scholarly journals Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model

JOM ◽  
2019 ◽  
Vol 72 (1) ◽  
pp. 420-428 ◽  
Author(s):  
Lingbin Meng ◽  
Jing Zhang
Keyword(s):  
Machine Learning ◽  
Stainless Steel ◽  
Gaussian Process ◽  
Process Design ◽  
Learning Model ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Machine Learning Model

scholarly journals Automatic Quality Assessments of Laser Powder Bed Fusion Builds from Photodiode Sensor Measurements

2020 ◽  
Author(s):  
Sarini Jayasinghe ◽  
Paolo Paoletti ◽  
Chris Sutcliffe ◽  
John Dardis ◽  
Nick Jones ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Learning Algorithms ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Part Density

This study evaluates whether a combination of photodiode sensor measurements, taken during laser powder bed fusion (L-PBF) builds, can be used to predict the resulting build quality via a purely data-based approach. We analyse the relationship between build density and features that are extracted from sensor data collected from three different photodiodes. The study uses a Singular Value Decomposition to extract lower-dimensional features from photodiode measurements, which are then fed into machine learning algorithms. Several unsupervised learning methods are then employed to classify low density (< 99% part density) and high density (≥ 99% part density) specimens. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density. Using the unsupervised clustering approaches, applied to features extracted from both photodiode sensor data as well as observations relating to the energy transferred to the material, build density was predicted with up to 93.54% accuracy. With regard to the supervised regression approach, a Gaussian Process algorithm was capable of predicting the build density with a RMS error of 3.65%. The study shows, therefore, that there is potential for machine learning algorithms to predict indicators of L-PBF build quality from photodiode build-measurements. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantification in machine-learnt predictions of L-PBF build quality.

Fracture toughness of 304L austenitic stainless steel produced by laser powder bed fusion

2021 ◽  
Vol 202 ◽  
pp. 114002
Author(s):  
Punit Kumar ◽  
Zhiguang Zhu ◽  
Sharon M.L. Nai ◽  
R.L. Narayan ◽  
U. Ramamurty
Keyword(s):  
Fracture Toughness ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

Crystallographic orientation dependence of Charpy impact behaviours in stainless steel 316L fabricated by laser powder bed fusion

2021 ◽  
pp. 102104
Author(s):  
Xianglong Wang ◽  
Oscar Sanchez-Mata ◽  
Sıla Ece Atabay ◽  
Jose Alberto Muñiz-Lerma ◽  
Mohammad Attarian Shandiz ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Crystallographic Orientation ◽  
Charpy Impact ◽  
Orientation Dependence ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Stainless Steel 316L ◽  
Powder Bed

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.

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