Melt Pool Acquisition Using Near-Infrared Camera in Aluminum Wire Arc Additive Manufacturing

Dellarre, Anthony; Limousin, Maxime; Béraud, Nicolas

doi:10.1007/978-3-031-15928-2_70

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Amid the importance of final bead geometry analysis, the challenge of online process control remains pronounced [25]. Infrared pyrometry techniques for melt-pool measurement have found favor within integrated process monitoring systems [26][27][28], some even hinting at symmetries in the generated geometries [9], thus offering a realm of exploration for the research community.…”

Section: Introductionmentioning

confidence: 99%

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Fernández-Zabalza,

Veiga,

Suárez

et al. 2024

Applied Sciences

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Having garnered significant attention in the scientific community over the past decade, wire-arc directed energy deposition (arc-DED) technology is at the heart of this investigation into additive manufacturing parameters. Singularly focused on Invar as the selected material, the primary objective revolves around devising a virtual sensor for the indirect size measurement of the bead. This innovative methodology involves the seamless integration of internal signals and sensors, enabling the derivation of crucial measurements sans the requirement for direct physical interaction or conventional measurement methodologies. The internal signals recorded, the comprising voltage, the current, the energy from the welding heat source generator, the wire feed speed from the feeding system, the traverse speed from the machine axes, and the temperature from a pyrometer located in the head were all captured through the control of the machine specially dedicated to the arc-DED process during a phase of optimizing and modeling the bead geometry. Finally, a feedforward neural network (FNN), also known as a multi-layer perceptron (MLP), is designed, with the internal signals serving as the input and the height and width of the bead constituting the output. Remarkably cost-effective, this solution circumvents the need for intricate measurements and significantly contributes to the proper layer-by-layer growth process. Furthermore, a neural network model is implemented with a test loss of 0.144 and a test accuracy of 1.0 in order to predict weld bead geometry based on process parameters, thus offering a promising approach for real-time monitoring and defect detection.

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Section: Introductionmentioning

confidence: 99%

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

Fernández-Zabalza,

Veiga,

Suárez

et al. 2024

Applied Sciences

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“…Several metrics can be monitored, such as temperature on a point [15] or welding current [14] but the melt pool seems a good indicator to predict defects [16]. The use of a near-infrared (NIR) camera (like a CMOS sensor) is identi ed as a suitable means to capture the melt pool shape [17]. From these images, it is possible to produce a thermal metric correlated to defects.…”

Section: Introductionmentioning

confidence: 99%

“…However, adapting these algorithms to aluminum alloys poses greater challenges. This is primarily because the thermal emission light from the aluminum melt pool is 1000-10,000 times lower than that of steel, and the boundaries of the aluminum melt pool are less distinct [17]. Figure 1 visually illustrates the contrast between two raw images captured during the manufacturing processes of aluminum alloy and steel.…”

Section: Introductionmentioning

confidence: 99%

A method to qualify image post-processing for thin wall thickness prediction from NIR camera image of aluminum WAAM process

Béraud,

Lombard,

Dellarre

et al. 2024

Preprint

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Wire Arc Additive Manufacturing (WAAM) is a metal arc welding additive process that allows the production of large parts. Managing the quality of produced parts is a key challenge to the adoption of this technology in the industry. A particular case of this is the production of aluminum thin walls and the management of their thicknesses. Literature supports that the use of a near-infrared camera is a good way to monitor the meltpool shape to predict the manufactured thickness. Nevertheless, the post-processing applied to the image has a big influence on the accuracy of the measure. That is why, this article proposes a method to qualify image post-processing for thin wall thickness prediction from near-infrared camera images of aluminum WAAM process. A dataset from the literature is used to evaluate the accuracy of different post-processes. First, a method is proposed to evaluate the accuracy of a given post-process. Secondly, two types of post-processing are presented: post-processing based on conventional image processing and post-processing based on neural networks. Their accuracy is evaluated with the proposed method. The article concludes that the proposed method is adapted to qualify post-processes and that the proposed post-processing based on neural networks gives significant results in terms of accuracy.

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Qualify a NIR camera to detect thermal deviation during aluminum WAAM

Dellarre

Béraud

Tardif

et al. 2023

Int J Adv Manuf Technol

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Melt Pool Acquisition Using Near-Infrared Camera in Aluminum Wire Arc Additive Manufacturing

Cited by 6 publications

References 17 publications

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

The Use of Virtual Sensors for Bead Size Measurements in Wire-Arc Directed Energy Deposition

A method to qualify image post-processing for thin wall thickness prediction from NIR camera image of aluminum WAAM process

Qualify a NIR camera to detect thermal deviation during aluminum WAAM

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