In Situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion Using Deep Learning

2019 ◽  
Vol 3 (1) ◽  
pp. 20190027 ◽  
Author(s):  
Aniruddha Gaikwad ◽  
Farhad Imani ◽  
Hui Yang ◽  
Edward Reutzel ◽  
Prahalada Rao
Keyword(s):  
Deep Learning ◽  
In Situ Monitoring ◽  
Thin Wall ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

On the use of spatter signature for in-situ monitoring of Laser Powder Bed Fusion

2017 ◽  
Vol 16 ◽  
pp. 35-48 ◽  
Author(s):  
Giulia Repossini ◽  
Vittorio Laguzza ◽  
Marco Grasso ◽  
Bianca Maria Colosimo
Keyword(s):  
In Situ Monitoring ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

In-Situ Monitoring of Laser Powder Bed Fusion Process Anomalies via a Comprehensive Analysis of Off-Axis Camera Data

2020 ◽  
Author(s):  
Chaitanya Krishna Prasad Vallabh ◽  
Yubo Xiong ◽  
Xiayun Zhao
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Video Data ◽  
In Situ Monitoring ◽  
Process Efficiency ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Monitoring Method ◽  
Powder Bed

Abstract In-situ monitoring of a Laser Powder-Bed Fusion (LPBF) additive manufacturing process is crucial in enhancing the process efficiency and ensuring the built part integrity. In this work, we present an in-situ monitoring method using an off-axis camera for monitoring layer-wise process anomalies. The in-situ monitoring is performed with a spatial resolution of 512 × 512 pixels, with each pixel representing 250 × 250 μm and a relatively high data acquisition rate of 500 Hz. An experimental study is conducted by using the developed in-situ off-axis method for monitoring the build process for a standard tensile bar. Real-time video data is acquired for each printed layer. Data analytics methods are developed to identify layer-wise anomalies, observe powder bed characteristics, reconstruct 3D part structure, and track the spatter dynamics. A deep neural network architecture is trained using the acquired layer-wise images and tested by images embedded with artificial anomalies. The real-time video data is also used to perform a preliminary spatter analysis along the laser scan path. The developed methodology is aimed to extract as much information as possible from a single set of camera video data. It will provide the AM community with an efficient and capable process monitoring tool for process control and quality assurance while using LPBF to produce high-standard components in industrial (such as, aerospace and biomedical industries) applications.

Design Rules and In-Situ Quality Monitoring of Thin-Wall Features Made Using Laser Powder Bed Fusion

2019 ◽  
Author(s):  
Aniruddha Gaikwad ◽  
Farhad Imani ◽  
Prahalad Rao ◽  
Hui Yang ◽  
Edward Reutzel
Keyword(s):  
Neural Network ◽  
Aspect Ratio ◽  
Convolutional Neural Network ◽  
Thin Wall ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Design Rules ◽  
Powder Bed

Abstract The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: 1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall (l) to its thickness (t) defined as the aspect ratio (length-to-thickness ratio, l/t), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine. 2) Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.

scholarly journals Linking In Situ Melt Pool Monitoring to Melt Pool Size Distributions and Internal Flaws in Laser Powder Bed Fusion

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1856
Author(s):  
Claudia Schwerz ◽  
Lars Nyborg
Keyword(s):  
Near Infrared ◽  
In Situ Monitoring ◽  
Ex Situ ◽  
Process Conditions ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Melt Pool Size

In situ monitoring of the melt pools in laser powder bed fusion (LPBF) has enabled the elucidation of process phenomena. There has been an increasing interest in also using melt pool monitoring to identify process anomalies and control the quality of the manufactured parts. However, a better understanding of the variability of melt pools and the relation to the incidence of internal flaws are necessary to achieve this goal. This study aims to link distributions of melt pool dimensions to internal flaws and signal characteristics obtained from melt pool monitoring. A process mapping approach is employed in the manufacturing of Hastelloy X, comprising a vast portion of the process space. Ex situ measurements of melt pool dimensions and analysis of internal flaws are correlated to the signal obtained through in situ melt pool monitoring in the visible and near-infrared spectra. It is found that the variability in melt pool dimensions is related to the presence of internal flaws, but scatter in melt pool dimensions is not detectable by the monitoring system employed in this study. The signal intensities are proportional to melt pool dimensions, and the signal is increasingly dynamic following process conditions that increase the generation of spatter.

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 In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Metals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 103 ◽  
Author(s):  
Gunther Mohr ◽  
Simon J. Altenburg ◽  
Alexander Ulbricht ◽  
Philipp Heinrich ◽  
Daniel Baum ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Data Analysis ◽  
Optical Tomography ◽  
In Situ Monitoring ◽  
Process Conditions ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Automatic Data ◽  
Powder Bed

Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented.

Vision-based in-situ monitoring system for melt-pool detection in laser powder bed fusion process

2021 ◽  
Vol 68 ◽  
pp. 1735-1745
Author(s):  
Trong-Nhan Le ◽  
Min-Hsun Lee ◽  
Ze-Hong Lin ◽  
Hong-Chuong Tran ◽  
Yu-Lung Lo
Keyword(s):  
Monitoring System ◽  
In Situ Monitoring ◽  
Fusion Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool
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