P) and lanthanides (e.g., La and Gd) than for alloys of other compositions. Increasing the number of components in alloy from 2 to 7 and changing the mass fraction of chemical elements has no significantly impact on the strength characteristics E and σy. Amorphous metal alloys with the most improved mechanical properties have been identified. In particular, such extremely high-strength alloys include Cr80B20 (among binary), Mo60B20W20 (among ternary) and Cr40B20Nb10Pd10Ta10Si10 (among multicomponent).

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“…The results were having a different combination of speed, feed, cutting depth, and tool nose radius during turning procedure investigated by Ramezani and Afsari. 38 Ciupan et al 39 estimated the various mechanical properties of composites using the principles of ANN modeling. The efficiency of the ANN model was found satisfactory for the prediction of mechanical properties of developed composite materials.…”

Section: Introductionmentioning

confidence: 99%

Neural network modeling and combined compromise solution (CoCoSo) method for optimization of drilling performances in polymer nanocomposites

Kharwar

Verma

Singh

2020

Journal of Thermoplastic Composite Materials

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Nowadays, polymer nanocomposite becomes a suitable alternative to conventional materials for lightweight and structural applications. Multiwall carbon nanotube (MWCNT)-reinforced epoxy composites possess superior electrical, mechanical, and thermal properties. In this article, drilling of MWCNT/epoxy nanocomposites has been investigated by considering varying parameters, namely, reinforcement wt% of MWCNT ( W), speed ( N), feed rate ( F), and tool material ( M). The response surface methodology (RSM) array was used for drilling experimentation. The mathematical modeling of drilling parameters was done by using artificial neural network. This study also highlights the integrated approach of principal components analysis (PCA)-embedded combined compromise solution method for multiobjective optimization of conflicting responses such as surface roughness (Ra), torque (Tr), and thrust force (Th). The PCA tool efficiently identified the response priority weight during the aggregation process. The confirmatory test was directed to evaluate the efficiency of the proposed hybrid module. The outcomes show a good agreement between the predicted and experimental value, and it can be endorsed to the polymer manufacturing sector for quality and productivity enhancements.

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Determining the Mechanical Properties of a New Composite Material using Artificial Neural Networks

Cited by 5 publications

References 8 publications

Characterisation of Hemp Fibres Reinforced Composites Using Thermoplastic Polymers as Matrices

Characterisation of Hemp Fibres Reinforced Composites Using Thermoplastic Polymers as Matrices

Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

Neural network modeling and combined compromise solution (CoCoSo) method for optimization of drilling performances in polymer nanocomposites

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