scholarly journals The Influence of Printing Orientation on Surface Texture Parameters in Powder Bed Fusion Technology with 316L Steel

Micromachines ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 639 ◽  
Author(s):  
Tomasz Kozior ◽  
Jerzy Bochnia
Keyword(s):  
Surface Texture ◽  
Microelectromechanical Systems ◽  
Powder Bed Fusion ◽  
Additive Technology ◽  
Powder Bed ◽  
Texture Parameters ◽  
316L Steel ◽  
Spatial Parameters ◽  
The Impact

Laser technologies for fast prototyping using metal powder-based materials allow for faster production of prototype constructions actually used in the tooling industry. This paper presents the results of measurements on the surface texture of flat samples and the surface texture of a prototype of a reduced-mass lathe chuck, made with the additive technology—powder bed fusion. The paper presents an analysis of the impact of samples’ orientation on the building platform on the surface geometrical texture parameters (two-dimensional roughness profile parameters (Ra, Rz, Rv, and so on) and spatial parameters (Sa, Sz, and so on). The research results showed that the printing orientation has a very large impact on the quality of the surface texture and that it is possible to set digital models on the building platform (parallel—0° to the building platform plane), allowing for manufacturing models with low roughness parameters. This investigation is especially important for the design and 3D printing of microelectromechanical systems (MEMS) models, where surface texture quality and printable resolution are still a large problem.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makiko Yonehara ◽  
Chika Kato ◽  
Toshi-Taka Ikeshoji ◽  
Koki Takeshita ◽  
Hideki Kyogoku
Keyword(s):  
Additive Manufacturing ◽  
Surface Texture ◽  
In Situ Monitoring ◽  
Mean Square ◽  
Powder Bed Fusion ◽  
Internal Defects ◽  
Powder Bed ◽  
Texture Parameters ◽  
Areal Surface

AbstractThe 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.

Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Tuğrul Özel ◽  
Ayça Altay ◽  
Bilgin Kaftanoğlu ◽  
Richard Leach ◽  
Nicola Senin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Density ◽  
Surface Topography ◽  
Process Parameters ◽  
Surface Texture ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Texture Parameters ◽  
Focus Variation

Abstract The powder bed fusion-based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.

scholarly journals Analysis of High-Friction Surface Texture with Respect to Friction and Wear

Coatings ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 758
Author(s):  
Cibi Pranav ◽  
Minh-Tan Do ◽  
Yi-Chang Tsai
Keyword(s):  
Friction Coefficient ◽  
Surface Texture ◽  
Safety Concern ◽  
Future Research ◽  
Scanning System ◽  
Novel Approach ◽  
Texture Parameters ◽  
Aggregate Loss ◽  
Friction Surfaces ◽  
The Impact

High Friction Surfaces (HFS) are applied to increase friction capacity on critical roadway sections, such as horizontal curves. HFS friction deterioration on these sections is a safety concern. This study deals with characterization of the aggregate loss, one of the main failure mechanisms of HFS, using texture parameters to study its relationship with friction. Tests are conducted on selected HFS spots with different aggregate loss severity levels at the National Center for Asphalt Technology (NCAT) Test Track. Friction tests are performed using a Dynamic Friction Tester (DFT). The surface texture is measured by means of a high-resolution 3D pavement scanning system (0.025 mm vertical resolution). Texture data are processed and analyzed by means of the MountainsMap software. The correlations between the DFT friction coefficient and the texture parameters confirm the impact of change in aggregates’ characteristics (including height, shape, and material volume) on friction. A novel approach to detect the HFS friction coefficient transition based on aggregate loss, inspired by previous works on the tribology of coatings, is proposed. Using the proposed approach, preliminary outcomes show it is possible to observe the rapid friction coefficient transition, similar to observations at NCAT. Perspectives for future research are presented and discussed.

scholarly journals Multiscale Analysis of Surface Texture Quality of Models Manufactured by Laser Powder-Bed Fusion Technology and Machining from 316L Steel

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2794
Author(s):  
Damian Gogolewski ◽  
Tomasz Bartkowiak ◽  
Tomasz Kozior ◽  
Paweł Zmarzły
Keyword(s):  
Multiscale Analysis ◽  
Wavelet Transformation ◽  
316L Stainless Steel ◽  
Multiscale Methods ◽  
Fusion Technology ◽  
Current Standard ◽  
Statistical Research ◽  
Powder Bed ◽  
316L Steel

The paper presents the results of tests aimed at evaluating the surface textures of samples manufactured from material based on 316L stainless steel. The analysis of the surface topography was conducted based on the classical approach in accordance with the current standard and with the use of multiscale methods; i.e., wavelet transformation and geometric via curvature. Selective laser melting 3D printing technology was used to produce samples for surface testing. Furthermore, additional assessment of surfaces created as result of milling was conducted. Statistical research demonstrated a differentiation in the distribution of particular morphological features in certain ranges of the analyzed scales.

Improvement of Build Speed and Quality of H13 Tool Steel Processed by Laser-Beam Powder Bed Fusion with a High-Power Laser

2021 ◽  
Vol 68 (10) ◽  
pp. 415-421
Author(s):  
Takashi MIZOGUCHI ◽  
Takaya NAGAHAMA ◽  
Makoto TANO ◽  
Shigeru MATSUNAGA ◽  
Takayuki YOSHIMI ◽  
...  
Keyword(s):  
Laser Beam ◽  
Tool Steel ◽  
High Power ◽  
Powder Bed Fusion ◽  
High Power Laser ◽  
Power Laser ◽  
Powder Bed ◽  
H13 Tool Steel

scholarly journals Improving the quality of up-facing inclined surfaces in laser powder bed fusion of metals using a dual laser setup

Procedia CIRP ◽  
2020 ◽  
Vol 94 ◽  
pp. 266-269 ◽  
Author(s):  
Jitka Metelkova ◽  
Daniel Ordnung ◽  
Yannis Kinds ◽  
Ann Witvrouw ◽  
Brecht Van Hooreweder
Keyword(s):  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Inclined Surfaces ◽  
Dual Laser ◽  
Laser Setup

Towards Thermal Simulation of Powder Bed Fusion on Path Level

2019 ◽  
Author(s):  
Yaqi Zhang ◽  
Vadim Shapiro ◽  
Paul Witherell
Keyword(s):  
Manufacturing Process ◽  
Thermal Simulation ◽  
Experimental Results ◽  
Intermediate Step ◽  
Valuable Insight ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Thermal Simulations ◽  
Element Method

Abstract Powder bed fusion (PBF) is a widely used additive manufacturing (AM) technology to produce metallic parts. Understanding the relationships between process parameter settings and the quality of finished parts remains a critical research question. Developing this understating involves an intermediate step: Process parameters, such as laser power and scan speed, influence the ongoing process characteristics, which then affect the final quality of the finished parts. Conventional approaches to addressing those challenges such as powder-based simulations (e.g., discrete element method (DEM)) and voxel-based simulations (e.g., finite element method (FEM)) can provide valuable insight into process physics. Those types of simulations, however, are not well-suited to handle realistic manufacturing plans due to their high computational complexity. Thermal simulations of the PBF process have the potential to implement that intermediate step. Developing accurate thermal simulations, however, is difficult due to the physical and geometric complexities of the manufacturing process. We propose a new, meso-scale, thermal-simulation, which is built on the path-level interactions described by a typical process plan. Since our model is rooted in manufactured geometry, it has the ability to produce scalable, thermal simulations for evaluating realistic process plans. The proof-of-concept simulation result is validated against experimental results in the literature and experimental results from National Institute of Standards and Technology (NIST). In our model, the laser-scan path is discretized into elements, and each element represents the newly melted material. An element-growth mechanism is introduced to simulate the evolution of the melt pool and its thermal characteristics during the manufacturing process. The proposed simulation reduces computational demands by attempting to capture the most important thermal effects developed during the manufacturing process. Those effects include laser-energy absorption, thermal interaction between adjacent elements and elements within the underneath substrate, thermal convection and radiation, and powder melting.

Design Rules and In-Situ Quality Monitoring of Thin-Wall Features Made Using Laser Powder Bed Fusion

2019 ◽  
Author(s):  
Aniruddha Gaikwad ◽  
Farhad Imani ◽  
Prahalad Rao ◽  
Hui Yang ◽  
Edward Reutzel
Keyword(s):  
Neural Network ◽  
Aspect Ratio ◽  
Convolutional Neural Network ◽  
Thin Wall ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Design Rules ◽  
Powder Bed

Abstract The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: 1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall (l) to its thickness (t) defined as the aspect ratio (length-to-thickness ratio, l/t), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine. 2) Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.

Sign in / Sign up

Export Citation Format

Share Document