Monitoring and Adaptive Control of Laser Processes

Purtonen, Tuomas; Kalliosaari, Anne; Salminen, Antti

doi:10.1016/j.phpro.2014.08.038

Cited by 196 publications

(94 citation statements)

References 114 publications

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“…6 General strategy for in-process sensing technology development-identifying quantitative correlations between process parameters, process signatures, and quality metrics property measurements, with the latter being more specific to the SLM process. Technologies for monitoring SLM process signatures bear great similarity to those used in laser welding [41,42]. The most common approaches to monitoring the "voice of process" have focused on acoustic signatures from the weld pool, electromagnetic signatures such as those illustrated in Fig.…”

Section: Process Signaturesmentioning

confidence: 99%

“…Acoustic signature monitoring Acoustic monitoring is widely practiced in laser welding processes due to the simplicity and low cost of acoustic sensors [41,42,50,51]. The basic process requires comparison of the acoustic signature of a "known good" standard with that from welds to be evaluated and is described in ASTM E749.…”

Section: "Voice Of Process" Measurementsmentioning

confidence: 99%

“…The basic process requires comparison of the acoustic signature of a "known good" standard with that from welds to be evaluated and is described in ASTM E749. While the intensity of a weld acoustic signature has not been shown to be a strong indicator of weld quality [50], the frequency response has been correlated to several weld quality metrics and process phenomena, including keyhole formation, plasma formation, and crack propagation [42]. Acoustic monitoring has been discussed as a potential monitoring technology for laser additive processes [42], but to date there have been no published studies of this monitoring approach applied to the SLM process.…”

Section: "Voice Of Process" Measurementsmentioning

confidence: 99%

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In-process sensing in selective laser melting (SLM) additive manufacturing

Spears¹,

Gold²

2016

Integr Mater Manuf Innov

378

161

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Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.

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Section: Process Signaturesmentioning

confidence: 99%

Section: "Voice Of Process" Measurementsmentioning

confidence: 99%

Section: "Voice Of Process" Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

In-process sensing in selective laser melting (SLM) additive manufacturing

Spears¹,

Gold²

2016

Integr Mater Manuf Innov

378

161

View full text Add to dashboard Cite

show abstract

“…However, vision cameras (CCD and CMOS) are not Fig. 4 Definition of the keyhole parameters [33] suitable for detecting mid-and long-wave infrared radiation, and for this reason, specific infrared cameras are required [43]. This argument is also mentioned by Voelkel and Mazumder [44], who suggested that the passive illumination was entirely insufficient for capturing details of the melt pool by providing very dark figures of the melt pool.…”

Section: Combined Vision and Thermal Image Processing Techniquesmentioning

confidence: 99%

“…It has also been claimed that acoustic sensing techniques could be applied to processes that involve melting, vaporization, plasma generation, and keyhole formation [43]. Moreover, acoustic emissions can provide useful data regarding the rapid phase change in the material as well as the formation and propagation of cracks [49].…”

Section: Acoustic Emission Techniquesmentioning

confidence: 99%

Quality assessment in laser welding: a critical review

Stavridis

Papacharalampopoulos

Stavropoulos

2017

Int J Adv Manuf Technol

182

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Quality assessment methods and techniques for laser welding have been developed both in-and post-process. This paper summarizes and presents relevant studies being classified according to the technology implemented (vision, camera, acoustic emissions, ultrasonic testing (UT), eddy current technique (ECT)) for the quality inspection. Furthermore, the current review aims to map the existing modeling approaches used to correlating measured weld characteristics and defects with the process parameters. Research gaps and implications of the quality assessment in laser welding are also described, and a future outlook of the research in the particular field is provided.

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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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Monitoring and Adaptive Control of Laser Processes

Cited by 196 publications

References 114 publications

In-process sensing in selective laser melting (SLM) additive manufacturing

In-process sensing in selective laser melting (SLM) additive manufacturing

Quality assessment in laser welding: a critical review

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

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