Measurement science needs for real-time control of additive manufacturing powder-bed fusion processes

Mani, Mahesh; Feng, Shaw C.; Lane, Brandon; Dönmez, Alkan; Moylan, Shawn P.; Fesperman, Ronnie R.

doi:10.1201/9781315119106-34

Cited by 77 publications

(104 citation statements)

References 141 publications

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“…The NIST report authored by Mani et al (2015) referred to the quantities that can be determined during the AM process as "process signatures". Accordingly to Mani et al (2015), the process signatures are "dynamic characteristics of the powder heating, melting, and solidification processes as they occur during the build". Mani et al (2015) also suggested a categorization of these signatures into two classes: i) observable signatures and ii) derived signatures.…”

Section: In-situ Sensingmentioning

confidence: 99%

“…Accordingly to Mani et al (2015), the process signatures are "dynamic characteristics of the powder heating, melting, and solidification processes as they occur during the build". Mani et al (2015) also suggested a categorization of these signatures into two classes: i) observable signatures and ii) derived signatures. The former can be observed and measured during the process by using in-situ sensing devices.…”

Section: In-situ Sensingmentioning

confidence: 99%

“…In this framework, ensuring the quality and repeatability of additively produced parts is a fundamental need to meet the stringent requirements and certification constraints imposed by leading sectors, like medical and aerospace. Nevertheless, the lack of process robustness, stability and repeatability was pointed out to be a major barrier for the industrial breakthrough of metal AM systems (Mani et al, 2015;Tapia and Elwany, 2014;Everton et al, 2016;Spear and Gold, 2016;Sharratt, 2015;Sames et al, 2016). Indeed, despite significant technological advances, the defect ratios are still too high with respect to more conventional production systems.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, despite significant technological advances, the defect ratios are still too high with respect to more conventional production systems. To face this issue, the development of process monitoring methodologies based on insitu sensing as well as novel feedback control strategies was indicated as a priority research area by many recent keynote studies, projects and roadmaps (Mellor et al, 2014;Olakanmi et al, 2015;Mani et al, 2015;Tapia and Elwany, 2014;Everton et al, 2016;Spear and Gold, 2016;Todorov et al, 2014). In-situ sensing and process monitoring are needed to determine the quality and stability of the process during the layer-wise growth of the part.…”

Section: Introductionmentioning

confidence: 99%

“…The studies of Sames et al (2016) and Olakamni et al (2015) and a report of the National Institute of Standard and Technology (NIST) (Mani et al, 2015) included a survey of metal AM processing defects. In addition, Tapia and Elwany (2014), Mani et al (2015), Spears and Gold (2016) and Everton et al (2016) presented a review of in-situ sensing and monitoring methods proposed in the literature. This study aims to contribute by merging a detailed discussion about the defect generation mechanisms with a review of the quantities that can be measured during the process and the corresponding in-situ sensing equipment.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Process defects andin situmonitoring methods in metal powder bed fusion: a review

Grasso

Colosimo

2017

Meas. Sci. Technol.

556

239

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Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g., aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in-situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in-situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for insitu monitoring of Powder Bed Fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in-situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

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Section: In-situ Sensingmentioning

confidence: 99%

Section: In-situ Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Process defects andin situmonitoring methods in metal powder bed fusion: a review

Grasso

Colosimo

2017

Meas. Sci. Technol.

556

239

View full text Add to dashboard Cite

show abstract

Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

et al. 2020

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Direct metal laser sintering, an additive manufacturing technique, has a huge growing demand in industries like aerospace, biomedical, and tooling sector due to its capability to manufacture complex parts with ease. Despite many technological advancements, the reliability and repeatability of the process are still an issue. Therefore, there is a demand for inline automatic fault detection and postprocessing tools to analyze the acquired in situ monitoring data aiming to provide better-quality assurance to the user. Herein, the treatment of the data obtained using the EOSTATE optical tomography monitoring system is focused. A balanced dataset is obtained with the help of computer tomography of the certified part (Stainless Steel CX cylindrical samples), through which a feature matrix is prepared, and the layers of the part are classified either having "Drift" or "No-drift." The model is trained with the feature matrix and tested on benchmark parts (Maraging Steel) and on an industrial part (knuckle, automotive part) manufactured in AlSi10Mg. The proposed semisupervised approach shows promising results for presented case studies. Thus, the semisupervised machine learning approach, if adopted, could prove to be a cost effective and fast approach to postprocess the in situ monitoring data with much ease.

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Ultrafast imaging for uncovering laser–material interaction dynamics

Wang

Wei

Yuan

et al. 2022

Int Journal of Mech Sys Dyn

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The physical mechanism of the dynamics in laser–material interaction has been an important research area. In addition to theoretical analysis, direct imaging‐based observation of ultrafast dynamic processes is an important approach to understand many fundamental issues in laser–material interaction such as inertial confinement fusion (ICF), laser accelerator construction, and advanced laser production. In this review, the principles and applications of three types of commonly used ultrafast imaging methods are introduced, including the pump–probe, X‐ray diagnosis, and single‐shot optical burst imaging. We focus on the technical features such as the spatial and temporal resolution for each technique, and present several conventional applications.

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Measurement science needs for real-time control of additive manufacturing powder-bed fusion processes

Cited by 77 publications

References 141 publications

Process defects andin situmonitoring methods in metal powder bed fusion: a review

Process defects andin situmonitoring methods in metal powder bed fusion: a review

Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

Ultrafast imaging for uncovering laser–material interaction dynamics

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