Spectroscopic monitoring and melt pool temperature estimation during the laser metal deposition process

2016 ◽  
Vol 28 (2) ◽  
pp. 022303 ◽  
Author(s):  
Dieter De Baere ◽  
Wim Devesse ◽  
Ben De Pauw ◽  
Lien Smeesters ◽  
Hugo Thienpont ◽  
...  
Keyword(s):  
Deposition Process ◽  
Metal Deposition ◽  
Laser Metal Deposition ◽  
Temperature Estimation ◽  
Spectroscopic Monitoring ◽  
Melt Pool ◽  
Melt Pool Temperature

scholarly journals Spatial distributed spectroscopic monitoring of melt pool and vapor plume during the laser metal deposition process

Procedia CIRP ◽  
2020 ◽  
Vol 94 ◽  
pp. 445-450
Author(s):  
Dieter De Baere ◽  
Wim Devesse ◽  
Jan Helsen ◽  
Patrick Guillaume
Keyword(s):  
Deposition Process ◽  
Metal Deposition ◽  
Laser Metal Deposition ◽  
Spectroscopic Monitoring ◽  
Melt Pool ◽  
Vapor Plume

scholarly journals Study of the Influence of Shielding Gases on Laser Metal Deposition of Inconel 718 Superalloy

Materials ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1388 ◽  
Author(s):  
Jose Ruiz ◽  
Magdalena Cortina ◽  
Jon Arrizubieta ◽  
Aitzol Lamikiz
Keyword(s):  
Metal Deposition ◽  
Slight Reduction ◽  
Laser Metal Deposition ◽  
Melt Pool ◽  
Industrial Sectors ◽  
Inconel 718 Superalloy ◽  
Melt Pool Temperature ◽  
Different Gases ◽  
Die And Mold

The use of the Laser Metal Deposition (LMD) technology as a manufacturing and repairing technique in industrial sectors like the die and mold and aerospace is increasing within the last decades. Research carried out in the field of LMD process situates argon as the most usual inert gas, followed by nitrogen. Some leading companies have started to use helium and argon as carrier and shielding gas, respectively. There is therefore a pressing need to know how the use of different gases may affect the LMD process due there being a lack of knowledge with regard to gas mixtures. The aim of the present work is to evaluate the influence of a mixture of argon and helium on the LMD process by analyzing single tracks of deposited material. For this purpose, special attention is paid to the melt pool temperature, as well as to the characterization of the deposited clads. The increment of helium concentration in the gases of the LMD processes based on argon will have three effects. The first one is a slight reduction of the height of the clads. Second, an increase of the temperature of the melt pool. Last, smaller wet angles are obtained for higher helium concentrations.

Vision-based defect detection in laser metal deposition process

2014 ◽  
Vol 20 (1) ◽  
pp. 77-85 ◽  
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.

Melt Pool Temperature Control for Laser Metal Deposition Processes—Part I: Online Temperature Control

2010 ◽  
Vol 132 (1) ◽  
Author(s):  
Lie Tang ◽  
Robert G. Landers
Keyword(s):  
Temperature Control ◽  
Process Parameters ◽  
Process Model ◽  
Dynamic Balance ◽  
Metal Deposition ◽  
Time Varying ◽  
Laser Metal Deposition ◽  
Melt Pool ◽  
Melt Pool Temperature ◽  
Deposition Processes

Melt pool temperature is of great importance to deposition quality in laser metal deposition processes. To control the melt pool temperature, an empirical process model describing the relationship between the temperature and process parameters (i.e., laser power, powder flow rate, and traverse speed) is established and verified experimentally. A general tracking controller using the internal model principle is then designed. To examine the controller performance, three sets of experiments tracking both constant and time-varying temperature references are conducted. The results show the melt pool temperature controller performs well in tracking both constant and time-varying temperature references even when process parameters vary significantly. However a multilayer deposition experiment illustrates that maintaining a constant melt pool temperature does not necessarily lead to uniform track morphology, which is an important criteria for deposition quality. The reason is believed to be that different melt pool morphologies may have the same temperature depending on the dynamic balance of heat input and heat loss.

Melt Pool Temperature Control for Laser Metal Deposition Processes—Part II: Layer-to-Layer Temperature Control

2010 ◽  
Vol 132 (1) ◽  
Author(s):  
Lie Tang ◽  
Robert G. Landers
Keyword(s):  
Temperature Control ◽  
Heat Input ◽  
Laser Power ◽  
Process Model ◽  
Metal Deposition ◽  
Laser Metal Deposition ◽  
Melt Pool ◽  
Power Profile ◽  
Melt Pool Temperature ◽  
Deposition Processes

Heat input regulation is crucial for deposition quality in laser metal deposition (LMD) processes. To control the heat input, melt pool temperature is regulated using temperature controllers. Part I of this paper showed that, although online melt pool temperature control performs well in terms of tracking the temperature reference, it cannot guarantee consistent track morphology. Therefore, a new methodology, known as layer-to-layer temperature control, is proposed in this paper. The idea of layer-to-layer temperature control is to adjust the laser power profile between layers. The part height profile is measured between layers, and the temperature is measured online. The data are then utilized to identify the parameters of a LMD process model using particle swarm optimization. The laser power profile is then computed using iterative learning control, based on the estimated process model and the reference melt pool temperature of the next layer. The deposition results show that the layer-to-layer temperature controller is capable of not only tracking the reference temperature, but also producing a consistent track morphology.

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