Machine-Learning Assisted Laser Powder Bed Fusion Process Optimization for AlSi10mg: New Microstructure Description Indices and Fracture Mechanisms

2020 ◽  
Author(s):  
Qian Liu ◽  
Hongkun Wu ◽  
Moses J. Paul ◽  
Peidong He ◽  
Zhongxiao Peng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Process Optimization ◽  
Fusion Process ◽  
Fracture Mechanisms ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

scholarly journals In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

Crystals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 524
Author(s):  
Pinku Yadav ◽  
Olivier Rigo ◽  
Corinne Arvieu ◽  
Emilie Le Guen ◽  
Eric Lacoste
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Fusion Process ◽  
Monitoring Systems ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
In Situ Sensing

In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion process still hinder risk-averse industries from adopting it wholeheartedly. The process-induced defects or drifts can have a detrimental effect on the quality of the final part, which could lead to catastrophic failure of the finished part. It led to the development of in situ monitoring systems to effectively monitor the process signatures during printing. Nevertheless, post-processing of the in situ data and defect detection in an automated fashion are major challenges. Nowadays, many studies have been focused on incorporating machine learning approaches to solve this problem and develop a feedback control loop system to monitor the process in real-time. In our study, we review the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection. We also discuss the working principles of the most common in situ sensing sensors to have a better understanding of the process. Commercially available in situ monitoring devices on laser powder bed fusion systems are also reviewed. This review is inspired by the work of Grasso and Colosimo, which presented an overall review of powder bed fusion technology.

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

Hot cracking in ZK60 magnesium alloy produced by laser powder bed fusion process

2021 ◽  
pp. 130283
Author(s):  
Jinge Liu ◽  
Bangzhao Yin ◽  
Zhaorui Sun ◽  
Peng Wen ◽  
Yufeng Zheng ◽  
...  
Keyword(s):  
Magnesium Alloy ◽  
Hot Cracking ◽  
Fusion Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Zk60 Magnesium Alloy

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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