Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography

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

doi:10.1016/j.addma.2020.101287

Cited by 29 publications

(23 citation statements)

References 16 publications

(20 reference statements)

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“…In their research, Lu et al [ 5 ] examined microcomputed tomography as a validation method of correlating features observed in optical images. They found that features detected on the spot could be matched with defects detected by microcomputed tomography, thus revealing the possibility of using optical images as a means of defect detection, in their case, in the parts that were selected for melting by laser.…”

Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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This paper describes the enhancement of the existing predictive system of quality management in the processes of metallurgic manufacturing. Specifically, it addresses steel-strip manufacturing. The main quality management innovation is the transition from the current methodological process of a single-step defect evaluation to a two-step evaluation. A two-step defect check of the strip’s surface involves checking for defects during the hot-rolling process first, and double-checking it during the process of pickling. These defects are detected in a well-established process of camera imaging in the production process. The recorded image is then processed mathematically to find the degree of defect correlation in those processes. The two-step evaluation enables a more detailed focus on a particular defect and its position on the strip. Decisions concerning further processing are based on defect evaluation, for instance, whether a rework is necessary to maximize the product utilization and minimize the eventual negative impact of the defect on production equipment. A crucial aspect is also the reduced probability of failures in the manufacturing process.

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Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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“…(h and i) image collected during SLM process and its binary processing results [71] ; (j) number of optical images and CT images collected under condition of single layer thickness distance [71] ; (k) relationship between image quantitative analysis results of each sample process and results of nondestructive testing [71] based on Archimedes' principle and mechanical properties of sample were correlated with the quantitative results of the image of sample during SLM process [Figs. 19 (f), (g)], which proved that the in situ monitoring system based on image processing was feasible to infer the mechanical properties of parts.…”

Section: Special Reviewmentioning

confidence: 99%

“…19 (f), (g)], which proved that the in situ monitoring system based on image processing was feasible to infer the mechanical properties of parts. Lu et al [71] conducted nondestructive testing on the parts by Micro CT based on the previous study [70] , and reconstructed the CT scan results of the components. The correlation between quantified features of images collected during SLM process and actual defects was verified.…”

Section: Special Reviewmentioning

confidence: 99%

“…Fig. 19: Paraxial in situ monitoring of scanned layer and powder spread layup-forming layer during SLM process:(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] ;(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] ;(h and i) image collected during SLM process and its binary processing results[71] ; (j) number of optical images and CT images collected under condition of single layer thickness distance[71] ; (k) relationship between image quantitative analysis results of each sample process and results of nondestructive testing[71] …”

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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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“…Because of this, almost all laser PBF (L-PBF) system developers currently provide their machines with embedded powder bed cameras and, in some cases, with basic automated powder bed anomaly detection capability, albeit commonly limited to macroscopic errors [1,2]. Images acquired after the melting phase, once the solidification of the scanned area has occurred, may be used for different aims, such as detecting undesired surface irregularities in the solidified layers, as possible sources of internal and surface defects [6][7][8][9][10], or signaling possible deviations with respect to the nominal shape in the layer, as evidence of geometrical errors [11][12][13][14][15]. Alternative sensing methods have also been presented, including fringe projection combined with single or multiple cameras for surface topography reconstruction [16][17][18][19] and high-spatial-resolution scanning sensors installed onto the recoating arm [20,21].…”

Section: Introductionmentioning

confidence: 99%

In Situ Monitoring of Powder Bed Fusion Homogeneity in Electron Beam Melting

Grasso

2021

Materials

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Increasing attention has been devoted in recent years to in situ sensing and monitoring of the electron beam melting process, ranging from seminal methods based on infrared imaging to novel methods based on backscattered electron detection. However, the range of available in situ monitoring capabilities and solutions is still quite limited compared to the wide number of studies and industrial toolkits in laser-based additive manufacturing processes. Some methods that are already industrially available in laser powder bed fusion systems, such as in situ detection of recoating errors, have not yet been investigated and tested in electron beam melting. Motivated by the attempt to fill this gap, we present a novel in situ monitoring methodology that can be easily implemented in industrial electron beam melting machines. The method is aimed at identifying local inhomogeneity and irregularities in the powder bed by means of layerwise image acquisition and processing, with no external illumination source apart from the light emitted by the hot material underneath the currently recoated layer. The results show that the proposed approach is suitable to detect powder bed anomalies, while also highlighting the link between the severity of in situ detected errors and the severity of resulting defects in the additively manufactured part.

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Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography

Cited by 29 publications

References 16 publications

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

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

In Situ Monitoring of Powder Bed Fusion Homogeneity in Electron Beam Melting

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