State-of-the-Art Review of Machine Learning Applications in Additive Manufacturing; from Design to Manufacturing and Property Control

Sarkon, Garshasp Keyvan; Safaei, Babak; Kenevisi, Mohammad Saleh; Arman, Samaneh; Zeeshan, Qasim

doi:10.1007/s11831-022-09786-9

Cited by 28 publications

(6 citation statements)

References 273 publications

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“…Nguyen et al [23] designed a dynamic PdM structure based on LSTM, a deep learning architecture, to look into the impact imperfect prognostics have on maintenance decisions in Prognostics and Health Management (PHM) system. Sarkon et al [24] reviewed ML applications in additive manufacturing; from design to manufacturing and property control. Wang et al [25] suggested a prognostics approach based on similarity for estimating the RUL of designed systems, of which a unique data-driven RUL estimation methodology that begins with damage assessment and then employs a similarity-based matching method to compute RUL was presented.…”

Section: Recent Researchmentioning

confidence: 99%

Applications of Machine Learning in Aircraft Maintenance

Karaoğlu¹,

Mbah²,

Zeeshan³

2022

JEMSE

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Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.

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Section: Recent Researchmentioning

confidence: 99%

Applications of Machine Learning in Aircraft Maintenance

Karaoğlu¹,

Mbah²,

Zeeshan³

2022

JEMSE

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“…Additive manufacturing (AM) technology is different from traditional polymer material production processes such as injection molding and compression molding; AM technology uses computer assistance to achieve orderly stacking of materials and manufacture 3D solid products. − Its advantages include no need for molds, facilitating the production of complex hollow structures, improving the lightweight design, increasing the specific surface area, and reducing the skin-core structure.…”

Section: Introductionmentioning

confidence: 99%

Mechanical, Thermal, and Electrical Properties on 3D Printed Short Carbon Fiber Reinforced Polypropylene Composites

Zhan,

Su,

Tuo

et al. 2024

ACS Appl. Polym. Mater.

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Carbon fiber-reinforced polymer-matrix composites have been an important research topic due to their high performance and versatility. In this study, we investigated the mechanical, thermal, and electrical properties of short carbon fiberreinforced polypropylene (SCF/PP) composites prepared using the fused deposition modeling (FDM)-3D printing technique. PP was selected as the matrix for 3D printed composites to overcome the processing problems caused by the high content of SCFs. The addition of SCFs also effectively mitigated the warping problem during the PP cooling process. Compared to pure PP, the tensile strength increased by up to 35%, and the bending strength exhibited an approximate 40% enhancement. With increasing SCF content, the thermal conductivity exhibited a linear growth, reaching 0.266 W/(m • K) at 70 °C for the PPCF50 sample, simultaneously demonstrating excellent electrical conductivity. The material also displayed significant potential in electromagnetic protection applications, achieving a maximum shielding effectiveness of 29.8 dB. The texture direction and filling density in 3D printing settings have also been proven to play important roles in adjusting the performance of composite samples. The attempt of this study is beneficial for promoting the widespread application of polyolefin polymers as a consumable matrix material for 3D printed SCF-reinforced composite products.

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“…Cenosphere, as the part of fly ash is seemed to be advantageous material due to low cost, high strength, lightness, chemical inertness, low bulk density and high thermal resistance. [28][29][30][31][32][33][34] Several methods of fly ash utilization have been proposed. [35] Inspiring results have been achieved with application of modified cenosphere in the esterification, [34] CO 2 methanation, [36] photocatalytic degradation of organic pollutants, [37] dry reforming of methane, synthesis of ethyl propionate [39] and hydrogen formation.…”

Section: Introductionmentioning

confidence: 99%

“…Power stations around the world produce a huge amount of fly ash during the combustion of coal fuel, and this waste also requires proper handling. Cenosphere, as the part of fly ash is seemed to be advantageous material due to low cost, high strength, lightness, chemical inertness, low bulk density and high thermal resistance [28–34] . Several methods of fly ash utilization have been proposed [35] .…”

Section: Introductionmentioning

confidence: 99%

Fly‐Ash Cenosphere as Non‐Porous Ag‐Nanoparticle Support for Epoxidation of Styrene

Tarasova,

Khanov,

Vorobiev

et al. 2024

ChemistrySelect

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The global strategy to reduce waste production implies development of recycling technologies and conversion of various waste into value‐added products. Fly ash is an industrial waste produced in a large amount. It comprised an aluminosilicate cenosphere, which seems perspective as a support for heterogeneous catalysts. Here we report the preparation and application of heterogeneous silver nanocatalysts deposited on cenosphere in the epoxidation reaction, a relevant process for preparation of valuable organic precursors and target chemicals. Reductive wet deposition method yields Ag nano‐dispersed coating of improved quality represented by Ag particles of about 2.7 nm on the surface of the microspheres size of 50–250 microns for catalytic synthesis of styrene‐oxide. The 0.1 mmol/g Ag/cenosphere provided best catalytic results with threefold excess of tret‐butyl hydroperoxide in acetonitrile at 80 °C. Comparison of fly ash cenosphere with other supports suggests that low porosity of microspherical support allows for an easier access of reagents to active sites of the catalyst and significantly increases the yield of epoxide and selectivity of the reaction.

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State-of-the-Art Review of Machine Learning Applications in Additive Manufacturing; from Design to Manufacturing and Property Control

Cited by 28 publications

References 273 publications

Applications of Machine Learning in Aircraft Maintenance

Applications of Machine Learning in Aircraft Maintenance

Mechanical, Thermal, and Electrical Properties on 3D Printed Short Carbon Fiber Reinforced Polypropylene Composites

Fly‐Ash Cenosphere as Non‐Porous Ag‐Nanoparticle Support for Epoxidation of Styrene

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