Special Issue on Metal-Based Composite Materials: Preparation, Structure, Properties and Applications

Suzdaltsev, Andrey; Rakhmanova, O. R.

doi:10.3390/app13084799

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Isametova et al [14] discuss the development of reliability prediction methods on the basis of experimental data and propose a statistical model for assessing reliability in terms of structural strength of polymer composite products. Suzdaltsev et al [15] analysed recent advances in the synthesis of new metal matrix composites and concluded that new approaches to the design and synthesis of such composites are still needed to improve and optimise their structure and properties. Zhao et al [16] used first principles to calculate various types of interfaces in graphene/aluminium matrix composites, and found that Si interfaces are prone to segregation and become a source of crack propagation.…”

Section: Introductionmentioning

confidence: 99%

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Xue,

Huang,

et al. 2023

Metals

View full text Add to dashboard Cite

Addressing the exceptional properties of aluminium alloy composites reinforced with graphene, this study presents an interpretable machine learning approach to aid in the rapid and efficient design of such materials. Initially, data on these composites were gathered and optimised in order to create a dataset of composition/process-property. Several machine learning algorithms were used to train various models. The SHAP method was used to interpret and select the best performing model, which happened to be the CatBoost model. The model achieved accurate predictions of hardness and tensile strength, with coefficients of determination of 0.9597 and 0.9882, respectively, and average relative errors of 6.02% and 5.01%, respectively. The results obtained from the SHAP method unveiled the correlation between the composition, process and properties of aluminium alloy composites reinforced with graphene. By comparing the predicted and experimental data in this study, all machine learning models exhibited prediction errors within 10%, confirming their ability to generalise. This study offers valuable insights and support for designing high-performance aluminium matrix composites reinforced with graphene and showcases the implementation of machine learning in materials science.

show abstract

Section: Introductionmentioning

confidence: 99%

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Xue,

Huang,

et al. 2023

Metals

View full text Add to dashboard Cite

show abstract

“…Over the years, FDM technology has experienced remarkable advancement, ushering in an era where the creation of complex and functional objects has become effortless [8][9][10]. This innovation, underpinned by the layer-by-layer construction of 3D objects from digital blueprints [11][12][13], has unlocked new horizons spanning aerospace, automotive, medical, and consumer goods sectors [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Correlation between Infill Percentages, Layer Width, and Mechanical Properties in Fused Deposition Modelling of Poly-Lactic Acid 3D Printing

Moradi,

Rezayat,

Rozhbiany

et al. 2023

Machines

View full text Add to dashboard Cite

The field of additive manufacturing (AM) has seen a transformation in the production of intricate and complex parts for various applications. Fused Deposition Modelling (FDM), among AM techniques, has garnered significant attention, particularly in fields like fibre-reinforced composites (FRC). In this study, the world of FDM-printed Polylactic Acid (PLA) components is explored, with a focus on how mechanical properties are influenced by infill percentages and layer widths. Through the utilisation of Response Surface Methodology (RSM), the optimisation of FDM-PLA 3D printing for a wide range of biomaterial applications is achieved, along with the unveiling of the potential for remarkable improvements in mechanical performance. Notably, a remarkable 91% reduction in surface roughness for PLA composites was achieved, accompanied by an impressive 25.6% and 34.1% enhancement in the tensile strength and Young’s modulus of fibre-reinforced PLA composites, respectively. This work, positioned at the crossroads of FDM, lays the groundwork for substantial advancements in the realm of additive manufacturing.

show abstract

Metal and Polymer Based Composites Manufactured Using Additive Manufacturing—A Brief Review

et al. 2023

View full text Add to dashboard Cite

This review examines the mechanical performance of metal- and polymer-based composites fabricated using additive manufacturing (AM) techniques. Composite materials have significantly influenced various industries due to their exceptional reliability and effectiveness. As technology advances, new types of composite reinforcements, such as novel chemical-based and bio-based, and new fabrication techniques are utilized to develop high-performance composite materials. AM, a widely popular concept poised to shape the development of Industry 4.0, is also being utilized in the production of composite materials. Comparing AM-based manufacturing processes to traditional methods reveals significant variations in the performance of the resulting composites. The primary objective of this review is to offer a comprehensive understanding of metal- and polymer-based composites and their applications in diverse fields. Further on this review delves into the intricate details of metal- and polymer-based composites, shedding light on their mechanical performance and exploring the various industries and sectors where they find utility.

show abstract

Special Issue on Metal-Based Composite Materials: Preparation, Structure, Properties and Applications

Abstract: The Special Issue is aimed at analyzing modern trends and recent advances in the synthesis of new metal-based composite materials [...]

Cited by 3 publications

References 13 publications

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance

Correlation between Infill Percentages, Layer Width, and Mechanical Properties in Fused Deposition Modelling of Poly-Lactic Acid 3D Printing

Metal and Polymer Based Composites Manufactured Using Additive Manufacturing—A Brief Review

Contact Info

Product

Resources

About