Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters

Farooq, Syed Saad

doi:10.36909/jer.v9i2.9073

Cited by 13 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Noteworthy is the comparative analysis of the convergence performance of the genetic algorithm (GA) and the particle swarm optimization (PSO), revealing the superior performance of the PSO algorithm. Farooq et al 31 optimized the Tricept mechanism through genetic algorithms to enhance its performance metrics. In a bid to augment existing research endeavors, this study introduces the Weighted Particle Swarm Optimization (PSO) technique for multi-objective optimization, offering a comparative analysis with previous optimization methodologies.…”

Section: Introductionmentioning

confidence: 99%

Optimization of dynamic parameter design of Stewart platform with Particle Swarm Optimization (PSO) algorithm

Shahbazi,

Heidari,

Ahmadzadeh

2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Today motion simulators are being produced rely on electric actuators. The conventional way of dealing with high velocity, accelerations, and bulky payload is using a bigger actuator, but this leads to increased power usage and costs. To overcome these limitations, an optimized design of the Stewart platform design parameter improves simulators’ ability to support the weight of the equipment and satisfy the desired velocity and acceleration. However, it is challenging to set platform design parameters to maintain efficiency across the entire workspace. In this article, the kinematics and dynamics of the six-axis general Stewart robot are explored. A high-rated desired velocity and acceleration for the Stewart platform are defined and simulated. Then, the electric actuator force during some motion trajectory based on the defined workspace, velocity, and acceleration are calculated. Particle Swarm Optimization (PSO) is employed to optimize platform design parameters. The algorithm defines a cost function to minimize the maximum speed and maximum Force of the actuator by examining the structural kinematics arrangement of design parameters. Findings demonstrate that optimized design parameters have been successful in reducing the maximum actuator power 88.3%. Additionally, improves Stewart platform mechanical components’ life. These procedures can be employed for any Stewart platform.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization of dynamic parameter design of Stewart platform with Particle Swarm Optimization (PSO) algorithm

Shahbazi,

Heidari,

Ahmadzadeh

2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…The main disadvantage of GA is that many parameters need to be selected by experiences, such as the operator parameters of crossover rate and mutation rate [34]. Different from the crossover and mutation operating of GA, PSO is easier to be implemented because the search is determined according to its speed [35]. Yun et al [36] found that fitness value of the PSO algorithm is better than that of the GA in the optimization of a 3PUPU parallel mechanism.…”

Section: Introductionmentioning

confidence: 99%

Design and multi-objective optimization of a novel 5-DOF parallel mechanism with two double-driven chains

et al. 2023

View full text Add to dashboard Cite

This paper focuses on the design, analysis, and multi-objective optimization of a novel 5-degrees of freedom (DOF) double-driven parallel mechanism. A novel 5-DOF parallel mechanism with two double-driven branch chains is proposed, which can serve as a machine tool. By installing two actuators on one branch chain, the proposed parallel mechanism can achieve 5-DOF of the moving platform with only three branch chains. Afterwards, analytical solution for inverse kinematics is derived. The 5 $\times$ 5 homogeneous Jacobian matrix is obtained by transforming actuator velocities into linear velocities at three points on the moving platform. Meanwhile, the workspace, dexterity, and volume are analyzed based on the kinematic model. Ultimately, a stage-by-stage Pareto optimization method is proposed to solve the multi-objective optimization problem of this parallel mechanism. The optimization results show that the workspace, compactness, and dexterity of this mechanism can be improved efficiently.

show abstract

“…Parallel manipulators (PMs) have become a research hotspot because of their excellent dynamic performance, simple inverse kinematics, and high stiffness (Yang et al, 2022). Sprint (3PRS) (Chen et al, 2014), Tricept (3UPS-UP) (Farooq et al, 2021), and delta (Wang et al, 2014) robots have been successfully commercialized, and the 3RPS PM is used as a parallel module in the five-axis machining center for blade surface machining (Arabshahi and Novinzadeh, 2015). Comprehensive performance of PMs is crucial for machining precision.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization design of parallel manipulators using a neural network and principal component analysis

Yang,

Li,

Wang

et al. 2023

Mech. Sci.

View full text Add to dashboard Cite

Abstract. In this work, a multi-objective optimization design method is proposed based on principal component analysis (PCA) and a neural network to obtain a mechanism's optimal comprehensive performance. First, multi-objective optimization mathematical modeling, including design parameters, objective functions, and constraint functions, is established. Second, the sample data are obtained through the design of the experiment (DOE) and are then standardized to eliminate the adverse effects of a non-uniform dimension of objective functions. Third, the first k principal components are established for p performance indices (k<p) using the variance-based PCA method, and then the factor analysis method is employed to define its physical meaning. Fourth, the overall comprehensive performance evaluation index is established by objectively determining weight factors. Finally, the computational cost of the modeling is improved by combining the neural network and a particle swarm optimization (PSO) algorithm. Dimensional synthesis of a Sprint (3RPS) parallel manipulator (PM) is taken as a case study to implement the proposed method, and the optimization results are verified by a comprehensive performance comparison of robots before and after optimization.

show abstract

Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters

Cited by 13 publications

References 40 publications

Optimization of dynamic parameter design of Stewart platform with Particle Swarm Optimization (PSO) algorithm

Optimization of dynamic parameter design of Stewart platform with Particle Swarm Optimization (PSO) algorithm

Design and multi-objective optimization of a novel 5-DOF parallel mechanism with two double-driven chains

Multi-objective optimization design of parallel manipulators using a neural network and principal component analysis

Contact Info

Product

Resources

About