Classification of specimen density in Laser Powder Bed Fusion (L-PBF) using in-process structure-borne acoustic process emissions

Eschner, Niclas; Weiser, Lukas; Häfner, Benjamin; Lanza, Gisela

doi:10.1016/j.addma.2020.101324

Cited by 40 publications

(29 citation statements)

References 38 publications

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“…Grasso et al (2017) and following studies from the same authors (Yan et al 2020, Colosimo and) used a complex star-shaped part to force the onset of anomalous heat accumulations, i.e., hot-spots, which represent critical events in PBF as potential sources of micro and macro geometrical defects. Other authors used specimens including overhang areas with different angles and aspect ratios to force the occurrence of volumetric defects (Mohr et al 2020) or geometrical errors (Boone et al 2018, Kolb et al 2018b, to investigate the effect on the thermal history of the process (Gaikwad et al 2020, Montazeri andRao 2018), the properties of the melt pool (Hooper 2018, Scime andBeuth 2019) and the time-frequency signature of structure-borne acoustic emissions (Eschner et al 2020b). Rather than varying the shape of individual parts, Williams et al (2019) investigated the influence of different inter-layer cooling time by varying the number of parts printed at different heights within the build.…”

Section: Other Influences: Part and Build Geometry Powder Properties ...mentioning

confidence: 99%

“…andShevchik et al (2019) showed that different scan speeds caused different wavelet spectrograms patterns of the air-borne acoustic signal. The influence of process parameters on time and frequency domain features of the air-borne signal were discussed byYe et al (2018b) andKouprianoff et al (2018), whereasEschner et al (2020b) andPlotnikov et al (2019) showed similar effects on structure-borne acoustic signals too.Page 36 of 66 AUTHOR SUBMITTED MANUSCRIPT -MST-112379.R1…”

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confidence: 98%

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

Grasso

Remani

Dickins

et al. 2021

Meas. Sci. Technol.

122

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The possibility of using a variety of sensor signals acquired during metal powder bed fusion processes, to support part and process qualification and for the early detection of anomalies and defects, has been continuously attracting an increasing interest. The number of research studies in this field has been characterised by significant growth in the last few years, with several advances and new solutions compared with first seminal works. Moreover, industrial powder bed fusion systems are increasingly equipped with sensors and toolkits for data collection, visualisation and, in some cases, embedded in-process analysis. Many new methods have been proposed and defect detection capabilities have been demonstrated. Nevertheless, several challenges and open issues still need to be tackled to bridge the gap between methods proposed in the literature and actual industrial implementation. This paper presents an updated review of the literature on in-situ sensing, measurement and monitoring for metal powder bed fusion processes, with a classification of methods and a comparison of enabled performances. The study summarises the types and sizes of defects that are practically detectable while the part is being produced and the research areas where additional technological advances are currently needed.

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Section: Other Influences: Part and Build Geometry Powder Properties ...mentioning

confidence: 99%

mentioning

confidence: 98%

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

Grasso

Remani

Dickins

et al. 2021

Meas. Sci. Technol.

122

View full text Add to dashboard Cite

show abstract

“…Acoustic emission is "the generation of an elastic wave by the rapid change in the stress state of some regions in the material" [77]. This change can be related to microcracks formation, pores, evaporation dynamics, and temperature gradients [78]. These sensors can be fixed within the build plate to collect structure-borne acoustic emissions or along the build chamber walls to collect air-borne acoustic emissions, as shown in Figure 6 [79].…”

Section: Acoustic Sensorsmentioning

confidence: 99%

“…For welding, cladding, and AM applications, transforming into fast Fourier transform are common preprocessing methods, as illustrated in Figure 9. Lately, the use of spectrogram is also becoming a common approach [78]. Some preprocessing for pyrometers included the use of discrete Fourier transform as well [100].…”

Section: Data Preprocessingmentioning

confidence: 99%

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

et al. 2021

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One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

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“…In addition, with regard to acoustic emission monitoring (Shevchik et al , 2018; Ye et al , 2018), respectively, demonstrated its feasibility for process condition classification by CNN and deep belief network. Eschner et al (2020) used artificial NN to achieve density level classification based on acoustic emission monitoring.…”

Section: Introductionmentioning

confidence: 99%

Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory

Zhang

Yan

Hong

et al. 2021

RPJ

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Purpose This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development. Design/methodology/approach Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied. Findings The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%. Originality/value An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

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Classification of specimen density in Laser Powder Bed Fusion (L-PBF) using in-process structure-borne acoustic process emissions

Cited by 40 publications

References 38 publications

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

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

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory

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