Pyrometry

Herman, Irving P.

doi:10.1016/b978-012342070-1/50014-0

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Ratio-based temperature determination methods are based on the collection of the emitted thermal energy in two closely spaced spectral ranges at the same time. Temperature can be determined independently of local emission variations, provided that the intensity ratio of the detected spectral radiance changes constantly [ 10 , 11 ]. For a detailed explanation of the physical background, the reader is referred to Section 2.1.1 .…”

Section: Introductionmentioning

confidence: 99%

Two-Color-Thermography for Temperature Determination in Laser Beam Welding of Low-Melting Materials

Schwarzkopf

Rothfelder

Rasch

et al. 2023

Sensors

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Spatial and temporal knowledge of temperature evolution is crucial in laser beam welding of low-melting materials such as aluminum alloys. Current temperature measurements are restricted to (i) one-dimensional temperature information (e.g., ratio-pyrometers), (ii) a priori knowledge of emissivity (e.g., thermography), and (iii) high-temperature regions (e.g., two-color-thermography). This study presents a ratio-based two-color-thermography system that enables acquiring spatially and temporally resolved temperature information for low-melting temperature ranges (<1200 K). The study demonstrates that temperature can be accurately determined despite variations in signal intensity and emissivity for objects emitting constant thermal radiation. The two-color-thermography system is further transferred into a commercial laser beam welding set-up. Experiments with varying process parameters are conducted, and the ability of the thermal imaging method to measure dynamic temperature behavior is assessed. Image artifacts presumably caused by internal reflections inside the optical beam path limit the direct application of the developed two-color-thermography system during dynamic temperature evolution.

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Section: Introductionmentioning

confidence: 99%

Two-Color-Thermography for Temperature Determination in Laser Beam Welding of Low-Melting Materials

Schwarzkopf

Rothfelder

Rasch

et al. 2023

Sensors

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Optical in-situ temperature management for high-quality ZnO molecular beam epitaxy

Albert

Golla

Meier

2021

Journal of Crystal Growth

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Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

et al. 2021

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One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

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Pyrometry

Cited by 4 publications

References 45 publications

Two-Color-Thermography for Temperature Determination in Laser Beam Welding of Low-Melting Materials

Two-Color-Thermography for Temperature Determination in Laser Beam Welding of Low-Melting Materials

Optical in-situ temperature management for high-quality ZnO molecular beam epitaxy

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

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