Machine condition monitoring in FDM based on electret microphone, SVM, and neural networks

Lopes, Thiago Glissoi; Aguiar, Paulo Roberto; Monson, Paulo Monteiro de Carvalho; D’Addona, Doriana Marilena; Conceição Júnior, Pedro de Oliveira; de Oliveira Junior, Reinaldo Götz

doi:10.1007/s00170-023-12375-0

Cited by 5 publications

(2 citation statements)

References 63 publications

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“…Bhavsar et al [10] utilized discrete wavelet transform to analyze the differences in vibration acoustic signals of sensors during FDM 3D printing, aiming to detect the rst layer lament deposition process, thereby achieving detection of rst layer bonding quality. Machine learning nds extensive application in defect detection, as demonstrated by Lopes et al [11], who employed piezoelectric microphones, support vector machines (SVMs), and neural networks for machine state monitoring in FDM 3D printing. Through signal processing and feature extraction techniques such as RMS values and spectral analysis, the study identi ed raw signal patterns associated with different machine conditions (such as normal operation, extruder blockages, and lament shortages).…”

Section: Introductionmentioning

confidence: 99%

Real-time defect detection for FDM 3D printing using lightweight model deployment

Hu,

Chang,

et al. 2024

Preprint

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FDM 3D printing is one of the most widely used additive manufacturing methods, bringing great convenience to production manufacturing. However, various printing defects may occur during the printing process due to human factors or printer-related issues. Timely detection of defects and halting printing becomes a scenario of significant practical importance. This paper first analyzes the causes of the five most common defects in FDM 3D printing, and a defect dataset is created by deliberately designing defects. Subsequently, a real-time defect detection system for FDM 3D printing, based on an improved YOLOv8 detection head, is developed. By employing an optimization method using Group Convolution to share parameters, the detection head is lightweight, resulting in better model performance. Experimental results demonstrate that the mAP50 of the improved YOLOv8 model reaches 97.5%, with an 18.1% increase in FPS and a 32.9% reduction in GFLOPs. This enhancement maintains comparable detection accuracy to the original model while achieving faster detection speed and lower computational requirements. The improved model is integrated into the detection system as the detection model, and through testing, the real-time detection system promptly and accurately identifies and alerts any occurring defects. The practical significance of this system lies in its ability to enhance production efficiency, reduce resource wastage due to defective printing, and improve product quality and manufacturing safety, thereby providing strong support for the application of visual inspection technology in FDM 3D printing.

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Section: Introductionmentioning

confidence: 99%

Real-time defect detection for FDM 3D printing using lightweight model deployment

Hu,

Chang,

et al. 2024

Preprint

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“…Notably, the use of acoustic emission sensors can be observed in the works developed by [4,5]. Additionally, a literature review reveals works proposing the use of low-cost electret microphones [6]. However, it is known that elevated temperature values, such as those that can be reached on the print bed, have various effects on a sensor's responses [7].…”

Section: Introductionmentioning

confidence: 99%

Study of the Temperature Influence on an Electret Microphone in the Monitoring of Fused Deposition Modeling

Lopes,

Aguiar,

Junior

et al. 2023

Ecsa 2023

Self Cite

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Real-time defect detection for FFF 3D printing using lightweight model deployment

Hu,

Chen,

et al. 2024

Int J Adv Manuf Technol

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Machine condition monitoring in FDM based on electret microphone, SVM, and neural networks

Cited by 5 publications

References 63 publications

Real-time defect detection for FDM 3D printing using lightweight model deployment

Real-time defect detection for FDM 3D printing using lightweight model deployment

Study of the Temperature Influence on an Electret Microphone in the Monitoring of Fused Deposition Modeling

Real-time defect detection for FFF 3D printing using lightweight model deployment

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