In-situ capture of melt pool signature in selective laser melting using U-Net-based convolutional neural network

Fang, Qihang; Tan, Zhenbiao; Li, Hui; Shen, Shengnan; Liu, Sheng; Song, Changhui; Zhou, Xin; Yang, Yongqiang; Wen, Shifeng

doi:10.1016/j.jmapro.2021.05.052

Cited by 33 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu, X. proposed a deep learning method based on transfer learning and self supervised learning to recognize CT image data set and detect cov1d-19, achieving 85% F 1 value and 0.94 AUC 11 . Fang, Q. used a government study to analyze 3epidemic from chest XR images and proposed interventions and integrations that allow clinicians to benefit from the rich benefits of personal data while protecting privacy 12 . Alhudhaif, A. Presenting a deep learning model for visual image extraction from chest volume CT images for epidemic detection.…”

Section: Related Workmentioning

confidence: 99%

“…Among them, accuracy refers to the percentage of correctly identified samples for each sample. As shown in formula (11); The truth is expressed by the formula (12), Represents the ratio of the number of identified samples to the total number of sequences to be analyzed; As shown in Equation ( 13), the sensitivity, i.e., the recovery rate, is used to measure the sample recognition; There can be both sensitivity and accuracy. derived from the confusion matrix.…”

Section: Evaluation Indexmentioning

confidence: 99%

See 1 more Smart Citation

Image classification and discrimination of COVID pneumonia based on convolutional neural network

Qin,

Liu,

Che

et al. 2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

View full text Add to dashboard Cite

To address the negative quality of RT-PCR and the time taken to obtain results, this paper reports on the classification and discrimination of COVID pneumonia images by developing a neural network adaptively. Given the limited number of published COVID-19 CT data currently available, we suggest using the proposed negative process to refine the data to obtain more CT data to reduce matching risk; Experimental results show that compared to network models such as AlexNet and GoogleNet, the proposed BUF-Net network model has the best performance with an accuracy of 93%. Visualization of systemic findings using Grad-CAM techniques may further elucidate the critical role of CT imaging in the diagnosis of COVID-19.Conclusion: The use of deep learning tools in clinical practice can help radiologists make better diagnoses.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Evaluation Indexmentioning

confidence: 99%

Image classification and discrimination of COVID pneumonia based on convolutional neural network

Qin,

Liu,

Che

et al. 2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

View full text Add to dashboard Cite

show abstract

“…Although these methods are able to achieve the basic needs of extracting the features of the weld pool surface, their accuracy and efficiency are still need to be improved due to the limitations of algorithms or hardware devices. In recent years, a large number of welding image processing methods based on deep learning technology are proposed which including (i) convolutional neural network (CNN)-based methods [7][8][9][10] ; (ii) recurrent neural network (RNN)-based methods [11][12] ; and (iii) generative adversarial network (GAN)-based methods [13] . These methods not only improve the accuracy and speed of weld pool feature extraction, but also lay the foundation for achieving full automation and intelligence of welding.…”

Section: Introductionmentioning

confidence: 99%

Research on the calibration algorithm of the GTAW weld pool analytical model designed for time-varying welding speed situation

Wu,

Qu,

Cheng

2023

Preprint

View full text Add to dashboard Cite

The weld pool analytical solution is an ideal method to visualize the thermal behavior of the welding process, to clarify the physical relationship between the welding process parameters and the shape of the weld pool and to quickly calculate the penetration. In previous work, the weld pool analytical model designed for time-varying welding speed situation was derived. The experimental results show that the maximum error of the calculated penetration is 18.91%. In order to improve the computational accuracy of the analytical model, this paper proposes a calibration method based on the in-situ reconstructed weld pool surface. Firstly, the RES-BiSeNet model based on convolutional neural network is built to extract the features of the weld pool surface. Then, the calibration parameters are obtained to calibrate the weld pool analytical model. The accuracy of the calibrated analytical model was verified through the welding experiments. The results show that the maximum calibrated penetration error is reduced to 9.7%. Finally, the influence of the heat source parameters on analytical model is discussed.

show abstract

“…Properly trained ANNs can model the correlations between the given input and output data and accordingly predict the responses based on unseen input values. ANNs have been employed in the SLM process in various ways; some of the applications are the design for AM, including topology and material design [52], in-situ process monitoring, including melt pool or powder bed monitoring using optical or acoustic techniques [53][54][55][56], and process-property correlation [57,58]. Recently, the process-property correlation application of the ANN models has been extended to the optimization of this process for different materials.…”

Section: Introductionmentioning

confidence: 99%

Experimental, Computational, and Data-Driven Study of the Effects of Selective Laser Melting (SLM) Process Parameters on Single-Layer Raster Scanning Surface Characteristics

Fotovvati

Rauniyar²,

Arnold³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this study, the influence of selective laser melting (SLM) parameters on the surface characteristics of “single-layer” raster scanning deposits, with Ti6Al4V powder, is thoroughly investigated through experimental, computational, and data-driven approaches. In layer-wise manufacturing processes, such as SLM, the evaluation and understanding of the surface characteristics of individual layers of materials are important; however, there is limited work in the literature on the “single-layer” surface characteristics in SLM. Single-layer samples are designed on a smooth surface on top of semi-cylinders and fabricated using different levels of SLM processing parameters, i.e., laser power, scan speed, and hatch spacing, in different numbers of scan lines, i.e., 5, 10, 20, and 40. The fabricated sample surface data are acquired using non-contact optical interferometry. The computational approach utilizes the discrete element method (DEM) for the powder bed distribution, as well as computational fluid dynamics (CFD) for simulating the laser-powder interaction. One of the main contributions of this study is to investigate and provide predictive models for the correlations between the surface roughness of raster scans and SLM process parameters through experiments and computational work. In addition, a method for comparing the surface characteristics of the thermo-fluid simulation and experimental results is presented. A technique is employed to extract the surface roughness from the simulated results. The discussion on the experiment-simulation comparison challenges provides a deep insight into the criteria selection for experiment-simulation comparison in the SLM process. Moreover, an artificial neural network was trained using the back-propagation method and K-fold cross-validation, as well as regularization techniques to avoid overfitting. The laser power proved to be the most significant parameter, having a contribution of 48.3%, in determining the surface roughness with a unique trend affected by the spattering of powder particles. The simulation results showed noticeable deviations from the experiments in cases with extreme parameters, e.g., too high or too low values of power, speed, or hatch spacing. The machine learning algorithm achieves reasonable predictability of the single-layer surface roughness, showing a root mean square error and a coefficient of determination of 0.9589 µm and 98.78%, respectively, for a separate testing dataset.

show abstract

In-situ capture of melt pool signature in selective laser melting using U-Net-based convolutional neural network

Cited by 33 publications

References 19 publications

Image classification and discrimination of COVID pneumonia based on convolutional neural network

Image classification and discrimination of COVID pneumonia based on convolutional neural network

Research on the calibration algorithm of the GTAW weld pool analytical model designed for time-varying welding speed situation

Experimental, Computational, and Data-Driven Study of the Effects of Selective Laser Melting (SLM) Process Parameters on Single-Layer Raster Scanning Surface Characteristics

Contact Info

Product

Resources

About