• Title/Summary/Keyword: Shape Prediction

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A Study on Weldability and Prediction of Nugget Shape in Dissimiar Metal Arc Spot Weld (이종 금속의 아크 스폿 용접성 및 접합부 형상 예측에 관한 연구)

  • Kim, Gi Sun;Jang, Gyeong Bok;Gang, Seong Su
    • Journal of Welding and Joining
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    • v.18 no.2
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    • pp.184-184
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    • 2000
  • In this study, the lap welding between austenitic stainless steel and carbon steel was carried out using arc spot welding process and weldability of welded specimens was estimated. From the tensile-shear strength test, micro Vickers hardness test, and microstructure observation, specimen of 6.5mm(hole of upper plate) showed the best results in terms of tensile-shear strength and nugget shape. And there was an unmixed zone in fusion boundary between the carbon steel base metal and bulk weld metal. This zone had very thin width with the hard microstructure. The shape of weld nugget in arc spot welding of dissimilar metal welds was predicted by searching thermal history of a weld joint through a three-dimensional finite element model. From the numerical analysis, predicted the shape of weld nugget showed good agreement with the experiment(Received August 24, 1999)

Prediction of Nuggest Shape by Finite Element Modeling in Arc-spot Welding (유한요소 모델링을 이용한 아크 스폿 용접의 너깃 형상 예측)

  • 황종근;장경복;김기순;강성수
    • Journal of Welding and Joining
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    • v.17 no.2
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    • pp.84-90
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    • 1999
  • The shape of weld nuggest in arc spot welding of 304 stainless steel was found by searching thermal history of a weld joint through a three-dimensional finite element model. The problem consists of one in which the finite element mesh is growing continuously in time in order to accomodate metal transfer in arc spot welding using element rebirth technique. The analysis was performed on the basis of experimental results. The finite element program MARC, along with a few user subroutines, was employed to obtain the numerical results. Temperature-dependent thermal properties, stir effect in weld pool, effect of phase transformation, and the convective and radiative boundary conditions are included in the model. Numerically predicted shape of weld nuggest is compared with the experimentally observed shape.

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A Study on Weldability and Prediction of Nugget Shape in Dissimilar Metal Arc Spot Weld (이종 금속의 아크 스폿 용접성 및 접합부 형상 예측에 관한 연구)

  • 김기순;장경복;강성수
    • Journal of Welding and Joining
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    • v.18 no.2
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    • pp.57-63
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    • 2000
  • In this study, the lap welding between austenitic stainless steel and carbon steel was carried out using arc spot welding process and weldability of welded specimens was estimated. From the tensile-shear strength test, micro Vickers harness test, and microstructure observation, specimen of $psi6.5mm$(hole of upper plate) showed the best results in terms of tensile-shear strength and nugget shape. And there was an unmix zone in fusion boundary between the carbon steel base metal and bulk weld metal. This zone had very width with the hard microstructure. The shape of weld nugget in arc spot welding of dissimilar metal melds was predicted by searching thermal history of a weld joint through a three-dimensional finite element model. From the numerical analysis, predicted the shape of weld nugget showed good agreement with the experiment.

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Shape morphing and adjustment of pantographic morphing aerofoil section structure

  • Saeed, Najmadeen M.;Kwan, Alan S.K.
    • Smart Structures and Systems
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    • v.24 no.2
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    • pp.193-207
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    • 2019
  • This study concerns with morphing structures, e.g. as applied in the aerospace industry. A morphing aerofoil structure capable of variable geometry was developed, which was shown to be able to cater for the different aerodynamic requirements at different stages of flight. In this work, the useful and relatively simple method has been applied, which provides a direct method for calculating required morphing shape displacements via finding the most effective bar through calculating bar sensitivity to displacement and calculating set of length actuations for bar assembly to control/adjust shape imperfection of prestressable structural assemblies including complex elements ("macro-elements", e.g., the pantographic element), involving Matrix Condensation. The technique has been verified by experiments on the physical model of an aerofoil shaped morphing pantographic structure. Overall, experimental results agree well with theoretical prediction. Furthermore, the technique of multi-iteration adjustment was presented that effective in eliminating errors that occur in the practical adjustment process itself. It has been demonstrated by the experiments on the physical model of pantographic morphing structure. Finally, the study discusses identification of the most effective bars with the objective of minimal number of actuators or minimum actuation.

Customization using Anthropometric Data Deep Learning Model-Based Beauty Service System

  • Wu, Zhenzhen;Lim, Byeongyeon;Jung, Hoekyung
    • Journal of information and communication convergence engineering
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    • v.19 no.2
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    • pp.73-78
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    • 2021
  • As interest in beauty has increased, various studies have been conducted, and related companies have considered the anthropometric data handled between humans and interfaces as an important factor. However, owing to the nature of 3D human body scanners used to extract anthropometric data, it is difficult to accurately analyze a user's body shape until a service is provided because the user only scans and extracts data. To solve this problem, the body shape of several users was analyzed, and the collected anthropometric data were obtained using a 3D human body scanner. After processing the extracted data and the anthropometric data, a custom deep learning model was designed, the designed model was learned, and the user's body shape information was predicted to provide a service suitable for the body shape. Through this approach, it is expected that the user's body shape information can be predicted using a 3D human body scanner, based upon which a beauty service can be provide.

Three-dimensional numerical simulation for the prediction of product shape in sheet casting process

  • Chae, Kyung-Sun;Lee, Mi-Hye;Lee, Seong-Jae;Lee, Seung-Jong
    • Korea-Australia Rheology Journal
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    • v.12 no.2
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    • pp.107-117
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    • 2000
  • Prediction of the product shape in sheet casting process is performed from the numerical simulation. A three-dimensional finite element method is used to investigate the flow behavior and to examine the effects of processing conditions on the sheet produced. Effects of inertia, gravity, surface tension and non-Newtonian viscosity on the thickness profile of the sheet are considered since the edge bead and the flow patterns in the chill roll region have great influence on the quality of the products. In the numerical simulation with free surface flows, the spine method is adopted to update the free surface, and the force-free boundary condition is imposed along the take-up plane to avoid severe singularity problems existing at the take-up plane. From the numerical results of steady isothermal flows of a generalized Newtonian fluid, it is shown that the draw ratio plays a major role in predicting the shape of the final sheet produced and the surface tension has considerable effect on the bead thickness ratio and the bead width fraction, while shear-thinning and/or tension-thickening viscosity affect the degree of neck-in.

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A Study on the Prediction of Mass and Length of Injection-molded Product Using Artificial Neural Network (인공신경망을 활용한 사출성형품의 질량과 치수 예측에 관한 연구)

  • Yang, Dong-Cheol;Lee, Jun-Han;Kim, Jong-Sun
    • Design & Manufacturing
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    • v.14 no.3
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    • pp.1-7
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    • 2020
  • This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.