wire arc additive manufacturing
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Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 137
Author(s):  
Xinyi Xiao ◽  
Clarke Waddell ◽  
Carter Hamilton ◽  
Hanbin Xiao

Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments.


Author(s):  
Tobias Hauser ◽  
Raven T. Reisch ◽  
Tobias Kamps ◽  
Alexander F. H. Kaplan ◽  
Joerg Volpp

AbstractAcoustic emissions in directed energy deposition processes such as wire arc additive manufacturing and directed energy deposition with laser beam/metal are investigated within this work, as many insights about the process can be gained from this. In both processes, experienced operators can hear whether a process is running stable or not. Therefore, different experiments for stable and unstable processes with common process anomalies were carried out, and the acoustic emissions as well as process camera images were captured. Thereby, it was found that stable processes show a consistent mean intensity in the acoustic emissions for both processes. For wire arc additive manufacturing, it was found that by the Mel spectrum, a specific spectrum adapted to human hearing, the occurrence of different process anomalies can be detected. The main acoustic source in wire arc additive manufacturing is the plasma expansion of the arc. The acoustic emissions and the occurring process anomalies are mainly correlating with the size of the arc because that is essentially the ionized volume leading to the air pressure which causes the acoustic emissions. For directed energy deposition with laser beam/metal, it was found that by the Mel spectrum, the occurrence of an unstable process can also be detected. The main acoustic emissions are created by the interaction between the powder and the laser beam because the powder particles create an air pressure through the expansion of the particles from the solid state to the liquid state when these particles are melted. These findings can be used to achieve an in situ quality assurance by an in-process analysis of the acoustic emissions.


Author(s):  
Raven T. Reisch ◽  
Tobias Hauser ◽  
Benjamin Lutz ◽  
Alexandros Tsakpinis ◽  
Dominik Winter ◽  
...  

AbstractWire Arc Additive Manufacturing allows the cost-effective manufacturing of customized, large-scale metal parts. As the post-process quality assurance of large parts is costly and time-consuming, process monitoring is inevitable. In the present study, a context-aware monitoring solution was investigated by integrating machine, temporal, and spatial context in the data analysis. By analyzing the voltage patterns of each cycle in the oscillating cold metal transfer process with a deep neural network, temporal context was included. Spatial context awareness was enabled by building a digital twin of the manufactured part using an Octree as spatial indexing data structure. By means of the spatial context awareness, two quality metrics—the defect expansion and the local anomaly density—were introduced. The defect expansion was tracked in-process by assigning detected defects to the same defect cluster in case of spatial correlation. The local anomaly density was derived by defining a spherical region of interest which enabled the detection of aggregations of anomalies. By means of the context aware monitoring system, defects were detected in-process with a higher sensitivity as common defect detectors for welding applications, showing less false-positives and false-negatives. A quantitative evaluation of defect expansion and densities of various defect types such as pore nests was enabled.


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