Process performance evaluation and classification via in-situ melt pool monitoring in directed energy deposition

Ertay, Deniz Sera; Naiel, Mohamed A.; Vlasea, Mihaela; Fieguth, Paul

doi:10.1016/j.cirpj.2021.06.015

Cited by 18 publications

(5 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second strategy concerned the in situ data coming from the sensor systems monitoring the melt pool and process environment. Due to the extreme physical conditions in DED-L, the in situ monitoring is prone to noise as lots of outliers can be expected throughout an experimental run [6]. As the goal of the present study is the overall evaluation of process stability across multiple DED-L print jobs, critical boundaries have to be defined within which a print job can be deemed acceptable.…”

Section: Methodsmentioning

confidence: 99%

“…The typical quality issues associated with DED-L are porosity, residual stress, cracking, and the high surface roughness of the final parts [4]. This missing process stability associated with DED-L can be correlated with the complex physical phenomena involved in the laser-powder interaction as well as the cyclic thermal loading of the layer-wise manufacturing process [6,7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process

Hartmann,

Vykhtar,

Möbs

et al. 2024

Processes

View full text Add to dashboard Cite

Additive manufacturing processes are prone to production errors. Specifically, the unique physical conditions of Laser-Directed Energy Deposition (DED-L) lead to unexpected process anomalies resulting in subpar part quality. The resulting costs and lack of reproducibility are two major barriers hindering a broader adoption of this innovative technology. Combining sensor data with data from relevant steps before and after the production process can lead to an increased understanding of when and why these process anomalies occur. In the present study, an IoT-based data mining framework is presented to assess the stability of processing Ti6Al4V on an industrial-grade DED-L machine. The framework employs an edge-cloud computing methodology to collect data efficiently and securely from various steps in the part lifecycle. During manufacturing, multiple sensors are employed to monitor the essential process characteristics in situ. Mechanical properties of the 160 printed specimens were obtained using appropriate destructive testing. All data are stored on a central database and can be accessed via the web for data analytics. The results prove the successful implementation of the proposed IoT framework but also indicate a lack of process stability during manufacturing. The occurring part errors can only be partially correlated with anomalies in the in situ sensor data.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process

Hartmann,

Vykhtar,

Möbs

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Ertay et al [294] developed a classifier that uses affordable machine vision sensing to categorize process signatures into specific zones, including under-melt, conduction, keyhole, and balling, to prevent instabilities, defects, and anomalies during the process. Statistical measures including average, standard deviation, and Root Mean Square, derived from various data types like process physics, signatures, and ex situ data (with a microscope), were used to formulate process maps.…”

Section: Performance Evaluation and Defects Identificationmentioning

confidence: 99%

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

Imran,

Che Idris,

De Silva

et al. 2024

Technologies

View full text Add to dashboard Cite

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.

show abstract

“…DED-related processes have different levels of in-situ control: recent research monitored the melting pool geometry during wire arc additive manufacturing to control for the bead's geometry and symmetry [31]; additional research was conducted on the use of a structured light system in the development of a real-time monitoring tool to aid the repair of an engine component [32]. Moreover, research towards establishing process maps that guide the deposition process towards avoiding lack-of-fusion and keyhole porosities has been conducted [33].…”

Section: Introductionmentioning

confidence: 99%

Automation of Property Acquisition of Single Track Depositions Manufactured through Direct Energy Deposition

et al. 2022

View full text Add to dashboard Cite

Metallic additive manufacturing processes have been significantly developed since their inception with modern systems capable of manufacturing components for structural applications. However, successful processing through these methods requires extensive experimentation before optimised parameters can be found. In laser-based processes, such as direct energy deposition, it is common for single track beads to be deposited and subjected to analysis, yielding information on how the input parameters influence characteristics such as the output’s adhesion to the substrate. These characteristics are often determined using specialised software, from images obtained by cross-section cutting the line beads. The proposed approach was based on a Python algorithm, using the scikit-image library and optical microscopy imaging from produced 18Ni300 Maraging steel on H13 tool steel, and it computes the relevant properties of DED-produced line beads, such as the track height, width, penetration, wettability angles, cross-section areas above and below the substrate and dilution proportion. 18Ni300 Maraging steel depositions were optimised with a laser power of 1550 W, feeding rate of 12 gmin−1, scanning speed of 12 mm s−1, shielding gas flow rate of 25 Lmin−1 and carrier gas flow rate of 4 Lmin−1 for a laser spot diameter of 2.1mm. Out of the cross-sectioned beads, their respective height, width and penetration were calculated with 2.71%, 4.01% and 9.35% errors; the dilution proportion was computed with 14.15% error, the area above the substrate with 5.27% error and the area below the substrate with 17.93% error. The average computational time for the processing of one image was 12.7s. The developed approach was purely segmentational and could potentially benefit from machine-learning implementations.

show abstract

Process performance evaluation and classification via in-situ melt pool monitoring in directed energy deposition

Cited by 18 publications

References 51 publications

IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process

IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process

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

Automation of Property Acquisition of Single Track Depositions Manufactured through Direct Energy Deposition

Contact Info

Product

Resources

About