Monitoring method for femtosecond laser modification of silicon carbide via acoustic emission techniques

Xie, Xiaozhu; Zhang, Yuliang; Huang, Qingpeng; Huang, Yajun; Zhang, Wei; Zhang, Jiafa; Long, Jiangyou

doi:10.1016/j.jmatprotec.2020.116990

Cited by 21 publications

(2 citation statements)

References 23 publications

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“…When applied in additive manufacturing, the AE technique presents more advantages in terms of monitoring [ 26 – 28 ], such as indicating processing quality in laser powder bed fusion [ 29 ], identifying processing state, diagnosing defects in laser cladding [ 30 ], classifying defects in selective laser melting [ 31 , 32 ], and so on. Almost all of the studies mentioned above have worked on AE monitoring of continuous laser processing of metal materials, and very little work has focused on pulsed laser processing [ 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Revealing the interaction mechanism of pulsed laser processing with the application of acoustic emission

Liu

Rong

Yang

et al. 2023

Front. Optoelectron.

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The mechanisms of interaction between pulsed laser and materials are complex and indistinct, severely influencing the stability and quality of laser processing. This paper proposes an intelligent method based on the acoustic emission (AE) technique to monitor laser processing and explore the interaction mechanisms. The validation experiment is designed to perform nanosecond laser dotting on float glass. Processing parameters are set differently to generate various outcomes: ablated pits and irregular-shaped cracks. In the signal processing stage, we divide the AE signals into two bands, main and tail bands, according to the laser processing duration, to study the laser ablation and crack behavior, respectively. Characteristic parameters extracted by a method that combines framework and frame energy calculation of AE signals can effectively reveal the mechanisms of pulsed laser processing. The main band features evaluate the degree of laser ablation from the time and intensity scales, and the tail band characteristics demonstrate that the cracks occur after laser dotting. In addition, from the analysis of the parameters of the tail band very large cracks can be efficiently distinguished. The intelligent AE monitoring method was successfully applied in exploring the interaction mechanism of nanosecond laser dotting float glass and can be used in other pulsed laser processing fields. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Revealing the interaction mechanism of pulsed laser processing with the application of acoustic emission

Liu

Rong

Yang

et al. 2023

Front. Optoelectron.

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“…Waveform analysis can provide the variation of amplitude in the time domain and frequency domain, and the relationship between time and frequency. Conventional waveform analysis methods include wavelet-based analysis [29], short-time Fourier transform [30][31][32], empirical mode decomposition (EMD) [33,34] and Hilbert-Huang transform [35]. However, compared with the above-mentioned parameter features, waveform analysis often requires more time, storage and computing resources.…”

Section: Introductionmentioning

confidence: 99%

Filament extrusion state monitoring in fused filament fabrication using Bhattacharyya distance and K-nearest neighbor

Li,

Yang,

wang

et al. 2023

Meas. Sci. Technol.

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Fused filament fabrication (FFF) is one of the most popular techniques of additive manufacturing. However, product quality issues still limit the further application of FFF technology. Filament extrusion state has a great influence on the quality of FFF fabricated products, since both under-extrusion and over-extrusion can lead to the deterioration of product quality. Therefore, monitoring the filament extrusion states is vital and essential. This paper aims to monitor the filament extrusion state by acoustic emission (AE). To achieve this goal, experiments are conducted on a desktop FFF machine, where the states of under-extrusion and over-extrusion are induced by different extrusion speeds. Original AE signals are collected during the experiments. Confronted with the challenge posed by the susceptibility of AE signals to noise during the complex extrusion process and different conditions, one calculates the statistical distribution of the features defined on the raw AE signals, without the need for noise reduction steps. The k-nearest neighbor algorithm is then adopted to identify the different extrusion states, where the Bhattacharyya distance is employed to measure the distances or similarities of the calculated distributions. The findings demonstrate the successful identification of various extrusion states induced by different extrusion speeds through the presented method. The outcomes of this study pave the way for the development of an affordable in-situ FFF monitoring system with comprehensive capabilities.

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Development of a multi-sensor system for in-situ process monitoring of femtosecond laser micromachining

Yildirim,

Nagarajan,

Tjahjowidodo

et al. 2024

Int J Adv Manuf Technol

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Monitoring method for femtosecond laser modification of silicon carbide via acoustic emission techniques

Cited by 21 publications

References 23 publications

Revealing the interaction mechanism of pulsed laser processing with the application of acoustic emission

Revealing the interaction mechanism of pulsed laser processing with the application of acoustic emission

Filament extrusion state monitoring in fused filament fabrication using Bhattacharyya distance and K-nearest neighbor

Development of a multi-sensor system for in-situ process monitoring of femtosecond laser micromachining

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