Reconstruction of Microscopic Thermal Fields from Oversampled Infrared Images in Laser-Based Powder Bed Fusion

Stanger, Leigh Russell; Rockett, Thomas B. O.; Lyle, A.R.; Davies, Mark B.; Anderson, Magnus; Todd, Iain; Basoalto, Hector; Willmott, Jon R.

doi:10.3390/s21144859

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The optical resolution was measured from the horizontal CTF targets (as shown in Figure 6) because the vertical CTF targets were influenced by small variations in scan speed, minor perturbations in the translation stage, and the finite slit width. A knife-edge measurement provides a simple method of determining the point-spread-function of an optical system [37]. The point-spread-function quantifies the extent to which an optical system can resolve a point source of light.…”

Section: Optical Characterisationmentioning

confidence: 99%

High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios

Stuart

Davies

Hobbs

et al. 2022

Sensors

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High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider implications, limiting data collection opportunities, and therefore our knowledge, across a wide range of environments. In this article we introduce a low-cost alternative to the currently available instrumentation. This instrument provides hyperspectral datasets capable of resolving spectral variations in mm-scale targets, that cannot typically be resolved with many existing low-cost hyperspectral imaging alternatives. Instrument metrology is provided, and its efficacy is demonstrated within a mineralogy-based environmental monitoring application highlighting it as a valuable addition to the field of low-cost hyperspectral imaging.

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Section: Optical Characterisationmentioning

confidence: 99%

High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios

Stuart

Davies

Hobbs

et al. 2022

Sensors

Self Cite

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“…Restricted by the limitations of clamping position, angle, and distance, a single highspeed camera cannot monitor comprehensive aspects of the SLM process [11]. Except for optical signals, the SLM process is also accompanied by acoustic signals and thermal signals [12,13]. Microphone sensors have been applied to monitoring acoustic signals due to the advantages of lower cost, non-contact, non-destructive, and flexible [14,15].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting

Luo

et al. 2021

Sensors

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As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors.

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In-situ monitoring in laser powder bed fusion based on acoustic signal time-frequency synchrosqueezing transform and multi-scale spatially interactive fusion convolutional neural network

Li,

Zhang,

Zhang

et al. 2024

Journal of Manufacturing Processes

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Reconstruction of Microscopic Thermal Fields from Oversampled Infrared Images in Laser-Based Powder Bed Fusion

Cited by 4 publications

References 27 publications

High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios

High-Resolution Hyperspectral Imaging Using Low-Cost Components: Application within Environmental Monitoring Scenarios

Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting

In-situ monitoring in laser powder bed fusion based on acoustic signal time-frequency synchrosqueezing transform and multi-scale spatially interactive fusion convolutional neural network

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