Online detection of powder spatters in the additive manufacturing process

Wu, Ziqian; Xu, Zhenying; Fan, Wei

doi:10.1016/j.measurement.2022.111040

Cited by 11 publications

(3 citation statements)

References 46 publications

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“…In [ 65 ], an algorithm called Inverse Distance Weight K-Nearest Neighbor (IDW-KNN) was proposed to address the imbalance between positive and negative samples. This algorithm can satisfy online detection because it has strong interpretability, fewer parameters, a faster operation speed, and greater flexibility than the commonly employed algorithms.…”

Section: Discussionmentioning

confidence: 99%

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

Kim,

Abisado,

Villaverde

et al. 2023

Sensors

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Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability.

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Section: Discussionmentioning

confidence: 99%

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

Kim,

Abisado,

Villaverde

et al. 2023

Sensors

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“…In fact, melt pool instabilities may cause excessive spatters [16,163], which, in turn, may cause concept drift in signals [182,261]. Only a few other studies have also proposed using spatters as process signatures, including their size and quantity, to assess process performance and stability in metal AM [159,162,164,165,168,174,257,262,263], yet a quantitative comparison among different types of spatters, such as metallic jet and agglomeration spatters, remains unexplored [156].…”

Section: Challenges Inmentioning

confidence: 99%

Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

Imran,

Che Idris,

De Silva

et al. 2024

Technologies

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This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.

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“…Recent studies by Thompson et al [20] explored the use of acoustic birefringence to evaluate residual stress-induced anisotropy, while Deng and Yang [21] utilized nonlinear Lamb wave approaches to characterize in-plane elastic anisotropy in solid plates. However, the presence of rough surfaces and defects such as spattering and spherification [22][23][24][25] on SLM components pose challenges in maintaining consistent coupling conditions for contact-based ultrasonic characterization [26].…”

Section: Introductionmentioning

confidence: 99%

Non-contact characterization of material anisotropy of additive manufacturing components by electromagnetic acoustic resonance technique

Yuan,

Li,

Deng

2023

Meas. Sci. Technol.

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The degree of material anisotropy in additive manufacturing (AM) components is greatly influenced by the AM process parameters and machine setup. It is crucial to develop an effective technique for evaluating the material anisotropy in AM components to optimize AM process parameters and component design. This paper proposed a non-contact ultrasonic characterization method using the electromagnetic acoustic resonance (EMAR) technique to characterize the anisotropy of AM components. Various electromagnetic acoustic transducers (EMATs) were designed and utilized to characterize the material anisotropy and to determine the principal direction of the AM components. The degree of anisotropy in AM components was characterized using radial radiation EMATs. The relationship between the degree of anisotropy and the laser scanning angle was explored and further determined through the acoustic birefringence factor (ABF). Experimental results demonstrated that the anisotropy of AM components is intricately associated with the laser scanning angle, and specific angles can render the AM components isotropic. Moreover, understanding the principal directions is of significance for structural design and analyzing stress distribution in anisotropic components. Therefore, the principal directions of AM components were obtained by rotating the linear polarization EMAT. Changes in the resonance spectrum captured by the linear polarization EMAT while evaluating of principal directions were clearly illustrated, despite negligible alterations in linear ultrasonic features. Metallographic diagrams further validated the experimental findings. This investigation presented a highly accurate and reliable alternative for characterizing the anisotropy of AM components.

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Online detection of powder spatters in the additive manufacturing process

Cited by 11 publications

References 46 publications

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

Advancements in 3D Printing: Directed Energy Deposition Techniques, Defect Analysis, and Quality Monitoring

Non-contact characterization of material anisotropy of additive manufacturing components by electromagnetic acoustic resonance technique

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