scholarly journals In Situ Monitoring of Powder Bed Fusion Homogeneity in Electron Beam Melting

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7015
Author(s):  
Marco Grasso

Increasing attention has been devoted in recent years to in situ sensing and monitoring of the electron beam melting process, ranging from seminal methods based on infrared imaging to novel methods based on backscattered electron detection. However, the range of available in situ monitoring capabilities and solutions is still quite limited compared to the wide number of studies and industrial toolkits in laser-based additive manufacturing processes. Some methods that are already industrially available in laser powder bed fusion systems, such as in situ detection of recoating errors, have not yet been investigated and tested in electron beam melting. Motivated by the attempt to fill this gap, we present a novel in situ monitoring methodology that can be easily implemented in industrial electron beam melting machines. The method is aimed at identifying local inhomogeneity and irregularities in the powder bed by means of layerwise image acquisition and processing, with no external illumination source apart from the light emitted by the hot material underneath the currently recoated layer. The results show that the proposed approach is suitable to detect powder bed anomalies, while also highlighting the link between the severity of in situ detected errors and the severity of resulting defects in the additively manufactured part.

Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 75
Author(s):  
Carmine Pirozzi ◽  
Stefania Franchitti ◽  
Rosario Borrelli ◽  
Antonio Chiariello ◽  
Luigi Di Palma

In this work a mechanical characterization of Ti6Al4V processed by electron beam powder bed fusion additive manufacturing was carried out to investigate the viability of this technology for the manufacturing of flyable parts for general aviation aircraft. Tests were performed on different manufacturing conditions in order to investigate the effect of post processing as machining on the mechanical behavior. The study provides useful information to airframe designers and manufacturing specialists that work with this technology. The investigation confirms the low process variability and provides data to be used in the design loop of general aviation primary structural elements. The test results show a high level of repeatability indicating that the process is well controlled and reliable enough to match the airworthiness requirements. In addition, the so-called “as-built specimens”, i.e., specimens produced by the electron beam melting machine without any major post-processing, have lower mechanical performances than specimens subjected to a machining phase after the electron beam melting process. Specific primary structural elements will be designed and flight cleared, resulting from the findings presented herein.


Crystals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 524
Author(s):  
Pinku Yadav ◽  
Olivier Rigo ◽  
Corinne Arvieu ◽  
Emilie Le Guen ◽  
Eric Lacoste

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.


2021 ◽  
Vol 38 ◽  
pp. 101767
Author(s):  
Guillaume Croset ◽  
Guilhem Martin ◽  
Charles Josserond ◽  
Pierre Lhuissier ◽  
Jean-Jacques Blandin ◽  
...  

2017 ◽  
Vol 16 ◽  
pp. 35-48 ◽  
Author(s):  
Giulia Repossini ◽  
Vittorio Laguzza ◽  
Marco Grasso ◽  
Bianca Maria Colosimo

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makiko Yonehara ◽  
Chika Kato ◽  
Toshi-Taka Ikeshoji ◽  
Koki Takeshita ◽  
Hideki Kyogoku

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.


2018 ◽  
Vol 22 ◽  
pp. 375-380 ◽  
Author(s):  
Pan Wang ◽  
Mui Ling Sharon Nai ◽  
Wai Jack Sin ◽  
Shenglu Lu ◽  
Baicheng Zhang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document