The Research of Surface Roughness Prediction with Machine Learning According to Process Parameters in Laser Powder Bed Fusion

2021 ◽  
pp. 62-65
Author(s):  
Jageon Koo ◽  
Eunju Park ◽  
Adrian Matias Chung Baek ◽  
Namhun Kim
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Process Parameters ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Surface Roughness Prediction ◽  
Powder Bed ◽  
Roughness Prediction

scholarly journals On the Relevance of Volumetric Energy Density in the Investigation of Inconel 718 Laser Powder Bed Fusion

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 538 ◽  
Author(s):  
Fabrizia Caiazzo ◽  
Vittorio Alfieri ◽  
Giuseppe Casalino
Keyword(s):  
Surface Roughness ◽  
Energy Density ◽  
Process Parameters ◽  
Vickers Hardness ◽  
Powder Bed Fusion ◽  
Processing Conditions ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Fractional Density ◽  
Experimental Variation

Laser powder bed fusion (LPBF) can fabricate products with tailored mechanical and surface properties. In fact, surface texture, roughness, pore size, the resulting fractional density, and microhardness highly depend on the processing conditions, which are very difficult to deal with. Therefore, this paper aims at investigating the relevance of the volumetric energy density (VED) that is a concise index of some governing factors with a potential operational use. This paper proves the fact that the observed experimental variation in the surface roughness, number and size of pores, the fractional density, and Vickers hardness can be explained in terms of VED that can help the investigator in dealing with several process parameters at once.

scholarly journals Understanding Laser Powder Bed Fusion Surface Roughness

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Jacob C. Snyder ◽  
Karen A. Thole
Keyword(s):  
Experimental Study ◽  
Surface Roughness ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Comprehensive Understanding ◽  
Powder Bed ◽  
Manufacturing Methods ◽  
New Framework

Abstract Surface roughness is a well-known consequence of additive manufacturing methods, particularly powder bed fusion processes. To properly design parts for additive manufacturing, a comprehensive understanding of the inherent roughness is necessary. While many researchers have measured different surface roughness resultant from a variety of parameters in the laser powder bed fusion process, few have succeeded in determining causal relationships due to the large number of variables at play. To assist the community in understanding the roughness in laser powder bed fusion processes, this study explored several studies from the literature to identify common trends and discrepancies amongst roughness data. Then, an experimental study was carried out to explore the influence of certain process parameters on surface roughness. Through these comparisons, certain local and global roughness trends have been identified and discussed, as well as a new framework for considering the effect of process parameters on surface roughness.

Experimental investigation on the effect of laser powder bed fusion process variables on dimensional accuracy, surface roughness and cylindricity of the AlSi10Mg alloy component

2021 ◽  
pp. 095440892110390
Author(s):  
Nagendra K Maurya ◽  
Ashish K Srivastava ◽  
Ambuj Saxena ◽  
Shashi P Dwivedi ◽  
Mashood Ashraf Ali ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Linear Dimension ◽  
Dimensional Accuracy ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Process Variables ◽  
Connecting Rod ◽  
Powder Bed ◽  
Response Variables

The present study deals with the influence of laser powder bed fusion process parameters on the selected linear dimension, surface roughness and cylindricity of AlSi10Mg alloy for manufacturing of a prototype connecting rod. The process variables used in this investigation are laser power, laser velocity, layer thickness and scanning speed. Response surface methodology is used to perform experiments and data analysis. The levels of process parameters are same that is, five for all the selected input process variables. An automotive component connecting rod is used as a component to analyze the effect of process variables on selected response variables. The optimum sating of process variables are different for dimensional accuracy, surface roughness and cylindricity. Minitab 14 software is used for the data analysis. The international tolerance grades of confirmation experiments are calculated as per the ISO standard UNI EN 20286-I and DIN 16901. A quadratic regression models are developed to estimate the response variables in terms of process parameters. The model is adequate within the experimental domain. X-chart of confirmation experiments is plotted. The deviation in the linear dimension is within the limit of ±3 sigma (σ). The lowest values of response variables at the best level of process parameters are obtained, that is, percentage error in dimensional accuracy of 2.65%, surface roughness of 2.57 µm and cylindricity of 0.09 mm. The novelty of this work lies in the fact that only a few studies have been conducted related to the form errors in the archival literature.

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 Comparison of Phase Characteristics and Residual Stresses in Ti-6Al-4V Alloy Manufactured by Laser Powder Bed Fusion (L-PBF) and Electron Beam Powder Bed Fusion (EB-PBF) Techniques

Crystals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 796
Author(s):  
Aya Takase ◽  
Takuya Ishimoto ◽  
Naotaka Morita ◽  
Naoko Ikeo ◽  
Takayoshi Nakano
Keyword(s):  
Electron Beam ◽  
Residual Stresses ◽  
Cooling Rate ◽  
Process Parameters ◽  
Phase Evolution ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Solidification Temperature ◽  
Powder Bed ◽  
Β Phase

Ti-6Al-4V alloy fabricated by laser powder bed fusion (L-PBF) and electron beam powder bed fusion (EB-PBF) techniques have been studied for applications ranging from medicine to aviation. The fabrication technique is often selected based on the part size and fabrication speed, while less attention is paid to the differences in the physicochemical properties. Especially, the relationship between the evolution of α, α’, and β phases in as-grown parts and the fabrication techniques is unclear. This work systematically and quantitatively investigates how L-PBF and EB-PBF and their process parameters affect the phase evolution of Ti-6Al-4V and residual stresses in the final parts. This is the first report demonstrating the correlations among measured parameters, indicating the lattice strain reduces, and c/a increases, shifting from an α’ to α+β or α structure as the crystallite size of the α or α’ phase increases. The experimental results combined with heat-transfer simulation indicate the cooling rate near the β transus temperature dictates the resulting phase characteristics, whereas the residual stress depends on the cooling rate immediately below the solidification temperature. This study provides new insights into the previously unknown differences in the α, α’, and β phase evolution between L-PBF and EB-PBF and their process parameters.

scholarly journals Role of laser powder bed fusion process parameters in crystallographic texture of additive manufactured Nb-48Ti alloy

2021 ◽  
Author(s):  
Rafael de Moura Nobre ◽  
Willy Ank de Morais ◽  
Matheus Tavares Vasques ◽  
Jhoan Guzmán ◽  
Daniel Luiz Rodrigues Junior ◽  
...  
Keyword(s):  
Process Parameters ◽  
Crystallographic Texture ◽  
Fusion Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

scholarly journals Optimizing Laser Powder Bed Fusion Parameters for IN-738LC by Response Surface Method

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4879
Author(s):  
Mireia Vilanova ◽  
Rubén Escribano-García ◽  
Teresa Guraya ◽  
Maria San Sebastian
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Process Parameters ◽  
Crack Density ◽  
Processing Parameters ◽  
Optimum Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Surface Method

A method to find the optimum process parameters for manufacturing nickel-based superalloy Inconel 738LC by laser powder bed fusion (LPBF) technology is presented. This material is known to form cracks during its processing by LPBF technology; thus, process parameters have to be optimized to get a high quality product. In this work, the objective of the optimization was to obtain samples with fewer pores and cracks. A design of experiments (DoE) technique was implemented to define the reduced set of samples. Each sample was manufactured by LPBF with a specific combination of laser power, laser scan speed, hatch distance and scan strategy parameters. Using the porosity and crack density results obtained from the DoE samples, quadratic models were fitted, which allowed identifying the optimal working point by applying the response surface method (RSM). Finally, five samples with the predicted optimal processing parameters were fabricated. The examination of these samples showed that it was possible to manufacture IN738LC samples free of cracks and with a porosity percentage below 0.1%. Therefore, it was demonstrated that RSM is suitable for obtaining optimum process parameters for IN738LC alloy manufacturing by LPBF technology.

A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion

2021 ◽  
Author(s):  
Yong Ren ◽  
Qian Wang ◽  
Panagiotis (Pan) Michaleris
Keyword(s):  
Machine Learning ◽  
Initial Temperature ◽  
Pool Size ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Input Feature ◽  
Melt Pool Size ◽  
Melt Pool Characteristics

Abstract Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.

Sign in / Sign up

Export Citation Format

Share Document