Zhang Hai-ou, Rui Wang, Liye Liang and Wang Gui-lan
The paper aims to introduce the fabrication of a medium steel aircraft part by hybrid deposition and micro-rolling technology (HDMR) and illustrate its advantages, microstructure…
Abstract
Purpose
The paper aims to introduce the fabrication of a medium steel aircraft part by hybrid deposition and micro-rolling technology (HDMR) and illustrate its advantages, microstructure features and mechanical properties of the part.
Design/methodology/approach
The HDMR technology contains two procedures happening almost at the same time: the welding deposition procedure and then the micro-rolling procedure. It takes the gas metal arc welding as the heat source to melt a metal wire and deposit metal in the welding deposition procedure. The metal just deposited is rolled synchronously by a micro roller following the welding torch in micro-rolling procedure almost at the same time layer by layer. The paper presents a contrast of the grain morphology of metal parts produced respectively by HDMR and freedom arc deposition (FAD) and the mechanical properties of metal parts of the same metal from HDMR casting, forging and FAD methods.
Findings
HDMR breaks the dendrite grain of welding beads into the fine crisscross grains. The mechanical properties of metal parts are improved distinctly by the micro-rolling procedure compared to casting, forging and FAD.
Practical implications
In addition, the application of HDMR technology has succeeded in the fabrication of an eligible aircraft metal part, which is quite difficult to achieve using other additive manufacturing (AM) or casting technologies.
Originality/value
HDMR has the advantage of equiponderance manufacturing by micro-rolling compared to other AM technologies. The metal part fabricated by HDMR technology obtains the fine crisscross grains and brings hope for AM metal components with excellent mechanical properties for aircraft applications.
Details
Keywords
Meiwen Li, Liye Xia, Qingtao Wu, Lin Wang, Junlong Zhu and Mingchuan Zhang
In traditional Chinese medicine (TCM), the mechanism of disease (MD) constitutes an essential element of syndrome differentiation and treatment, elucidating the mechanisms…
Abstract
Purpose
In traditional Chinese medicine (TCM), the mechanism of disease (MD) constitutes an essential element of syndrome differentiation and treatment, elucidating the mechanisms underlying the occurrence, progression, alterations and outcomes of diseases. However, there is a dearth of research in the field of intelligent diagnosis concerning the analysis of MD.
Design/methodology/approach
In this paper, we propose a supervised Latent Dirichlet Allocation (LDA) topic model, termed MD-LDA, which elucidates the process of MDs identification. We leverage the label information inherent in the data as prior knowledge and incorporate it into the model’s training. Additionally, we devise two parallel parameter estimation algorithms for efficient training. Furthermore, we introduce a benchmark MD identification dataset, named TMD, for training MD-LDA. Finally, we validate the performance of MD-LDA through comprehensive experiments.
Findings
The results show that MD-LDA is effective and efficient. Moreover, MD-LDA outperforms the state-of-the-art topic models on perplexity, Kullback–Leibler (KL) and classification performance.
Originality/value
The proposed MD-LDA can be applied for the MD discovery and analysis of TCM clinical diagnosis, so as to improve the interpretability and reliability of intelligent diagnosis and treatment.