Optical in-situ monitoring and correlation of density and mechanical properties of stainless steel parts produced by selective laser melting process based on varied energy density

Lu, Qingyang; Nguyen, N.V.; Hum, Allen Jun Wee; Tran, Tuan; Wong, Chee How

doi:10.1016/j.jmatprotec.2019.04.026

Cited by 38 publications

(23 citation statements)

References 11 publications

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“…As shown in Figs. 19(a) and (b), Lu et al [70] built a set of in situ monitoring devices for SLM process. Light emitting diode (LED) light strips were installed onto the chamber walls of the…”

Section: Special Reviewmentioning

confidence: 99%

“…18: Schematic diagram of paraxial image acquisition system built in EOS M290 [62] (a), original image captured [62] (b), result of brightness enhancement of captured image [62] (c), image of powder spreading layer with defects [63] (d),examples of six types of defects in powder spread layer [62,63] (e), schematic diagram of defect classification algorithm based on visual word bag and KNN [62] (f), example of defect detection results in scanned layer image [62] (g), multi-scale CNN structure for defect classification [63] (h), confusion matrix representing the performance of MsCNN [63] (i), example of defect detection results in powder spread layer image [63] (j) machine to achieve uniform lighting during image capturing. The Nikon D500 DSLR camera was used to capture optical images during the printing process [70] . Ten samples were formed under different process parameters by changing laser power and scanning speed, and the images of powder spread layer and scanned layer during the process were collected, as shown in Figs.…”

Section: Special Reviewmentioning

confidence: 99%

“…Ten samples were formed under different process parameters by changing laser power and scanning speed, and the images of powder spread layer and scanned layer during the process were collected, as shown in Figs. 19(c (a) illumination layout designed for in situ monitoring during SLM process [70] ; (b) schematic diagram of image acquisition system [70] ; (c) image of scanned layer with defects [70] ; (d) image of scanned layer with black slag [70] ;…”

Section: Special Reviewmentioning

confidence: 99%

“…(e) image of uneven spread powder layer [70] ; (f) relationship between results of image quantitative analysis and density [70] ; (g) relationship between results of image quantitative analysis and mechanical properties [70] ;…”

Section: Special Reviewmentioning

confidence: 99%

See 3 more Smart Citations

In situ monitoring methods for selective laser melting additive manufacturing process based on images — A review

Zhou

et al. 2021

China Foundry

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Selective laser melting (SLM) has been widely used in the fields of aviation, aerospace and die manufacturing due to its ability to produce metal components with arbitrarily complex shapes. However, the instability of SLM process often leads to quality fluctuation of the formed component, which hinders the further development and application of SLM. In situ quality control during SLM process is an effective solution to the quality fluctuation of formed components. However, the basic premise of feedback control during SLM process is the rapid and accurate diagnosis of the quality. Therefore, an in situ monitoring method of SLM process, which provides quality diagnosis information for feedback control, became one of the research hotspots in this field in recent years. In this paper, the research progress of in situ monitoring during SLM process based on images is reviewed. Firstly, the significance of in situ monitoring during SLM process is analyzed. Then, the image information source of SLM process, the image acquisition systems for different detection objects (the molten pool region, the scanned layer and the powder spread layer) and the methods of the image information analysis, detection and recognition are reviewed and analyzed. Through review and analysis, it is found that the existing image analysis and detection methods during SLM process are mainly based on traditional image processing methods combined with traditional machine learning models. Finally, the main development direction of in situ monitoring during SLM process is proposed by combining with the frontier technology of image-based computer vision.

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Section: Special Reviewmentioning

confidence: 99%

Section: Special Reviewmentioning

confidence: 99%

Section: Special Reviewmentioning

confidence: 99%

Section: Special Reviewmentioning

confidence: 99%

See 2 more Smart Citations

In situ monitoring methods for selective laser melting additive manufacturing process based on images — A review

Zhou

et al. 2021

China Foundry

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show abstract

“…This study focuses on the first level. In this framework, increasing attention has been devoted to the use of layerwise images of the powder bed either to identify recoating errors and powder bed in-homogeneities [4,5,6] or to identify surface and geometrical defects in the printed area [7,8,9,10,11,12,13]. Some of these methods are particularly appealing for a direct industrial implementation, as in most cases they exploit in-situ sensing architectures that are either already available in industrial L-PBF systems or easy to integrate [3].…”

Section: Introductionmentioning

confidence: 99%

Automated layerwise detection of geometrical distortions in laser powder bed fusion

Pagani

Grasso

Scott

et al. 2020

Additive Manufacturing

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In-situ layerwise imaging in laser powder bed fusion (L-PBF) has been implemented by many system developers to monitor the powder bed homogeneity. Increasing attention has been recently devoted to the possibility of using the same sensing approach to detect also in-plane and out-of-plane geometrical distortions of the part while it is being produced. To this aim, seminal works investigated the suitability of various image segmentation algorithms and assessed the accuracy of layerwise dimensional and geometrical measurements. Nevertheless, there is a lack of automated methods to identify, in-situ and in-process, geometrical defects and out-of-control deviations from the nominal geometry. This study presents a methodology that combines an active contours methodology for image segmentation with a statistical process monitoring approach suitable to deal with complex geometries that change layer by layer. The proposed approach enables a data-driven and automated alarm rule to detect the onset of geometrical distortions during the build by comparing the slice contour reconstruction with the nominal geometry in each layer. Moreover, by coupling edge-based and region-based segmentation techniques, the method is sufficiently robust to be applied to imaging and illumination setups that are already available on industrial L-PBF systems. The effectiveness of the proposed approach was tested on a real case study involving the L-PBF of complex Ti6Al4V parts that exhibited local geometrical distortions.

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Acoustic emission for in situ process monitoring of selective laser melting additive manufacturing based on machine learning and improved variational modal decomposition

Wang

Xuan

2022

Int J Adv Manuf Technol

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Optical in-situ monitoring and correlation of density and mechanical properties of stainless steel parts produced by selective laser melting process based on varied energy density

Cited by 38 publications

References 11 publications

In situ monitoring methods for selective laser melting additive manufacturing process based on images — A review

In situ monitoring methods for selective laser melting additive manufacturing process based on images — A review

Automated layerwise detection of geometrical distortions in laser powder bed fusion

Acoustic emission for in situ process monitoring of selective laser melting additive manufacturing based on machine learning and improved variational modal decomposition

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