Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model

Hai-xi, WU; Yu, Zhonghua; Wang, Yan

doi:10.1007/s00170-016-9548-6

Cited by 117 publications

(44 citation statements)

References 36 publications

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“…Ever since its discovery in the early 1950s, there has been a tremendous growth in the use of acoustic emissions in machine failure diagnosis [ 22 , 23 ], electric power system [ 24 ], civil engineering [ 25 ], etc. It has also been proven to be a promising technique in the emerging research field of AM process monitoring [ 26 , 27 ]. However, the potential of acoustic emission is underestimated in AM process monitoring.…”

Section: Methodsmentioning

confidence: 99%

Filament Breakage Monitoring in Fused Deposition Modeling Using Acoustic Emission Technique

Yang

Yan

et al. 2018

Sensors

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Polymers are being used in a wide range of Additive Manufacturing (AM) applications and have been shown to have tremendous potential for producing complex, individually customized parts. In order to improve part quality, it is essential to identify and monitor the process malfunctions of polymer-based AM. The present work endeavored to develop an alternative method for filament breakage identification in the Fused Deposition Modeling (FDM) AM process. The Acoustic Emission (AE) technique was applied due to the fact that it had the capability of detecting bursting and weak signals, especially from complex background noises. The mechanism of filament breakage was depicted thoroughly. The relationship between the process parameters and critical feed rate was obtained. In addition, the framework of filament breakage detection based on the instantaneous skewness and relative similarity of the AE raw waveform was illustrated. Afterwards, we conducted several filament breakage tests to validate their feasibility and effectiveness. Results revealed that the breakage could be successfully identified. Achievements of the present work could be further used to develop a comprehensive in situ FDM monitoring system with moderate cost.

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Section: Methodsmentioning

confidence: 99%

Filament Breakage Monitoring in Fused Deposition Modeling Using Acoustic Emission Technique

Yang

Yan

et al. 2018

Sensors

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“…Extraction of Temperature Field Feature Parameters. The properties such as viscosity, shrinkage, and other properties of the FDM material will be affected by temperature parameters, which are important factors affecting the surface quality of the products [13,17,18]. Through the analysis of the temperature field of the product surface, the corresponding relation between the temperature and the surface quality state can be found, which can provide a theoretical basis for improving the product quality.…”

Section: Construction Of Product Surface Defect Knowledge Basementioning

confidence: 99%

“…One is to study the working status of the machine in the building process, such as obtaining the diagnostic method of machine failure through the processing of the sound signal. In this kind of research work, the acoustic emission original waveform data are collected directly using signal processing methods such as wavelet analysis and empirical mode decomposition to extract the relevant eigenvalues according to the extracted eigenvalues of the process state identification in the FDM building process [12][13][14]. Another focus is to directly analyze the quality of the product in the building process.…”

Section: Introductionmentioning

confidence: 99%

Approach to Online Defect Monitoring in Fused Deposition Modeling Based on the Variation of the Temperature Field

Wang

2018

Complexity

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According to the surface state of the product, a process quality monitoring technology based on a Qualitative Trend Analysis method is proposed. The construction of the product surface defect knowledge base provides a basis for online monitoring of the nozzle status of the fused deposition modeling (FDM) machine. Ten workpieces are designed in all, and each one conducts two prints which are normal and abnormal, respectively. An infrared thermal imager is used to obtain the temperature image of each layer of the workpiece surface; the feature values are extracted, and the difference between the feature values in the two kinds of printing is calculated. The Qualitative Trend Analysis method is used to analyze the difference in order to obtain the corresponding sensitive feature parameters corresponding to the starting points in different phases of the product printing. The change rules of the sensitive feature values are summarized, and the threshold of each starting point is solved. Base on that, the knowledge rules are defined and the defect knowledge base is constructed. Finally, a new experiment is designed to obtain the theoretical starting point of each state of the product surface in the building process. By comparing the theoretical starting point with the actual starting point, the feasibility of the knowledge rules in monitoring the nozzle working state is verified.

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“…These methods can be divided into model-based (or physical-based), knowledge-based, data-driven, and hybrid methods. The model-based methods utilize physical/chemistry knowledge to describe the degradation of specific systems, such as rotating machinery (Wu et al, 2017a(Wu et al, , 2017b, lithium-ion batteries (Guha et al, 2017), and crack growth of electric equipment (Cadini et al, 2009). The knowledge-based methods predict the equip-ment failure by comparing with structured rules and knowledge, such as Expert System (Biagetti and Sciubba, 2004) and Adaptive Neuro-Fuzzy Inference System (Chen et al, 2013a).…”

Section: Introductionmentioning

confidence: 99%

A method for degradation prediction based on Hidden semi-Markov models with mixture of Kernels

Yang

Zheng

2020

Computers in Industry

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Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model

Cited by 117 publications

References 36 publications

Filament Breakage Monitoring in Fused Deposition Modeling Using Acoustic Emission Technique

Filament Breakage Monitoring in Fused Deposition Modeling Using Acoustic Emission Technique

Approach to Online Defect Monitoring in Fused Deposition Modeling Based on the Variation of the Temperature Field

A method for degradation prediction based on Hidden semi-Markov models with mixture of Kernels

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