Online geometry monitoring during directed energy deposition additive manufacturing using laser line scanning

Binega, Eden; Yang, Liu; Sohn, Hoon; Cheng, Jackie

doi:10.1016/j.precisioneng.2021.09.005

Cited by 41 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Methodsmentioning

confidence: 99%

“…The average height of each deposition order was estimated from the laser line scanning results and is presented in Table 3. More details regarding the laser line scanning test can be found in the study by Binega et al 45 After the fifth deposition order, the manufactured sample was sectioned, and OM images were taken under a 253 magnification. The area fraction of porosity a was computed for each deposition order based on the OM observations categorized according to the deposition height listed in Table 3.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Liu

Yang

et al. 2022

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

Nonlinear ultrasonic techniques have the benefit of high sensitivity to micro or early-stage defects. Among the various nonlinear techniques, the newly proposed sideband peak count (SPC) technique investigates defect-induced nonlinearity by counting the spectral sidebands from a broadband ultrasonic response in the frequency domain. In this study, SPC analysis is transformed into the time–frequency plane through synchrosqueezed wavelet transform (SWT) for transient nonstationary ultrasonic signals. The proposed new SPC technique was then adopted for in situ porosity monitoring in directed energy deposition (DED)—a typical metal additive manufacturing process. Porosity is one of the most critical defects in DED and has detrimental effects on the mechanical properties and fatigue performance of products. For in situ porosity monitoring, a fully noncontact ultrasonic measurement was achieved with a laser ultrasonic system, and its detectability was improved by laser polishing. Time and frequency windows were properly selected to suppress the effects of wave characteristic variations on the SPC analysis in the SWT domain. The performance of the proposed technique was verified by monitoring porosity in stainless steel 316L samples manufactured in the DED process. The test results demonstrated that the proposed nonlinear technique is much more sensitive to porosity than conventional linear techniques, and hence, is more suitable for in situ porosity monitoring.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Liu

Yang

et al. 2022

Structural Health Monitoring

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, the focus is to monitor the geometrical accuracy of the build parts; to detect normal, under-and overdeposition. There are two monitoring approaches of obtaining geometrical process signatures [16,36]: laser scanning-based [37][38][39][40][41][42] and machine vision-based [43][44][45][46][47][48], each with their strengths and weaknesses. Laser scanning-based monitoring system has been used to obtain more precise depth information on fabricated parts, i.e.…”

Section: B Geometrical Sensingmentioning

confidence: 99%

In-Situ Process Monitoring and Defects Detection Based on Geometrical Topography With Streaming Point Cloud Processing in Directed Energy Deposition

Imran,

Kim,

Jung

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The 3D printing industry faces challenges in ensuring reliable and repeatable processes, as increasing slopes can lead to defects and faults forming layer by layer in fabricated parts. Detecting these defects through post-processing quality inspections is time-consuming and laborious. To address this, a new method is proposed that incorporates a scoring scheme to quantitatively evaluate process performance using clad height data during printing. This approach aims to save time and cost while preserving the structural integrity of the part. The study develops a layer-wise point cloud processing technique to convert the incoming unordered data streams into rasterized points. By transforming raw signals into spatially equidistant points representing clad height in a 2-D Cartesian plane, a heatmap tomography of each layer is generated. Subsequently, a novel Defects-Finder algorithm is developed to locate and cluster surface defects based on the assigned scores. The findings demonstrate the algorithm's ability to identify the root cause of propagated faults, which can result in severe defects. Additionally, the study employs various statistical measures in the layer-level analysis to evaluate miniature process faults or shifts, which may get overshadowed, including cases of under and over-deposition. Through comparison and validation with physical artifacts, the proposed method proves effective in identifying and assessing process faults. Ultimately, this method enhances big data visualization for cost-effective quality control and boosts the overall productivity of additive manufacturing processes. By streamlining defect detection and performance evaluation, it addresses the challenges faced by the industry in ensuring reliable 3D printing outcomes.

show abstract

“…In addition to the topology of the networks, the activation function, the loss function and the optimization algorithm are essential hyper-parameters that influence the functionality of ANN. This work was based on the comparatively simple and often-used activation function, Rectified Linear Unit [28]. Furthermore, because a categorical target variable was used, the common loss function Cross-Entropy Loss was applied in the training phase to determine the error between the predicted and actual quality categories.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals

et al. 2022

View full text Add to dashboard Cite

The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.

show abstract

Online geometry monitoring during directed energy deposition additive manufacturing using laser line scanning

Cited by 41 publications

References 29 publications

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing

In-Situ Process Monitoring and Defects Detection Based on Geometrical Topography With Streaming Point Cloud Processing in Directed Energy Deposition

Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals

Contact Info

Product

Resources

About