Camera signal dependencies within coaxial melt pool monitoring in laser powder bed fusion

Abstract

Purpose The purpose of this paper is to analyse the signal dependency of the camera-based coaxial monitoring system QMMeltpool 3D (Concept Laser GmbH, Lichtenfels, Germany) for laser powder bed fusion (LPBF) under the variation of process parameters, position, direction and layer thickness to determine the capability of the system. Because such and similar monitoring systems are designed and presented for quality assurance in series production, it is important to present the dominant signal influences and limitations. Design/methodology/approach Hardware of the commercially available coaxial monitoring QMMeltpool 3D is used to investigate the thermal emission of the interaction zone during LPBF. The raw images of the camera are analysed by means of image processing to bypass the software of QMMeltpool 3D and to gain a high level of signal understanding. Laser power, scan speed, laser spot diameter and powder layer thickness were varied for single-melt tracks to determine the influence of a parameter variation on the measured sensory signals. The effects of the scan direction and position were also analysed in detail. The influence of surface roughness on the detected sensory signals was simulated by a machined substrate plate. Findings Parameter variations are confirmed to be detectable. Because of strong directional and positional dependencies of the melt-pool monitoring signal a calibration algorithm is necessary. A decreasing signal is detected for increasing layer thickness. Surface roughness is identified as a dominating factor with major influence on the melt-pool monitoring signal exceeding other process flaws. Research limitations/implications This work was performed with the hardware of a commercially available QMMeltpool 3D system of an LPBF machine M2 of the company Concept Laser GmbH. The results are relevant for all melt-pool monitoring research activities connected to LPBF, as well as for end users and serial production. Originality/value Surface roughness has not yet been revealed as being one of the most important origins for signal deviations in coaxial melt-pool monitoring. To the best of the authors’ knowledge, the direct comparison of influences because of parameters and environment has not been published to this extent. The detection, evaluation and remelting of surface roughness constitute a plausible workflow for closed-loop control in LPBF.

Pyrometric-Based Melt Pool Monitoring Study of CuCr1Zr Processed Using L-PBF

“…A summary of in situ methods is provided in several reviews (e.g., References [ 17 , 18 , 19 , 20 , 21 ]). Most sensor techniques are related to either optical (e.g., References [ 16 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]) or acoustic methods (e.g., References [ 32 , 33 , 34 , 35 , 36 ]), which were often originally applied to laser welding [ 37 ]. Other methods such as low coherence interferometry [ 38 , 39 ], electrical methods (e.g., capacitance monitoring) [ 37 ], X-Ray backscatter imaging [ 40 ], and eddy current measurements [ 41 ] have been investigated.…”

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review

“…Table 6 and Table 7 summarise and classify the literature in this field. Kolb et al, 2020;Vasileska et al, 2020;Forien et al, 2020;Lane and Yeung, 2019: Demir et al, 2019Fisher et al, 2018;Kolb et al, 2018Kolb et al, , 2018bFox et al, 2017 Co-axial A c c e p t e d M a n u s c r i p t Zhirnov et al, 2020Heigel et al, 2020a,b Scime and Beuth, 2019Bruna-Rosso et al, 2018Mitchell et al, 2020 Section 3.4.1. and Section 3.4.2. review spatially integrated and spatially resolved methods, respectively. The reader is also referred to Liu et al (2020c) for a review of patents devoted to melt pool temperature monitoring.…”

“…A large number of studies has been devoted to the effect of different process parameters on melt pool properties. An increase of the energy input to the material was shown to cause an increase of the melt pool thermal emission, the size and peak radiance of melt pool temperature isotherms, melt pool area, lengths and width (Alberts et al 2017, Yuan et al 2018, Forien et al 2020, Kolb et al 2020, Kwon et al 2020. The amplitude of co-axial pyrometer signals could be used to identify transitions between conduction and key hole mode laser processing conditions (Forien et al 2020).…”

“…Demir et al (2019) also showed that the energy density is not sufficient to describe the melting behaviour, as under the same energy density continuous and pulsed mode emission regimes resulted in different melt pool dimensions. Kolb et al (2018aKolb et al ( , 2018bKolb et al ( , 2020 showed that the melt pool properties are affected also by the surface roughness of the consolidated material beneath the current layer. The salient mechanisms of laser-material interactions, convective motions, penetration depth variation, powder consolidation and pore formation for different sets of process parameters were highlighted in depth via in-situ x-ray video imaging (Leung et al 2018, Bobel et al 2019, Martin et al 2019.…”

An integrated fuzzy logic and machine learning platform for porosity detection using optical tomography imaging during laser powder bed fusion

“…In-situ sensors are generally set up in one of two configurations: coaxially [20][21][22][23][24][25][26][27], aligned with the laser beam's path, or off-axis [23,[28][29][30][31][32], placed away from the path at a specific angle. The primary tools for in-situ monitoring are optical devices (like high-speed digital and infrared (IR) thermal cameras), record critical aspects of the process, such as the melt-pool shape and brightness, alongside sequential surface imagery.…”