Hybrid Surface Characterisation of Intra Thin-Walled Ti6Al4V Surfaces Produced by Laser Powder Bed Fusion Technology

The proposed work investigates the hybrid surface characterisation of intra thin-walled Ti6Al4V surfaces fabricated using laser powder bed fusion technology. The thin-walled samples were characterised using scanning electron microscopy and Opto-digital microscopy techniques. The fractal dimensional analysis was performed using ImageJ software integrated with an open-source MultiFrac plug-in. The surface microscopy analysis revealed satellites powder particles, partially melted powder particles, spherical balling, and pores on the thin-walled surface. The fractal dimension establishes a correlation between the surface roughness values. The surface areal surface parameters analysis suggested variation along the build direction of thin-walled Ti6Al4V sample. The development of sharp peaks and thus higher Ra, Sku and Ssk values were found along the build direction of the intra thin-walled samples. Therefore, the combination of areal surface topography analysis and fractal dimension approach can be a promising methodology towards surface characterisation of additively manufactured complex thin-walled surfaces.

Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces

Artificial Neural Networks (ANNs) are well-established knowledge acquisition systems with proven capacity for learning and generalisation. Therefore, ANNs are widely applied to solve engineering problems and are often used in laser-based manufacturing applications. There are different pattern recognition and control problems where ANNs can be effectively applied, and one of them is laser structuring/texturing for surface functionalisation, e.g. in generating Laser-Induced Periodic Surface Structures (LIPSS). They are a particular type of sub-micron structures that are very sensitive to changes in laser processing conditions due to processing disturbances like varying Focal Offset Distance (FOD) and/or Beam Incident Angle (BIA) during the laser processing of 3D surfaces. As a result, the functional response of LIPSS-treated surfaces might be affected, too, and typically needs to be analysed with time-consuming experimental tests. Also, there is a lack of sufficient process monitoring and quality control tools available for LIPSS-treated surfaces that could identify processing patterns and interdependences. These tools are needed to determine whether the LIPSS generation process is in control and consequently whether the surface’s functional performance is still retained. In this research, an ANN-based approach is proposed for predicting the functional response of ultrafast laser structured/textured surfaces. It was demonstrated that the processing disturbances affecting the LIPSS treatments can be classified, and then, the surface response, namely wettability, of processed surfaces can be predicted with a very high accuracy using the developed ANN tools for pre- and post-processing of LIPSS topography data, i.e. their areal surface roughness parameters. A Generative Adversarial Network (GAN) was applied as a pre-processing tool to significantly reduce the number of required experimental data. The number of areal surface roughness parameters needed to fully characterise the functional response of a surface was minimised using a combination of feature selection methods. Based on statistical analysis and evolutionary optimisation, these methods narrowed down the initial set of 21 elements to a group of 10 and 6 elements, according to redundancy and relevance criteria, respectively. The validation of ANN tools, using the salient surface parameters, yielded accuracy close to 85% when applied for identification of processing disturbances, while the wettability was predicted within an r.m.s. error of 11 degrees, equivalent to the static water contact angle (CA) measurement uncertainty.

Correlation between surface texture and internal defects in laser powder-bed fusion additive manufacturing

The availability of an in-situ monitoring and feedback control system during the implementation of metal additive manufacturing technology ensures that high-quality finished parts are manufactured. This study aims to investigate the correlation between the surface texture and internal defects or density of laser-beam powder-bed fusion (LB-PBF) parts. In this study, 120 cubic specimens were fabricated via application of the LB-PBF process to the IN 718 Ni alloy powder. The density and 35 areal surface-texture parameters of manufactured specimens were determined based on the ISO 25,178–2 standard. Using a statistical method, a strong correlation was observed between the areal surface-texture parameters and density or internal defects within specimens. In particular, the areal surface-texture parameters of reduced dale height, core height, root-mean-square height, and root-mean-square gradient demonstrate a strong correlation with specimen density. Therefore, in-situ monitoring of these areal surface-texture parameters can facilitate their use as control variables in the feedback system.

Correlation between Fractal Dimension and Areal Surface Parameters for Fracture Analysis after Bending-Torsion Fatigue

This paper investigates the fracture surface topography of two steel and aluminum alloys subject to bending-torsion fatigue loadings, as well as their susceptibility to fatigue performance and failure mechanisms. Using fracture surface topography data analysis, elements with different geometries were elaborated. A correlation between the fractal dimension, other selected parameters of surface topography such as areal Sx, and fatigue loading conditions was found. Distinctions in particular regions of cracks were also recognized through proving the correctness and universality of the total fracture surface method. The influence of fatigue loading parameters on the surface topography of fatigue fractures was demonstrated. For the analyzed cases, results show that the fractal dimension and standard surface topography parameters represent a correlation between them and loading conditions. As a single parameter, the appropriate loading ratio cannot be outright calculated with fractal dimension, but can be estimated with some approximation, taking into account additional assumptions.

Effect of Cut-Off, Evaluation Length, and Measurement Area in Profile and Areal Surface Texture Characterization of As-Built Metal Additive Manufactured Components

Surface texture characterization of components built using additive manufacturing (AM) remains a challenge. The presence of various asperities and random roughness distributions across a surface poses several challenges to users in selecting an appropriate cut-off wavelength (λc), evaluation length (ln), and measurement area. This paper investigates a modified framework for surface texture characterization of AM components. First, the surface asperities in an AM component were identified through scanning electron microscope (SEM) analyses. The maximum diameter (φm) of the surface asperities were determined through image processing and were used as cut-off for surface texture evaluation. Second, another set of surface texture results were extracted using standard measurement procedures per ISO 4287, 4288, 25178-1, -2, and -3. Third, the investigative measurement framework’s effectiveness and suitability were explored by comparing the results with ISO standard results. Last, the effects of using non-standard cut-off wavelength, evaluation length, and measurement area during surface texture characterization were studied, and their percentage deviations from the standard values were discussed. The key findings prove that (a) the evaluation length could be compromised instead of cut-off, (b) measurement area must be 2.5 times the maximum asperity size present in the surface, and (c) it is possible to identify, distinguish, and evaluate specific features from the AM surface by selecting appropriate filters, thereby characterizing them specifically. The investigations and the obtained results serve as valuable data for users to select appropriate measurement settings for surface texture evaluation of AM components.

Surface Texture Characterization of Metal Selective Laser Melted Part With Varying Surface Inclinations

Additive manufacturing offers the advantage of infinite freedom to design and fabricate complex parts at reduced lead-time. However, the surface quality of additively manufactured parts remains well behind the conventionally processed counterparts. This paper aims to systematically investigate the impact of varying surface inclination angles with respect to the build direction on the resultant surface textures. A bespoke metal truncheon artifact with inclination angles varying from 0 deg to 180 deg was built by selective laser melting. Focus variation microscopy was used to measure the topography of inclined surfaces with a tilt angle of up to 132 deg. The measurement data were then analyzed to characterize the staircase effect and the particles adherent to the artifact surface. Areal surface texture parameters, including height parameters, spatial parameters, functional parameters, and feature parameters, were explored to quantify the general surface topography, the staircase effect, and the particle features. The areal surface texture characterization and particle analysis reveal the resulted surface topographies are strongly correlated with the surface inclination angles.

Effects of Heat Treatment of Selective Laser Melting Printed Ti-6Al-4V Specimens on Surface Texture Parameters and Cell Attachment

Selective laser melting (SLM) is extensively used for fabricating metallic biomedical products. After 3D printing, it is almost always advisable to apply a heat treatment to release the internal tensions or optimize the mechanical properties of the printed parts. The aim of this paper is to investigate the effects of heat treatment of SLM printed Ti-6Al-4V (Ti64) circular specimens on the areal surface texture parameters and cell attachment. Areal surface texture parameters, including the arithmetic mean height (Sa), root-mean-square height (Sq), skewness (Ssk), and kurtosis (Sku) were characterized. In addition, wavelet-based multi-resolution analysis was applied to investigate the characteristic length scales of untreated and heat-treated Ti64 specimens. In this study, the vertical distance between the highest and lowest position of cell attachment for each sampling area was defined as DH. Results showed that an increase in the periodic characteristic length scale was primarily due to the formation of large-scale aggregations of Ti64 metal powder particles on the heat-treated surface. In addition, MG-63 cells preferred lying in concave hollows; in heat-treated specimens, values of DH statistically significantly decreased from 31.6 ± 4.2 to 8.8 ± 2.8 μm, while Sku decreased from 3.3 ± 1.4 to 2.6 ± 0.6, indicating a strong influence of Sku on cell attachment.