Aluminum and copper deposition through the process of thermal flame spray on polypropylene-pine wood fiber composite filaments and their applications

This paper presents a study about polypropylene-pine wood composites, both as filaments and products, coated with aluminum (Al) or copper (Cu), obtained through flame thermal spray process after subjecting the composites to thermal treatments in the second and third step of the study. Results revealed that a previous aluminum layer was needed in order to obtain copper coatings on the composites. The physical and mechanical properties of both metal coated composite filaments were also evaluated and compared with the uncoated composite filaments with and without heat treating these. Consequently, it was observed that the nature of the coating adhesion on the substrates was mechanical, and therefore abrasion blasting of filaments or the use of a higher wood fiber content in the composite improved the Al or Cu adhesion. Also, it was observed that extruded wood fiber/PP filaments should not be cooled in water because pieces might be molded directly once the moisture affects the metal coatings adhesion onto the substrates.

Development and Characterization of Phosphate-Based Glass Coatings via Suspension High-Velocity Oxy-Fuel (SHVOF) Thermal Spray Process

Phosphate-based glasses (PBGs) are promising materials for biomedical applications due to their biocompatible and fully resorbable characteristics in aqueous environments. These glasses can be coated onto metal substrate via the technique of suspension high-velocity oxy-fuel (SHVOF) thermal spraying to produce nanostructured coatings with improved physical and mechanical properties. PBGs coatings were produced using SHVOF thermal spray process at 50 and 75 kW flame power. The 75 kW coating was rougher (Ra = 3.6 ± 0.1 µm) than the 50 kW coating (Ra = 2.7 ± 0.1 µm), whereas the 50 kW coating was much thicker (24.6 ± 2.3 µm) than the 75 kW coating (16.0 ± 3.4 µm). Due to the rougher surface, the 75 kW coating showed high degradation and ion release rates. Moreover, structural changes were observed by Raman analysis, and the initial glass formulation contained Q1 (phosphate tetrahedra with one-bridging oxygen) and Q2 (phosphate tetrahedra with two-bridging oxygen) species. However, the coatings showed a reduction of Q2 species and higher concentrations of Q1 and Q0 (phosphate tetrahedra with no-bridging oxygen) species, which led to lower degradation rates and reduced ion release profiles in the glass coating compared to the initial glass.

Electromagnetic Shielding Performance of Different Metallic Coatings Deposited by Arc Thermal Spray Process

Advancement in electronic and communication technologies bring us up to date, but it causes electromagnetic interference (EMI) resulting in failure of building and infrastructure, hospital, military base, nuclear plant, and sensitive electronics. Therefore, it is of the utmost importance to prevent the failure of structures and electronic components from EMI using conducting coating. In the present study, Cu, Cu-Zn, and Cu-Ni coating was deposited in different thicknesses and their morphology, composition, conductivity, and EMI shielding effectiveness are assessed. The scanning electron microscopy (SEM) results show that 100 µm coating possesses severe defects and porosity but once the thickness is increased to 500 µm, the porosity and electrical conductivity is gradually decreased and increased, respectively. Cu-Zn coating exhibited lowest in porosity, dense, and compact morphology. As the thickness of coating is increased, the EMI shielding effectiveness is increased. Moreover, 100 µm Cu-Zn coating shows 80 dB EMI shielding effectiveness at 1 GHz but Cu and Cu-Ni are found to be 68 and 12 dB, respectively. EMI shielding effectiveness results reveal that 100 µm Cu-Zn coating satisfy the minimum requirement for EMI shielding while Cu and Cu-Ni required higher thickness.

Hard Alloys with High Content of WC and TiC—Deposited by Arc Spraying Process

Obtained by different spraying technologies: in atmospheric plasma spray, High Velocity Oxygen Fuel (HVOF) or laser cladding, the layers of hard alloys with a high content of WC and TiC find their industrial applications due to their high hardness and resistance to wear. Recognized as being a process associated with welding, the arc spraying process is a method applied industrially both in obtaining new surfaces and for reconditioning worn ones. This chapter presents the technology for obtaining ultra-hard layers based on WC and TiC - by the arc spraying process, using a classic spray device equipped with a conical nozzle system and tubular wire additional material containing ultra-hard compounds (WC, TiC). To study both the quality of deposits and the influence of thermal spray process parameters on the properties of deposits with WC and TiC content, we approached various investigative techniques, such as optical scanning microscopy (SEM), X-ray diffraction, and determination of adhesion, porosity, Vickers micro-hardness and wear resistance.

Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3-13 wt% TiO2 Coatings

The study aims to elaborate a neural model and algorithm for optimizing hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13 wt% TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings were prepared with different values of two spray process parameters: the stand-off distance and torch velocity. Microstructure, porosity and microhardness of the coatings were examined. Cavitation erosion tests were conducted in compliance with the ASTM G32 standard. Artificial neural networks (ANN) were employed to elaborate the model, and the multi-objectives genetic algorithm (MOGA) was used to optimize both properties and cavitation erosion resistance of the coatings. Results were analyzed with MATLAB software by Neural Network Toolbox and Global Optimization Toolbox. The fusion of artificial intelligence methods (ANN + MOGA) is essential for future selection of thermal spray process parameters, especially for the design of ceramic coatings with specified functional properties. Selection of these parameters is a multicriteria decision problem. The proposed method made it possible to find a Pareto front, i.e., trade-offs between several conflicting objectives—maximizing the hardness and cavitation erosion resistance of Al2O3-13 wt% TiO2 coatings and, at the same time, minimizing their porosity.

Neural Modelling of APS Thermal Spray Process Parameters for Optimising the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3 -13% TiO2 Coatings

The study aims to elaborate a neural model and algorithm for optimising hardness and porosity of coatings and thus ensure that they have superior cavitation erosion resistance. Al2O3-13wt.%TiO2 ceramic coatings were deposited onto 316L stainless steel by atmospheric plasma spray (ASP). The coatings were prepared with different values of two spray process parameters: the stand-off distance and torch velocity. Microstructure, porosity and microhardness of the coatings were examined. Cavitation erosion tests were conducted in compliance with the ASTM G32 standard. Artificial neural networks (ANN) were employed to elaborate the model, and the multi-objectives genetic algorithm (MOGA) was used to optimise both properties and cavitation erosion resistance of the coatings. Results were analysed with Matlab software by Neural Network Toolbox and Global Optimization Toolbox. The fusion of artificial intelligence methods (ANN+MOGA) is essential for future selection of thermal spray process parameters, especially for the design of ceramic coatings with specified functional properties. Selection of these parameters is a multicriteria decision problem. The proposed method made it possible to find a Pareto front, i.e. trade-offs between several conflicting objectives – maximising the hardness and cavitation erosion resistance of Al2O3-13%TiO2 coatings and, at the same time, minimizing their porosity.