In Situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion Using Deep Learning

Gaikwad, Aniruddha; Imani, Farhad; Yang, Hui; Reutzel, Edward W.; Rao, Prahalada

doi:10.1520/ssms20190027

Cited by 15 publications

(16 citation statements)

References 29 publications

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“…Recent advances in machine learning (ML) technologies based on deep neural networks (NNs) have successfully demonstrated their capability in assisting industrial manufacturing [14]. For example, researchers have reported the use of an NN-based ML technique to help optimize the processing parameters of inkjet-based 3D printing [15], fused filament fabrication-based 3D printing [16,17], and laser powder bed fusion [18][19][20][21]. However, to the best of our knowledge, using ML to assist in DLP-based 3D printing has not yet been reported.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning

You

Guan

Alido

et al. 2020

Journal of Manufacturing Science and Engineering

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When using light-based three-dimensional (3D) printing methods to fabricate functional micro-devices, unwanted light scattering during the printing process is a significant challenge to achieve high-resolution fabrication. We report the use of a deep neural network (NN)-based machine learning (ML) technique to mitigate the scattering effect, where our NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. Furthermore, the NN was used to model an inverse 3D printing process, where it took desired printed structures as inputs and subsequently generated grayscale digital masks that optimized the light exposure dose according to the desired structures’ local features. Verification results showed that using NN-generated digital masks yielded significant improvements in printing fidelity when compared with using masks identical to the desired structures.

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Section: Introductionmentioning

confidence: 99%

Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning

You

Guan

Alido

et al. 2020

Journal of Manufacturing Science and Engineering

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“…The new research question is whether and how design parameters influence the quality of AM builds? Our prior work has designed and performed experiments on an LPBF machine to investigate how design parameters (i.e., height, width, recoating orientation, and hatching pattern) impact the quality in the final build of thin-wall structures [59], [60] that are widely used in the fabrication of heat exchangers. As shown in Fig.…”

Section: A Engineering Design Versus Build Qualitymentioning

confidence: 99%

“…The defect level refers to the number and degree of defects in each layer of the thin wall. These quality characteristics are tracked from one layer to another for the detection of the impending collapse of thin-wall failures (see [59] and [60] for the analysis of variance with respect to design parameters).…”

Section: A Engineering Design Versus Build Qualitymentioning

confidence: 99%

Six-Sigma Quality Management of Additive Manufacturing

et al. 2021

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| Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, Manuscript

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“…In addition, the ground truth obtained from CAD files makes it easy to apply and prevents postprocessing data collection. A layerwise monitoring approach to detect build failure using a DCNN was also suggested by Gaikwad et al [128]. X-ray computed tomography was used to obtain ground truth.…”

Section: Vision Sensormentioning

confidence: 99%

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

et al. 2021

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One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

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In Situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion Using Deep Learning

Cited by 15 publications

References 29 publications

Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning

Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning

Six-Sigma Quality Management of Additive Manufacturing

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

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