X-ray diffraction measurements and computational prediction of residual stress mitigation scanning strategies in powder bed fusion additive manufacturing

Zhang, Wenyou; Guo, Da; Wang, Lin; Davies, Catrin M.; Mirihanage, Wajira; Tong, Min; Harrison, Noel M.

doi:10.1016/j.addma.2022.103275

Cited by 9 publications

(3 citation statements)

References 50 publications

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“…Specifically, our method improves 8.8%, 6.4%, and 6.2% of the average performance of the benchmark methods regarding their F-score, Precision, and Recall, respectively, in all case studies. The outstanding performances of the proposed method in various case studies validate that the method could significantly contribute to many applications area using spectral data, such as radiology 45 and additive manufacturing 46 . In addition, it would be an interesting future research topic to generate the minority data from the test data for the users who need to assess the performance of the methods requiring balanced test data.…”

Section: Discussionmentioning

confidence: 68%

Imbalanced spectral data analysis using data augmentation based on the generative adversarial network

Chung,

Zhang,

Saimon

et al. 2024

Sci Rep

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Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier’s F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method’s broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering.

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Section: Discussionmentioning

confidence: 68%

Imbalanced spectral data analysis using data augmentation based on the generative adversarial network

Chung,

Zhang,

Saimon

et al. 2024

Sci Rep

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“…The usage of high energies leads to increased cracks and layer detachment due to higher residual stresses that form during the PBF-LB/M process. The work of Zhang et al [35] and Patcharapit, [36] who examined the influence of the PBF-LB/M process parameters on residual stress formation, supports this statement. According to the works above, a 90°scan strategy, low scan lengths (or v s ), and E V result in low residual stress formation.…”

Section: Determination Of Suitable Process Parametersmentioning

confidence: 85%

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Vehrs,

Röttger,

Roj

et al. 2024

Adv Eng Mater

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This work considers the processing of the hard‐phase containing CoCrW‐alloy Celsit F using powder bed fusion‐laser beam/metal (PBF‐LB/M), whereby a suitable process window for producing dense parts having a low‐defect density is determined. Several cuboid specimens are manufactured by varying the exposure parameters (laser power, hatch distance, scanning speed). With the help of quantitative image analysis, the samples are evaluated regarding crack density and porosity, and optimal exposure parameters are derived. Dense samples with a low defect density can be produced if the energy density is 60–70 J mm−3 and the base‐plate is preheated to 800 °C. Choosing lower energy densities leads to increased pore formation, whereas higher energy densities result in increased ablation and cracking. With a view to a later application in which additively manufactured hot work tools are made of Celsit F, the possibility of introducing internal cavities as potential cooling channels is considered. It can be shown that the hardness sideways is lower than above and below the cavity due to a changed microstructure and locally different heat dissipation. The results of this work give a first guideline for the PBF‐LB/M‐processing of wear‐resistant CoCrW alloys with a high hard phase volume content to tools for hot working.

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“…It includes mainly laser energy input, which is affected by the laser power, scan speed, or layer thickness [16][17][18]. Also, the laser scanning strategy turned out to be significant, which is confirmed by the increasing number of publications devoted to this parameter [19][20][21]. An earlier study, made by Mercelis and Kruth [22], mentions the influence of the number of layers on the residual stress state of SLM components.…”

Section: Introductionmentioning

confidence: 89%

Stress Relieving Heat Treatment of 316L Stainless Steel Made by Additive Manufacturing Process

Gel’atko,

Hatala,

Botko

et al. 2023

Materials

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Residual stress occurs in the materials after different methods of processing due to the application of pressure and/or thermal gradient. The occurrence of residual stresses can be observed in both subtractive and additive-manufactured (AM) materials and objects. However, pressure residual stresses are considered, in some cases, to have a positive effect; there are applications where the neutral stress state is required. As there is a lack of standards describing the heat treatment of AM materials, there is a need for experimental research in this field. The objective of this article is to determine the heat treatment thermal regime to achieve close to zero stress state in the subsurface layer of additively manufactured AM316L stainless steel. The presented objective leads to the long-term goal of neutral etalons for eddy current residual stress testing preparation. A semi-product intended for the experiment was prepared using the Selective Laser Melting (SLM) process and subsequently cut, using Abrasive Water Jet (AWJ) technology, into experimental specimens, which were consequently heat-treated in combination with four temperatures and three holding times. Residual stresses were measured using X-ray diffraction (XRD), and microstructure variations were observed and examined. A combination of higher temperature and longer duration of heat treatment caused more significant stress relaxation, and the original stress state of the material influenced a degree of this relaxation. The microstructure formed of cellular grains changed slightly in the form of grain growth with randomly occurring unmolten powder particles, porosity, and inclusion precipitation.

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X-ray diffraction measurements and computational prediction of residual stress mitigation scanning strategies in powder bed fusion additive manufacturing

Cited by 9 publications

References 50 publications

Imbalanced spectral data analysis using data augmentation based on the generative adversarial network

Imbalanced spectral data analysis using data augmentation based on the generative adversarial network

Laser Powder Bed Fusion of Hard‐Phase Containing Co‐Base Alloy

Stress Relieving Heat Treatment of 316L Stainless Steel Made by Additive Manufacturing Process

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