Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data

Christopher, C; Sasikumar, T.; Santulli, Carlo; Fragassa, Cristiano

doi:10.5937/fmet1802253m

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…Increase of mass or volumetric share of SiC, Al 2 O 3 and graphite changes the tribological characteristics of MMCs. By combining the appropriate share of reinforcement materials, the optimal values of tribological characteristics of materials are achieved [24,25].…”

Section: Introductionmentioning

confidence: 99%

Dry Sliding Wear Performance of ZA27/SiC/GraphiteComposites

Miloradović

Vujanac

Mitrović

et al. 2019

Metals

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The paper describes the wear performance of zinc-aluminium ZA27 alloy, reinforced with silicon-carbide (SiC) and graphite (Gr) particles. The compo-casting technique produced the composite samples. The tested samples were: ZA27 alloy, ZA27/5%SiC composite, and ZA27/5%SiC/3%Gr hybrid composite. A block-on-disc tribometer was used during wear tests under the dry sliding conditions by varying the normal loads and sliding speeds. The sliding distance was constant during tests. The microstructure of the worn surfaces of the tested materials was analysed using the scanning electronic microscope (SEM) and the energy dispersive spectrometry (EDS).

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Section: Introductionmentioning

confidence: 99%

Dry Sliding Wear Performance of ZA27/SiC/GraphiteComposites

Miloradović

Vujanac

Mitrović

et al. 2019

Metals

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“…Another application of ANNs is the interpretation of acoustic emission data for failure prediction. Christopher et al [26] propose the prediction of the ultimate strength of aluminum/silicon carbide (Al/SiC) composites by using acoustic emission parameters through ANN analysis. This approach was earlier pursued for the prediction of the ultimate strength of unidirectional T‐300/914 tensile specimens using acoustic emission response and an ANN back propagation algorithm [132].…”

Section: State Of the Artmentioning

confidence: 99%

Machine learning for material characterization with an application for predicting mechanical properties

Stoll

Benner

2021

GAMM-Mitteilungen

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Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data‐driven methods for the discovery of patterns among multiple lengthscales and time‐scales and structure‐property relationships essential. These data‐driven approaches show enormous promise within materials science. The following review covers machine learning (ML) applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of ML methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness, or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests, or creep tests are often time consuming and expensive to perform. Therefore, the application of ML approaches is considered helpful for an easier generation of material property information. This study also gives an application of ML methods on small punch test (SPT) data for the determination of the property ultimate tensile strength for various materials. A strong correlation between SPT data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with ML.

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“…Aluminium and its alloy are widely preferred over the conventional materials in automotive, sports goods, marine and aircraft industries owing to its superior properties such as low density, good strength to weight ratio and wear resistance [1,2]. Literature demonstrated substantial augmentation in the mechanical properties and resistance to wear of the aluminium based materials upon reinforcing micro and nano-sized reinforcements such as silicon carbide (SiC) [3], titanium oxide (TiO 2 ) [4], alumina (Al 2 O 3 ) [5,6], graphene (GNPs) [7], titanium boride (TiB 2 ) [8], B 4 C [9] and carbon nanotubes (CNTs) [10]. The above-stated aluminium based material reinforced with micro and nano-size reinforcements (AMMCs) are widely used as automobile bodies, aircraft structure, cylinder liners, pistons, bearing surfaces, gears, brake components and connecting rods [1].…”

Section: Introductionmentioning

confidence: 99%

Microstructure, mechanical properties and machinability studies of Al7075/SiC/h-BN hybrid nanocomposite fabricated via ultrasonic-assisted squeeze casting

Soni¹,

Thomas²

2020

FME Transactions

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The present research article deals with the microstructure, mechanical properties and machinability investigation of squeeze-cast Al7075 and Al7075/SiC/h-BN hybrid nanocomposite. The Al7075 alloy nanocomposite has been reinforced by micro-size SiC (1 wt.%) particles and h-BN (0.5 wt.%) nanoparticles, prepared via ultrasonic-assisted melt-stirring approach. In order to achieve the better mixing of reinforcements, diminish the agglomeration effect of nanoparticles and improved wettability of the particles in the melt, SiC and h-BN powders have been ball-milled for the duration of 4 h. The microstructural investigation of the prepared nanocomposite was carried out by using optical microscopy (OM), scanning electron microscopy (SEM) and x-ray mapping analysis. The xray mapping and optical microscopic analysis show good dispersion uniformity of reinforcements and refinement in the grain sizes. The investigation of mechanical properties of hybrid nanocomposite shows the significant improvement of about 35.33 %, 21.69 %, 13.87 % and 12.27 % in the offset yield strength, ultimate tensile strength, Rockwell hardness and microhardness (Vickers), respectively. Furthermore, the machinability analysis has been performed to examine the influence of several machining parameters such as cutting speed, feed rate and depth of cut on the surface roughness, cutting force and chips length of the squeeze-cast hybrid nanocomposites under dry and minimum quantity lubrication (MQL) machining conditions. The outcomes of the machinability analysis for hybrid nanocomposites are compared with the Al7075 specimen and discussed.

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Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data

Cited by 8 publications

References 18 publications

Dry Sliding Wear Performance of ZA27/SiC/GraphiteComposites

Dry Sliding Wear Performance of ZA27/SiC/GraphiteComposites

Machine learning for material characterization with an application for predicting mechanical properties

Microstructure, mechanical properties and machinability studies of Al7075/SiC/h-BN hybrid nanocomposite fabricated via ultrasonic-assisted squeeze casting

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