Cold spray process for the production of AlSi10Mg coatings on glass fibers reinforced polymers

Gatta, Roberta Della; Viscusi, Antonio; Perna, Alessia Serena; Caraviello, Antonio; Astarita, Antonello

doi:10.1080/10426914.2020.1813895

Cited by 21 publications

(10 citation statements)

References 43 publications

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“…In this case, although the particle speed is optimal for the copper to create a metal coating, the impact energy, and related impact stresses transmitted from the particle to the substrate go beyond the material strength, hence, dense erosion takes place; the impacts of the particles damaged the coating zone and broken fibres are visible in the figure . Giraud et al [91] stated that by increasing the carrier gas pressure, the particle penetration into the polymer increases. Such a result was observed also by other authors [52]. Anyway, at higher pressure, the deformation of the particle does still not occur while the substrate could be easily deformed or even cracked.…”

Section: Gas Pressuresupporting

confidence: 83%

“…In this last case, higher resistance to the incoming particles of the subsequent layers is expected. Della Gatta et al [52] investigated the influence of the glass fibres reinforced composite stratification on the deposition of AlSi10Mg powder. Two different stratifications to obtain two different thicknesses of the superficial polymer layer were used.…”

Section: The Surface Morphology and The Fibre Influencementioning

confidence: 99%

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Cold spray deposition of metallic coatings on polymers: a review

et al. 2021

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Cold spray deposition, which can be framed in the wider family of additive manufacturing, is a manufacturing technique that is able to produce coatings on diverse types of substrates through the deposition of feedstock powder. As a low-temperature process, cold spray represents a potential solution for the metallization of temperature-sensitive materials, i.e. polymers and polymer matrix composites. The study of the cold spray technology for the metallization of polymers is still in its early stage, and the deposition mechanisms of metals on polymer-based materials are not thoroughly understood yet. On these premises, a review on this topic is needed to systematically depict the actual state of the art and to provide a reliable and well-organized overview discussing all the theories arisen in these years. In summary, this review aims: i) to collect all the available literature and enucleate the most discussed and interesting points (the most prevailing theories regarding the bonding mechanisms, the influence of the different process parameters and the main characteristics of cold-sprayed coating), providing a reliable and well-organized state of the art; ii) to define the open questions and to delineate the directions of future work.

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Section: Gas Pressuresupporting

confidence: 83%

Section: The Surface Morphology and The Fibre Influencementioning

confidence: 99%

Cold spray deposition of metallic coatings on polymers: a review

et al. 2021

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“…The abovementioned depositions were carried out using low-pressure cold spray equipment (DYCOMET). The carrier gas chosen was air, as previous experiments evidenced no significant differences portrayed using different carrier gasses [20].…”

Section: Methodsmentioning

confidence: 99%

Cold Sprayed Metallic Coatings on Fibre-Reinforced Composites: A Machine Learning Approach for the Optimization of the Process

Perna

Carrino

Gatta

et al. 2022

KEM

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Cold spray additive manufacturing (CSAM) is a promising process for producing metallic layers on different substrates, using powders as a feedstock material. The metallic powders are deposited through pressured gas that reaches supersonic velocities. Due to the low heat input required, as the powders remain in solid-state, this technology is particularly suitable to coat thermo-sensitive materials such as composites. Moreover, the absence of melting allows design freedom, allowing to build complex structures on the substrates, layer by layer. In this scenario, machine learning techniques can be crucial to improve the quality and understanding of this manufacturing process. The aim of this work is to predict the deformation and penetration of a particle upon impact using machine learning techniques in order to assess the properties of the coating. A univariate linear regression method was chosen to verify the feasibility of Theory Guided Machine Learning (TGML) techniques to predict the characteristics of the coating. The training dataset was obtained from both experimental data and computational data. It was confirmed that TGML could be a good route to pursue in order to optimize this process.

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“…On the other hand, generally reported issues include substrate damage (Ref 18 , 26 ), low deposition efficiencies (DEs) (i.e. the ratio of the weight of the effectively deposited particles to sprayed particles onto the substrate) (Ref 24 , 26 - 29 ), or delamination (Ref 17 , 31 - 33 ).…”

Section: Introductionmentioning

confidence: 99%