Effect of substrate roughness on splat formation of thermally sprayed polymer

Polypropylene (PP) was flame sprayed onto rough mild steel substrates at room temperature (RT) that was preheated at 70 °C, 120 °C, and 170 °C. Single solidified droplets (splats) were collected and analysed to understand how processing variables influenced the thermal spray coating characteristics. The splat morphology was characterized in detail using optical and scanning electron microscopy (SEM). The splats exhibited a disk-like shape with a large central viscous core and a fully melted wide rim with a thin edge. The splat size increased with increasing substrate temperature. A unique flat microstructure was observed on the surface of the splat deposited onto the RT substrate, whereas a flowing pattern appeared on the splat surfaces deposited onto the preheated substrates and the pattern increased by increasing the substrate temperature. The results of this study revealed improved splat-substrate adhesion by heating the substrate from RT to 170 °C. On the basis of the result, the influence of substrate parameters on splat morphologies was employed to establish a relationship between the microstructural characteristics and processing variables of flame sprayed polymeric coatings

Micro-Raman Spectroscopy Shows How the Coating Process Affects the Characteristics of Hydroxylapatite

The diversity in the structural and chemical state of apatites allows implant manufacturers to fine-tune implant properties. This requires suitable manufacturing processes and characterization tools to adjust the amorphous phase and hydroxyl content from the source hydroxylapatite. Hydroxylapatite was processed by high-velocity oxy-fuel spraying, plasma spraying and flame spraying, and primarily analyzed by Raman spectroscopy. Investigation of rounded splats, the building blocks of thermal spray coatings, allowed correlation between the visual identity of the splat surface and the Raman spectra. Splats were heat-treated to crystallize any remaining amorphous phase. The ν1 PO4 stretching peak at 950-970 cm-1 displayed the crystalline order, but the hydroxyl peak at 3572 cm-1 followed the degree of dehydroxylation. Hydroxyl loss was greatest for flame-sprayed particles, which create the longest residence time for the melted particle. Higher-frequency hydroxyl peaks in flame- and plasma-sprayed splats indicated a lower structural order for the recrystallized hydroxylapatite within the splats. Crystallization at 700 C has shown potential for revealing hydroxyl ions previously trapped in amorphous calcium phosphate. This work compares Fourier transform infrared and Raman spectroscopy to measure the hydroxyl content in rapidly solidified apatites and shows that Raman spectroscopy is more suitable

A Morphological Study of Hydroxyapatite Splats Flame-Sprayed onto Commercially Pure Titanium

Thermal spray coatings consist of aggregates of solidified and flattened droplets. Although research has been directed into the examination of bulk coatings, a recent emphasis has been on examination of individual splats as a means to understand coating properties. In this study the morphology and microstructure of the solidified flattened droplets were examined. The morphology of single, micro-deposited HAp coatings onto commercially pure Ti (CP Ti) was investigated using scanning electron microscopy (SEM). Transmission Electron Microscopy (TEM) in conjunction with Focused Ion Beam (FIB) was used to reveal the ultra-microstructure. Also, the topography of splats sprayed at room temperature, 100 oC and 300 oC was investigated; as was the micromechanical properties of thermally sprayed hydroxyapatite (HAp) splats. The intention was to relate the processing conditions to the splat geometry and, hence, the micro-mechanical characteristics of splats that, together, confer extrinsic properties onto the bulk coating

An extreme learning machine algorithm to predict the in-flight particle characteristics of an atmospheric plasma spray process

A robust single hidden layer feed forward neural network (SLFN) is used in this study to model the in-flight particle characteristics of the atmospheric plasma spray (APS) process with regard to the input processing parameters. The in-flight particle characteristics influence the structure and properties of the APS coating and, thus, are considered important parameters to comprehend the manufacturing process. The training times of traditional back propagation algorithms, mostly used to model such processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. Performance comparisons of the networks trained with ELM algorithm and standard error back propagation algorithms are presented. It is found that networks trained with ELM have good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The trends represent robustness of the trained networks and enhance reliability of the application of the artificial neural network in modelling APS processes