Predictive Manufacturability Assessment System for Laser Powder Bed Fusion Based on a Hybrid Machine Learning Model

2021 ◽  
pp. 101946
Author(s):  
Ying Zhang ◽  
Sheng Yang ◽  
Guoying Dong ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Assessment System ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Machine Learning Model ◽  
Hybrid Machine

scholarly journals Predicting heart disease using hybrid machine learning model

2021 ◽  
Vol 1916 (1) ◽  
pp. 012208
Author(s):  
G Renugadevi ◽  
G Asha Priya ◽  
B Dhivyaa Sankari ◽  
R Gowthamani
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Model ◽  
Machine Learning Model ◽  
Hybrid Machine

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.

scholarly journals Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model

2020 ◽  
Vol 2 (6) ◽  
Author(s):  
Rachel Cook ◽  
Keitumetse Cathrine Monyake ◽  
Muhammad Badar Hayat ◽  
Aditya Kumar ◽  
Lana Alagha
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Metal Sulfides ◽  
Machine Learning Model ◽  
Hybrid Machine
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