In-situ Monitoring and Defect Detection for Laser Metal Deposition by Using Infrared Thermography

Abstract A preliminary experimental study on “warm ultrasonic impact-assisted laser metal deposition” (WUI-LMD) is reported, and such a study is rare in literatures to the authors' knowledge. In WUI-LMD, an ultrasonic impact treatment (UIT) tip is placed near laser spot for in-situ treatment of laser-deposited warm solid material, and the UIT and LMD processes proceed simultaneously. Under the conditions investigated, it is found that in-situ UIT during WUI-LMD can be much more effective in reducing porosity than a post-process UIT. Possible underlying mechanisms are analyzed. WUI-LMD has a great potential to reduce defects and improve mechanical properties without increasing manufacturing time.

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In-situ synthesis via laser metal deposition of a lean Cu–3.4Cr–0.6Nb (at%) conductive alloy hardened by Cr nano-scale precipitates and by Laves phase micro-particles

Acta Materialia ◽

10.1016/j.actamat.2020.07.035 ◽

2020 ◽

Vol 197 ◽

pp. 330-340 ◽

Cited By ~ 1

Author(s):

Anoop R Kini ◽

Dora Maischner ◽

Andreas Weisheit ◽

Dirk Ponge ◽

Baptiste Gault ◽

...

Keyword(s):

In Situ Synthesis ◽

Metal Deposition ◽

Laves Phase ◽

Laser Metal Deposition ◽

Nano Scale ◽

Micro Particles

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Vision-based defect detection in laser metal deposition process

Rapid Prototyping Journal ◽

10.1108/rpj-04-2012-0036 ◽

2014 ◽

Vol 20 (1) ◽

pp. 77-85 ◽

Cited By ~ 24

Author(s):

Shyam Barua ◽

Frank Liou ◽

Joseph Newkirk ◽

Todd Sparks

Keyword(s):

Defect Detection ◽

Detection System ◽

Vision System ◽

Low Cost ◽

Deposition Process ◽

Metal Deposition ◽

Cooling Curve ◽

Laser Metal Deposition ◽

Content Type ◽

Melt Pool

Purpose – Laser metal deposition (LMD) is a type of additive manufacturing process in which the laser is used to create a melt pool on a substrate to which metal powder is added. The powder is melted within the melt pool and solidified to form a deposited track. These deposited tracks may contain porosities or cracks which affect the functionality of the part. When these defects go undetected, they may cause failure of the part or below par performance in their applications. An on demand vision system is required to detect defects in the track as and when they are formed. This is especially crucial in LMD applications as the part being repaired is typically expensive. Using a defect detection system, it is possible to complete the LMD process in one run, thus minimizing cost. The purpose of this paper is to summarize the research on a low-cost vision system to study the deposition process and detect any thermal abnormalities which might signify the presence of a defect. Design/methodology/approach – During the LMD process, the track of deposited material behind the laser is incandescent due to heating by the laser; also, there is radiant heat distribution and flow on the surfaces of the track. An SLR camera is used to obtain images of the deposited track behind the melt pool. Using calibrated RGB values and radiant surface temperature, it is possible to approximate the temperature of each pixel in the image. The deposited track loses heat gradually through conduction, convection and radiation. A defect-free deposit should show a gradual decrease in temperature which enables the authors to obtain a reference cooling curve using standard deposition parameters. A defect, such as a crack or porosity, leads to an increase in temperature around the defective region due to interruption of heat flow. This leads to deviation from the reference cooling curve which alerts the authors to the presence of a defect. Findings – The temperature gradient was obtained across the deposited track during LMD. Linear least squares curve fitting was performed and residual values were calculated between experimental temperature values and line of best fit. Porosity defects and cracks were simulated on the substrate during LMD and irregularities in the temperature gradients were used to develop a defect detection model. Originality/value – Previous approaches to defect detection in LMD typically concentrate on the melt pool temperature and dimensions. Due to the dynamic and violent nature of the melt pool, consistent and reliable defect detection is difficult. An alternative method of defect detection is discussed which does not involve the melt pool and therefore presents a novel method of detecting a defect in LMD.

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Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

Applied Sciences ◽

10.3390/app112411910 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11910

Author(s):

Dalia Mahmoud ◽

Marcin Magolon ◽

Jan Boer ◽

M.A Elbestawi ◽

Mohammad Ghayoomi Mohammadi

Keyword(s):

Machine Learning ◽

Defect Detection ◽

In Situ Monitoring ◽

Sensor Data ◽

Learning Approaches ◽

Monitoring And Control ◽

In Situ Sensors ◽

Applications Of Machine Learning ◽

And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

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Fabrication of Metal Matrix Composite by Laser Metal Deposition—A New Process Approach by Direct Dry Injection of Nanopowders

Materials ◽

10.3390/ma12213584 ◽

2019 ◽

Vol 12 (21) ◽

pp. 3584 ◽

Cited By ~ 2

Author(s):

Briac Lanfant ◽

Florian Bär ◽

Antaryami Mohanta ◽

Marc Leparoux

Keyword(s):

Metal Matrix ◽

Metallic Matrix ◽

Process Approach ◽

Metal Deposition ◽

Al2o3 Nanoparticles ◽

Laser Metal Deposition ◽

Metal Matrix Nanocomposites ◽

X Ray ◽

New Process

Laser Metal Deposition (LMD) offers new perspectives for the fabrication of metal matrix nanocomposites (MMnCs). Current methods to produce MMnCs by LMD systematically involve the premixing of the nanopowders and the micropowders or require in-situ strategies, thereby restricting the possibilities to adjust the nature, content and location of the nano-reinforcement during printing. The objective of this study is to overcome such restrictions and propose a new process approach by direct injection of nanoparticles into a metallic matrix. Alumina (n-Al2O3) nanoparticles were introduced into a titanium matrix by using two different direct dry injection modes in order to locally increase the hardness. Energy dispersive X-ray spectroscopy (EDS) analyses validate the successful incorporation of the n-Al2O3 at chosen locations. Optical and high resolution transmission electron microscopic (HR-TEM) observations as well as X-ray diffraction (XRD) analyses indicate that n-Al2O3 powders are partly or totally dissolved into the Ti melted pool leading to the in-situ formation of a composite consisting of fine α2 lamellar microstructure within a Ti matrix and a solid solution with oxygen. Mechanical tests show a significant increase in hardness with the increase of injected n-Al2O3 amount. A maximum of 620 HV was measured that is almost 4 times higher than the pure LMD-printed Ti structure.

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Influence of delay strategies and residual heat on in-situ tempering in the laser metal deposition of 300M high strength steel

Surface and Coatings Technology ◽

10.1016/j.surfcoat.2019.125279 ◽

2020 ◽

Vol 383 ◽

pp. 125279 ◽

Cited By ~ 1

Author(s):

Cameron Barr ◽

Shi Da Sun ◽

Mark Easton ◽

Nicholas Orchowski ◽

Neil Matthews ◽

...

Keyword(s):

High Strength Steel ◽

High Strength ◽

Metal Deposition ◽

Laser Metal Deposition ◽

Residual Heat

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

Crystals ◽

10.3390/cryst10060524 ◽

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.

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Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

Journal of Intelligent Manufacturing ◽

10.1007/s10845-021-01787-y ◽

2021 ◽

Author(s):

Matteo Bugatti ◽

Bianca Maria Colosimo

Keyword(s):

Additive Manufacturing ◽

Real Time ◽

Process Monitoring ◽

Defect Detection ◽

High Speed ◽

Data Extraction ◽

In Situ Monitoring ◽

Imaging Data ◽

Computational Speed

AbstractThe increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection.

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