A systematic literature review on recent trends of machine learning applications in additive manufacturing

Xames, Md Doulotuzzaman; Torsha, Fariha Kabir; Sarwar, Ferdous

doi:10.1007/s10845-022-01957-6

Cited by 54 publications

(8 citation statements)

References 170 publications

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“…Note that, the title and the abstract belong to the same article. This article (Xames et al, 2022) got published in the Journal of Intelligent Manufacturing in May 2022.…”

Section: Appendixmentioning

confidence: 99%

ChatGPT for research and publication: Opportunities and challenges

2023

JALT

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“…Note that, the title and the abstract belong to the same article. This article (Xames et al, 2022) got published in the Journal of Intelligent Manufacturing in May 2022.…”

Section: Appendixmentioning

confidence: 99%

ChatGPT for research and publication: Opportunities and challenges

2023

JALT

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“…Despite the intrinsic suitability of VP to ML modeling, only a few studies have applied ML tools to VP. [23][24][25][26] This adoption lags considerably behind applications of ML in metal AM. [23] You et al applied machine learning to predict the lateral extent of polymerization of larger photopatterns [26] ; however, to date, no VP ML studies have sought to quantitatively predict 3D voxel and sub-voxel scale geometry.…”

Section: Introductionmentioning

confidence: 99%

“…[23][24][25][26] This adoption lags considerably behind applications of ML in metal AM. [23] You et al applied machine learning to predict the lateral extent of polymerization of larger photopatterns [26] ; however, to date, no VP ML studies have sought to quantitatively predict 3D voxel and sub-voxel scale geometry. The lack of development in this space is attributed to a lack of demonstrated VP characterization tools capable of generating the big, high-resolution data sets necessary for model training.…”

Section: Introductionmentioning

confidence: 99%

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A Data‐Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U‐Nets and Generative Adversarial Networks

Killgore

Kolibaba

Caplins

et al. 2023

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Data‐driven U‐net machine learning (ML) models, including the pix2pix conditional generative adversarial network (cGAN), are shown to predict 3D printed voxel geometry in digital light processing (DLP) additive manufacturing. A confocal microscopy‐based workflow allows for the high‐throughput acquisition of data on thousands of voxel interactions arising from randomly gray‐scaled digital photomasks. Validation between prints and predictions shows accurate predictions with sub‐pixel scale resolution. The trained cGAN performs virtual DLP experiments such as feature size‐dependent cure depth, anti‐aliasing, and sub‐pixel geometry control. The pix2pix model is also applicable to larger masks than it is trained on. To this end, the model can qualitatively inform layer‐scale and voxel‐scale print failures in real 3D‐printed parts. Overall, machine learning models and the data‐driven methodology, exemplified by U‐nets and cGANs, show considerable promise for predicting and correcting photomasks to achieve increased precision in DLP additive manufacturing.

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“…2022, 12, 6610 2 of 16 finishing processes in order to control their appearance in the same way that can be carried out for other technical and technological surface features [7][8][9]. Strong synergies with other Industry 4.0 axes such as machine learning [10] and deep learning techniques [11] are also possible, or in connection with new manufacturing processes such as additive manufacturing [12,13] or remanufacturing [14] in a general context, where we seek to achieve more eco-efficient production methods [15]. Indeed, the challenges associated with the inspection and control of the visual quality of products can constitute important levers for the development and control of these processes [16].…”

Section: Introductionmentioning

confidence: 99%

Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection

et al. 2022

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This work investigates the use of Reflectance Transformation Imaging (RTI) rendering for visual inspection. This imaging technique is being used more and more often for the inspection of the visual quality of manufactured surfaces. It allows reconstructing a dynamic virtual rendering of a surface from the acquisition of a sequence of images where only the illumination direction varies. We investigate, through psychometric experimentation, the influence of different essential parameters in the RTI approach, including modeling methods, the number of lighting positions and the measurement scale. In addition, to include the dynamic aspect of perception mechanisms in the methodology, the psychometric experiments are based on a design of experiments approach and conducted on reconstructed visual rendering videos. The proposed methodology is applied to different industrial surfaces. The results show that the RTI approach can be a relevant tool for computer-aided visual inspection. The proposed methodology makes it possible to objectively quantify the influence of RTI acquisition and processing factors on the perception of visual properties, and the results obtained show that their impact in terms of visual perception can be significant.

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A systematic literature review on recent trends of machine learning applications in additive manufacturing

Cited by 54 publications

References 170 publications

ChatGPT for research and publication: Opportunities and challenges

ChatGPT for research and publication: Opportunities and challenges

A Data‐Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U‐Nets and Generative Adversarial Networks

Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection

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