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RISS 인기검색어
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- 저자 : Xunpeng Qin (검색결과 5 건)
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Zeqi Hu,Xunpeng Qin,Yifeng Li,Mao Ni 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.4
In wire and arc additive manufacturing, the weld bead geometry determined the slicing layer height, which was decided by the welding parameters. Generally, the determination of the welding parameters relied on empirical and experimental data through the trial-and-error methods that incur considerable time and cost. To obtain the proper welding process parameters according to the desired single bead geometry and layer height, a full factorial experimental design matrix was applied to collect the original data of welding parameters and bead geometrical variables. A forward artificial neural network (FANN) was built to predict the bead geometry form the welding parameters. Then, a closed-loop iteration method combined a genetic algorithm (GA) and the FANN model (FANN-GA) was developed to search for the most optimal welding process parameters in accordance with the selected bead geometrical variables. The results confirmed that the FANN-GA model has a good performance on the backward prediction of the welding process parameters compared with the direct backward artificial neural network (BANN). Several groups of single layer multi-bead and multi-layer multi-bead experiment were performed to testify the proposed method, and the relative error between the desired and actual layer height was small. The proposed method makes it possible to fabricate the component with an arbitrary desired layer height, and could be used in the adaptive slicing additive manufacturing or surface coating.
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Huaming Liu,Xunpeng Qin,Song Huang,Lei Jin,Yongliang Wang,Kaiyun Lei 한국정밀공학회 2018 International Journal of Precision Engineering and Vol.19 No.7
This paper aims to establish a correlation between the process parameters and geometrical characteristics of the sectional profile of the single track cladding deposited by high power diode laser with rectangle beam spot. By applying the genetic algorithm and back propagation neural network, a nonlinear model for predicting the geometry features of the single track cladding is developed. A full factorial design method is used to conduct the experiments, and the experimental results are chosen randomly as training dataset and testing dataset for the neural network. Three main input variables such as laser power, scanning speed, and powder thickness were considered. The performance of the genetic algorithm and back propagation artificial neural network was compared to that of the standard back propagation neural network. To improve the accuracy of the neural network, one-hidden-layer and double-hidden-layer neural network with different architectures were performed. Further, one-output and multi-output neural network are also trained and tested. The results indicate that, by using genetic algorithm, the prediction accuracy of the neural network is significantly improved. Meanwhile, the double-hidden-neural network has higher prediction accuracy than the one-hidden-layer-neural network, while the one-output-neural network has higher prediction accuracy than the multi-output-neural network.
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A study on the effect of subsurface crack propagation on rolling contact fatigue in a bearing ring
Song Deng,Xunpeng Qin,Song Huang 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.3
To study the subsurface fatigue crack propagation under rolling contact fatigue (RCF), a subsurface fatigue crack is firstly observed ina ball bearing race. Then, a three-dimensional model of a bearing ring containing a subsurface crack is used to evaluate fatigue crackpropagation based on stress intensity factor (SIF) calculations. Several parameters, such as the crack shape, depth and size, are varied toinvestigate their effects on RCF in the bearing ring.
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Xiaochen Xiong,Xunpeng Qin,Lin Hua,Gang Wan,Shilong Wei,Mao Ni,Zeqi Hu 대한금속·재료학회 2023 METALS AND MATERIALS International Vol.29 No.6
An in-situ follow-up hammering-assisted (FH) wire arc additive manufacturing (WAAM) process is proposed for hydraulicturbine blade repairing. With different hammering intervention temperatures above the austenite recrystallization temperature(Tre-γ), the influence and mechanism of the process on the grain size of prior austenite grains and room-temperaturemartensite, as well as the texture of 0Cr13Ni5Mo deposited layers are systematically studied. The OM, SEM and EBSD areused for characterization. The repairing layer of large-sized blade is dominated with the coarse columnar grains with severalmillimeters in length, and the grain size is rated as grade 0. After the FH process, the prior austenite grains are significantlyrefined to grade 8. As the hammering temperature increases, the recrystallized austenite grains gradually grow and coarsenowing to the higher ambient temperature. FH at 950 ℃, a temperature slightly higher than the Tre-γ can achieve the austenitegrains with excellent grain refinement effect. Meanwhile, thanks to microstructure inheritance, the room-temperature martensiticis also refined from 4.69 to 2.47 μm, and the typical < 100 > fibre texture content in the deposited layer is obviouslyreduced with the texture intensity reduced from 6.68 to 2.95. Furthermore, the yield strength is increased by about 200 MPa. The main strengthening mechanisms are grain refinement strengthening and dislocation strengthening, and the contributionsto the yield strength are 96.1 MPa and 79 MPa respectively. Additionally, the FH process is also expected to simultaneouslyimprove the formability of the blade repaired layer.
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Coupling method of magnetic memory and eddy current nondestructive testing for retired crankshafts
Chen Ni,Lin Hua,Xiaokai Wang,Zhou Wang,Xunpeng Qin,Zhou Fang 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.7
To verify the validity of the Coupling method of magnetic memory and eddy current (CMMEC) testing for crankshafts, we use this technique to test a 12-cylinder V-design diesel crankshaft. First, the stress distribution in the crankshaft was obtained under 12 working conditions using a Finite element (FE) model that complied with the commercial FE code ABAQUS. Second, Magnetic memory testing (MMT) and Eddy current testing (ECT) were adopted to detect the regions of stress concentration in the crankshaft and the specific location of cracks based on simulation results. Lastly, magnetic particle testing was conducted to detect and display the corresponding crack to verify the CMMEC testing results. The MMT and ECT results can provide basis and guidance for the remanufacture and life evaluation of retired crankshafts.
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