• Title/Summary/Keyword: LCD 유리 이송 로봇

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Six Sigma Robust Design of Composite Hand for LCD Glass Transfer Robot (LCD 유리 이송용 복합재료 로봇 핸드의 식스 시그마 강건설계)

  • Nam Hyunwook
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.3 s.234
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    • pp.455-461
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    • 2005
  • This research studied robust design of composite hand for LTR (LCD glass Transfer Robot). $1^{st}$ DOE (Design of Experiment) was conducted to find out vital few Xs. 108 experiments were performed and their results were statistically analyzed. Pareto chart analysis shows that the geometric parameters (height and width of composite beam) are more important than material parameters $(E_{1},\;E_{2})$ or stacking sequence angle. Also, the stacking sequence of mid-layer is more important than that of outer-layer. The main effect plots shows that the maximum deflection of LTR hand is minimized with increasing height, width of beam and layer thickness. $2^{nd}$ DOE was conducted to obtain RSM (Response Surface Method) equation. 25 experiments were conducted. The CCD (Central Composite Design) technique with four factors was used. The coefficient of determination $(R^{2})$ for the calculated RSM equation was 0.989. Optimum design was conducted using the RSM equation. Multi-island genetic algorithm was used to optimum design. Optimum values for beam height, beam width, layer thickness and beam length were 24.9mm, 186.6mnL 0.15mm and 2402.4mm respectively. An approximate value of 0.77mm in deflection was expected to be a maximum under the optimum conditions. Six sigma robust design was conducted to find out guideline for control range of design parameter. To acquire six sigma level reliability, the standard deviation of design parameter should be con trolled within $2{\%}$ of average design value

The Study on the Development of Composite Robot Hand for TFT-LCD Glass Transport (대면적 TFT-LCD 유리기판 이송용 복합재료 로봇 손 개발에 관한 연구)

  • Choi, Gi-Han;Han, Chang-Woo;Lee, Sang-Ryong
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.7
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    • pp.1357-1365
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    • 2002
  • A robot hand is used to transport the glass substrate in TFT-LCD manufacturing process. Carbon/epoxy composite is one of the best materials for this kind of robot hand application, due to their lightweight, high stiffness, and good damping characteristics. Major requirement of the robot hand is given as allowable deflection under weight loading of glass substrate and robot hand itself. In this thesis, a carbon/epoxy robot hand was analyzed using finite element method and beam theory to determine the deflection of the hand under the loading that is equivalent to actual weight. Because natural frequency is one of the major interests in robot hand design for TFT-LCD manufacturing process, modal analysis is also conducted using finite element method and beam theory. A robot hand was manufactured, and actual deflection and natural frequency were measured to verify the analysis results and compliance to requirement. The test results showed good agreement with analysis results.

Stiffness Enhancement of Piecewise Integrated Composite Robot Arm using Machine Learning (머신 러닝을 이용한 PIC 로봇 암 강성 향상에 대한 연구)

  • Ji, Seungmin;Ham, Seokwoo;Cheon, Seong S.
    • Composites Research
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    • v.35 no.5
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    • pp.303-308
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    • 2022
  • PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.