Smart machining: Monitoring of CFRP milling using AE and IR

Oliveira, Thiago Luiz Lara; Zitoune, Rédouane; Ancelotti, Antônio Carlos; Cunha, Sebastião Simões da

doi:10.1016/j.compstruct.2020.112611

Cited by 22 publications

(3 citation statements)

References 24 publications

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“…Specifically, milling is a machining process that aims to remove material using rotating cutters. For the analysis of different manufacturing operations, AE sensors have been often used in combination (or compared with) other sensors, such simpler microphones [11], force and power measurements [12], vibration acceleration sensing [13,14], and infrared cameras [15] (for a general survey of data-driven monitoring in the manufacturing process, a reader can refer to the survey of Xu et al [16]).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Machining Quality Prediction Using Acoustic Sensors and Machine Learning

Carrino

Guerne

Dreyer

et al. 2020

The 14th International Conference on Interdisciplinarity in Engineering—INTER-Eng 2020

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The online automatic estimation of the quality of products manufactured in any machining process without any manual intervention represents an important step toward a more efficient, smarter manufacturing industry. Machine learning and Convolutional Neural Networks (CNN), in particular, were used in this study for the monitoring and prediction of the machining quality conditions in a high-speed milling of stainless steel (AISI 303) using a 3mm tungsten carbide. The quality was predicted using the Acoustic Emission (AE) signals captured during the cutting operations. The spectrograms created from the AE signals were provided to the CNN for a 3-class quality level. A promising average f1-score of 94% was achieved.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Machining Quality Prediction Using Acoustic Sensors and Machine Learning

Carrino

Guerne

Dreyer

et al. 2020

The 14th International Conference on Interdisciplinarity in Engineering—INTER-Eng 2020

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“…AE signals are used in analysis to identify the relationship between damage and event, are directly related to damage mechanisms, and aid in damage detection, particularly through disregarding noise [29]. Recent studies show the high applicability of AE signal analyses for monitoring real-time processes, in mechanical tests, structural monitoring, and manufacturing processes [30][31][32]. The findings support the application of AE sensing technology for future automation, in which it is possible to visualize defect and crack development in real time, allowing higher-quality products and enhancing safety.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission Analysis of Mode II Interlaminar Fracture Toughness of 3D Reinforced CFRP

Oliveira,

Bortoluzzi,

Barbosa

et al. 2024

NDT

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The use of composites in industry is increasing due to their ability to replace traditional materials. Carbon fiber-reinforced polymers offer a favorable strength-to-weight ratio, making them advantageous in numerous applications. Delamination is a common failure mode for composite materials, making it a crucial factor in ensuring material safety during service life. While fiber orientation in composites is designed for specific directional reinforcement, out-of-plane loads are often neglected, posing a critical challenge. Implementing through-thickness reinforcement, such as tufting, can enhance out-of-plane resistance, enabling more accurate structural designs. Non-destructive testing methods, particularly acoustic emission, play a significant role in ensuring component safety by detecting early damage and flaws. This study focused on monitoring mode II interlaminar fracture toughness and end-notched flexure (ENF), using acoustic emissions to compare the performance of samples with different through-thickness reinforcements against that of nonreinforced samples. The research analyzed acoustic emission patterns during testing, revealing a strong correlation with failure stages and the resistance induced by reinforcements. This approach provided valuable insights into damage characterization, supported by fractography analysis, especially concerning the final stages of failure due to damage, and the effects of different thread reinforcements. Acoustic emission proved crucial for real-time monitoring, enabling informed decisions to be made regarding component repair and lifespan extension in composite materials.

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“…Acoustic emission (AE) is a vibration phenomenon in a matrix material when phase transition or material removal happens under stress [ 11 ]. AE technique is a suitable and efficient method for monitoring the machining process, since it is a non-destructive testing method, and AE signals generated during the machining process include essential information about the machined materials [ 12 , 13 ]. In the case of traditional machining methods, such as turning and milling, some researchers applied the AE technique to monitor the degree of metal material removal under different conditions [ 14 – 16 ].…”

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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Smart machining: Monitoring of CFRP milling using AE and IR

Cited by 22 publications

References 24 publications

Machining Quality Prediction Using Acoustic Sensors and Machine Learning

Machining Quality Prediction Using Acoustic Sensors and Machine Learning

Acoustic Emission Analysis of Mode II Interlaminar Fracture Toughness of 3D Reinforced CFRP

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

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