Method for Structural Optimization Design of Wafer Handling Robot Arms

Liu, Yanjie; Wu, Mingyue; Wang, Gang; Cai, Hao

doi:10.3901/jme.2015.01.001

Cited by 15 publications

(4 citation statements)

References 2 publications

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“…With the design variables in Table 8 as inputs and the calculated results in Table 9 as outputs, the response surface models of the four objective functions are obtained as in (14)∼ (17), respectively, where ( ) is the mass function, 1 ( ) is the first natural frequency function, max ( ) is the maximum stress function, and max ( ) is the maximum deformation function.…”

Section: Static Analysis Of the End Effector Mounting Bracketmentioning

confidence: 99%

“…Liu et al researched structure optimization problem on wafer handling robot arm, with the natural frequency of the robotic arm as the optimization objective, the end effector static deformation as constraints, and wall thickness and mass of the second and the third arm as optimization variables; the results show that optimization improved the natural frequency of the arm and reduced the amount of deformation of the end effector [14]; Ye et al researched lightweight design on the humanoid robot frameworks. They adopted evolutionary structural optimization method and took into account some factors such as mass, maximum stress, and the first natural frequency [15].…”

Section: Introductionmentioning

confidence: 99%

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Multicriteria Optimization Design for End Effector Mounting Bracket of a High Speed and Heavy Load Palletizing Robot

Mei

Zang

et al. 2018

Mathematical Problems in Engineering

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End effector mounting bracket is an important load bearing part of high speed and heavy load palletizing robot, which is located at the most distant point in robot rotation radius and frequently works in complex conditions such as start-stop, switch direction, and acceleration and deceleration motion; therefore, optimizing design for its structure is beneficial to improve the dynamic performance of robotic system and reduce energy consumption. Firstly, finite element model of end effector mounting bracket was established, and its accuracy was verified by contrastive analysis of modal test result and finite element model. Secondly, through modal analysis, vibration response test, frequency response analysis, and the static analysis, taking inertia into account, the mass is minimized, the maximal stress is minimized, the maximal deformation is minimized, and the first natural frequency is maximized as the optimization objectives are determined; the design variables were selected by sensitivity analysis, taking their value range as the constraint conditions; approximation models of objective functions were established by the Box-Behnken design and the response surface methodology, and their reliability was validated; to determine weighting factor of each optimization objective, an analytic hierarchy process based on finite element analysis (FEA + AHP) method was put forward to improve the objectivity of comparison matrix; subsequently, the multicriteria optimization mathematical model was established by the methods mentioned above. Thirdly, the multicriteria optimization problem was solved by the NSGA-II algorithms and optimization results were obtained. Finally, the contrastive analysis results between optimized model and initial model showed that, in the case of the maximum stress and deformation within allowable values range, the mass reduction was 17.8%; meanwhile, the first natural frequency was increased, and vibration response characteristics of the entire structure were improved significantly. The validity of this optimization design method was verified.

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Section: Static Analysis Of the End Effector Mounting Bracketmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multicriteria Optimization Design for End Effector Mounting Bracket of a High Speed and Heavy Load Palletizing Robot

Mei

Zang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…CAE software is used to optimize the single structural part of robots in order to achieve lightweight and high stiffness in Bugday and Karali (2019), Lohmeier et al (2009), and Luo et al (2019). A structural optimization design method of the wafer handling robot is presented in Liu et al (2015) with the static elastic deformation as the constraint, the links are equivalent to hollow cross-section beams, and the thicknesses of it are determined as the optimization parameters. A multi-objective optimization method based on CAE software is proposed to optimize the structural dimensions of a slender robot arm in Luo et al (2012), and the selected pose for optimization is the lowest accuracy pose of the robot.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective global optimum design of collaborative robots

Wang

Pan

2020

Struct Multidisc Optim

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Optimum design is proven significant for improving task performances of robotic manipulators under certain constraints. However, when it is utilized for collaborative robots (Cobots), there are still many challenges such as complex smooth surface links, time-varying kinematic configurations, computational expensiveness, and nonstructural parameter optimization. Therefore, based on orthogonal design experiment (ODE) and finite element substructure method (FESM), a multi-objective optimum design method of Cobots is proposed with the structural dimensions and parameterized joint components as the optimization variables and the natural frequency, the Cartesian stiffness, and the mass of the robot as optimization objectives. Firstly, to obtain multiple global performance indexes (GPIs) of robots in real-time and efficiently, the FESM model of Cobots is established which can preserve the accuracy of the finite element method (FEM) while ensuring the computational efficiency. Then, the gray relational analysis method (GRAM) is used to construct the multi-objective optimization function which includes the global first-order natural frequency index (GFNFI), the global elastic deformation index (GEDI), and the mass of robots. The ODE is constructed, and the structural dimensions and parameterized joint components are taken as influencing factors. According to the orthogonal array (OA), the degree of gray incidence under different levels of influencing factors is solved. And the optimal combination of influencing factor levels is obtained by range analysis (RA), which is used to guide the design of Cobots. Finally, a Cobot SHIR5-I is taken as an illustrative example to perform optimum design in this paper.

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“…There are many researches on reducing the energy consumption of robots. The main methods include optimization of robot structure [6][7][8], trajectory planning and so on. Optimization of robot structure method is especially suitable for robot structure design stage.…”

Section: Introductionmentioning

confidence: 99%

Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints

Mei

Fang

et al. 2018

Mathematical Problems in Engineering

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Palletizing robot is widely used in logistics operation. At present, people pay attention to not only the loading capacity and working efficiency of palletizing robots, but also the energy consumption in their working process. This paper takes MD1200-YJ palletizing robot as the research object and studies the problem of low energy consumption optimization of joint driving system from the perspective of trajectory optimization. Firstly, a multifactor dynamic model of palletizing robot is established based on the conventional inverse rigid body dynamic model of the robot, the Stribeck friction model and the spring balance torque model. And then based on joint torque, friction torque, motion parameter, and joule’s law, the useful work model, thermal loss model of joint motor, friction energy consumption model of joint system, and total energy consumption model of palletizing robot are established, and through simulation and experiment, the correctness of the multifactor dynamic model and energy consumption model is verified. Secondly, based on the Fourier series approximation method to construct the joint trajectory expression, the minimum total energy consumption as the optimization objective, with coefficients of Fourier series as optimization variables, the motion parameters of initial and final position, and running time constant as constraint conditions, the genetic algorithm is used to solve the optimization problem. Finally, through the simulation analysis the optimized Fourier series motion law and the 3-4-5 polynomial motion law are comprehensively evaluated to verify the effectiveness of the optimization method. Moreover, it provides the theoretical basis for the follow-up research and points out the direction of improvement.

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Method for Structural Optimization Design of Wafer Handling Robot Arms

Cited by 15 publications

References 2 publications

Multicriteria Optimization Design for End Effector Mounting Bracket of a High Speed and Heavy Load Palletizing Robot

Multicriteria Optimization Design for End Effector Mounting Bracket of a High Speed and Heavy Load Palletizing Robot

Multi-objective global optimum design of collaborative robots

Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints

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