A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision

Lin, Zhenqiang; Lai, Yiwen; Pan, Tao; Zhang, Wang; Zheng, Jun; Xia, Ge; Liu, Yuangang

doi:10.3390/ma14154175

Cited by 20 publications

(10 citation statements)

References 18 publications

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“…The testing of the vision system might be realized in commercial SLM machines [14] or with our own developed powder feeder prototype [15]. In this work, our own powder feeder was constructed.…”

Section: Powder Feeder -Operating Principlementioning

confidence: 99%

Application of machine vision for the detection of powder bed defects in additive manufacturing processes

Korzeniowski,

Małachowska,

Wiatrzyk

2023

Materials Science-Poland

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The quality of the powder layers in the 3D printing process is extremely important and directly corresponds to the quality of the structures made with this technology. Therefore, it is essential to control it. It can be made in-line with a vision system combined with image processing algorithms, which can significantly improve control of the process and help with the adjustment of powder spreading systems, especially in case of difficult-to-feed powders like magnetic ones – e.g., Fe-based metallic glass powder – Fe56.04Co13.45Nb5.5B25. In this work, two algorithms – machine learning – Support Vector Machines (SVM), deep learning – Convolutional Neural Networks (CNN) – were evaluated for their ability to detect and classify the enumerated anomalies based on powder layer images. The SVM algorithm makes it possible to efficiently and quickly analyze the powder-spreading process. CNN, however, appears to be a more promising choice for the developed application, as they alleviate the need for complex image operations.

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Section: Powder Feeder -Operating Principlementioning

confidence: 99%

Application of machine vision for the detection of powder bed defects in additive manufacturing processes

Korzeniowski,

Małachowska,

Wiatrzyk

2023

Materials Science-Poland

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“…The literature describes pyrometry as a promising, affordable and robust candidate for melt pool recording as well as process monitoring in L-PBF (e.g., see McCann et al [ 2 ], Everton et al [ 6 ], Yang et al [ 8 ], Gutknecht et al [ 11 ] or Hooper [ 13 ]). Since most studies focus on the potential of using pyrometry for processing and quality monitoring (e.g., see [ 7 , 18 , 19 , 20 ]), the obtained signals are mainly interpreted as arbitrary units. Measuring temperatures instead of arbitrary units remains challenging due to the dependency of the emissivity from unknown factors such as temperature, surface condition and aggregate state.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Cooling Behavior of Melt Pools in L-PBF by Pyrometry

et al. 2023

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This study aims to measure the cooling rates or, more precisely, the cooling durations of single laser tracks by pyrometry within the laser powder bed fusion (L-PBF) process. Two-color, as well as one-color pyrometers are tested within this work. Regarding the second, the emissivity of the investigated 30CrMoNb5-2 alloy is determined in-situ within the L-PBF system in order to measure temperature instead of arbitrary units. This is done by heating up printed samples and verifying the measured pyrometer signal by comparing it to values obtained by thermocouples attached to the samples. In addition, the precision of two-color pyrometry is verified for the given setup. Following the verification experiments, single laser track experiments are conducted. The obtained signals prove to be partially distorted mainly due to by-products such as smoke and weld beads arising from the melt pool. To encounter this problem, a new fitting method is presented and experimentally validated. Melt pools resulting from different cooling durations are analyzed by EBSD. These measurements show areas of extreme deformation or potential amorphization correlating with the cooling durations. The obtained cooling duration can be used for the validation of simulations as well as for the correlation of corresponding microstructure and process parameters.

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“…Additive manufacture (AM) technology (also known as 3D-printing technology) is a new production technology developed in the late 1980s [ 1 , 2 , 3 ]; the processing principle is the opposite of traditional additive and subtractive production technology [ 4 ], with a parts production process using layer-by-layer stacking production process [ 5 ]. The technology offers design flexibility, the printing of complex components [ 6 ], and lightweight, personalized design [ 7 ]; Mg, Cu, Fe, Al, Mo, and other metals and their alloys can be formed [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], so it is widely used in the aerospace, energy, biomedical, and automotive industries and other fields of metal parts manufacturing [ 22 ]. The metal manufacturing processes mainly include equal-material manufacturing, subtractive manufacturing, and additive manufacturing, as shown in Figure 1 [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Review of Visual Measurement Methods for Metal Vaporization Processes in Laser Powder Bed Fusion

et al. 2023

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Laser powder bed fusion (LPBF) is of great importance for the visual measurement and analysis of the metallization process, which is the process of solid, liquid, and gas phase transformations of metal powders under high-energy laser irradiation due to the low boiling point/high saturated vapor pressure. Since the evaporation of metals involves the interaction of driving forces such as vapor back pressure, surface tension, and gravity, the movement of the melt pool is not stable. At the same time, it also produces vaporization products such as vapor plumes and sprays, which cause defects such as bubbles, porosity, lack of fusion, inclusions, etc., during the manufacturing process of the parts, affecting the performance and manufacturing quality of the parts. More and more researchers are using imaging technologies, such as high-speed X-ray, high-speed visible light cameras, and high-speed schlieren imaging, to perform noncontact visual measurements and analyses of the melt pool, vapor plume, and spatter during the metal evaporation process, and the results show that the metal evaporation process can be suppressed by optimizing the process parameters and changing the processing atmosphere, thereby reducing part defects and improving part performance and built part quality. This paper reviews the research on metal evaporation mechanisms and visual measurement methods of metal evaporation, then discusses the measures of metal evaporation, and finally summarizes and prospects the future research hotspots of LPBF technology, according to the existing scholars’ research on numerical simulation analysis and visual measurement methods of the metal evaporation process.

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A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision

Cited by 20 publications

References 18 publications

Application of machine vision for the detection of powder bed defects in additive manufacturing processes

Application of machine vision for the detection of powder bed defects in additive manufacturing processes

Measuring the Cooling Behavior of Melt Pools in L-PBF by Pyrometry

Review of Visual Measurement Methods for Metal Vaporization Processes in Laser Powder Bed Fusion

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