Slurry flow characteristics control of 3D printed ceramic core layered structure: Experiment and simulation

Li, Qiaolei; Qiu, Yumin; Hou, Weiqiang; Liang, Jingjing; Zou, Mingke; Li, Jinguo; Zhou, Yizhou; Sun, Xiaofeng

doi:10.1016/j.jmst.2023.02.065

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…Jacobsmühlen [10] Inconel 625, Inconel 718 SVM, RF, SGD Structure defects Zhang [11] Inconel 625 Topography analysis Flatness of powder bed Wang [13] 30CrMnSiNi2A Topography analysis Flatness of powder bed Xiong [14] / Visual binocular vision Thin-walled components Scime [15] Inconel 718 MsCNN Flatness of powder bed Li [12] Metal Topography analysis Flatness of powder bed Gaikwrad [17] Ti-6Al-4V CNN Geometric integrity Liu [18] Ti-6Al-4 V alloy CNN EBM powder bed Bevans [19] Inconel 718 Multi-sensor fusion Part-level and micro defects Repossini [21] Nickel alloy Statistics Melt pool Rodriguez [22] Ti-6Al-4V Regression Melt pool Li [23] ZrO 2 , SiO 2 Finite element simulation Slurry flow Zhao [24] Alumina ceramics Finite element simulation lamellar structure…”

Section: Materials Methods Targetmentioning

confidence: 99%

“…In terms of manipulation of material and process, the focus of research is on adjusting raw materials and process parameters to acquire better performance of AM parts. Li [23] conducted experiments and finite element simulations to delve into the effects of thickness and powder size on the flow properties of slurry during the recoating process. They proposed optimizing the print layer thickness to control the impact of the turbulent zone in front of the scraper.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

High-Performance Defect Detection Methods for Real-Time Monitoring of Ceramic Additive Manufacturing Process Based on Small-Scale Datasets

Jia,

Li,

Wang

et al. 2024

Processes

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Vat photopolymerization is renowned for its high flexibility, efficiency, and precision in ceramic additive manufacturing. However, due to the impact of random defects during the recoating process, ensuring the yield of finished products is challenging. At present, the industry mainly relies on manual visual inspection to detect defects; this is an inefficient method. To address this limitation, this paper presents a method for ceramic vat photopolymerization defect detection based on a deep learning framework. The framework innovatively adopts a dual-branch object detection approach, where one branch utilizes a fully convolution network to extract the features from fused images and the other branch employs a differential Siamese network to extract the differential information between two consecutive layer images. Through the design of the dual branches, the decoupling of image feature layers and image spatial attention weights is achieved, thereby alleviating the impact of a few abnormal points on training results and playing a crucial role in stabilizing the training process, which is suitable for training on small-scale datasets. Comparative experiments are implemented and the results show that using a Resnet50 backbone for feature extraction and a HED network for the differential Siamese network module yields the best detection performance, with an obtained F1 score of 0.89. Additionally, as a single-stage defect object detector, the model achieves a detection frame rate of 54.01 frames per second, which meets the real-time detection requirements. By monitoring the recoating process in real-time, the manufacturing fluency of industrial equipment can be effectively enhanced, contributing to the improvement of the yield of ceramic additive manufacturing products.

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