A Topology Optimization Approach and its Application in the Design of a Key Connecting Component of a Harvester

2011 ◽  
Vol 308-310 ◽  
pp. 2471-2477
Author(s):  
Xiang Chen ◽  
Xin Jun Liu
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
Engineering Application ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Penalization Method ◽  
Weight Method ◽  
Derivation Process

This paper focuses on an approach to topology optimization and its engineering application. Based on SIMP (Solid Isotropic Microstructure with Penalization) method combined with Guide-Weight method, an approach to solve topology optimization problems is proposed. Then the topology optimization is applied in the design of a key connecting component in a sorghum harvester by the use of proposed method. The derivation process of the iteration formulations demonstrates that the proposed approach has the advantages of easiness to derive and good universality. The result is satisfactory and the convergence speed is fast enough for engineering application.

B-Spline Based Robust Topology Optimization

2015 ◽  
Author(s):  
Yu Gu ◽  
Xiaoping Qian
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
Compliant Mechanism ◽  
Analytical Description ◽  
Minimal Length ◽  
Optimization Approach ◽  
Material Density ◽  
B Spline ◽  
B Splines ◽  
Robust Formulation

In this paper, we present an extension of the B-spline based density representation to a robust formulation of topology optimization. In our B-spline based topology optimization approach, we use separate representations for material density distribution and analysis. B-splines are used as a representation of density and the usual finite elements are used for analysis. The density undergoes a Heaviside projection to reduce the grayness in the optimized structures. To ensure minimal length control so the resulting designs are robust with respect to manufacturing imprecision, we adopt a three-structure formulation during the optimization. That is, dilated, intermediate and eroded designs are used in the optimization formulation. We give an analytical description of minimal length of features in optimized designs. Numerical examples have been implemented on three common topology optimization problems: minimal compliance, heat conduction and compliant mechanism. They demonstrate that the proposed approach is effective in generating designs with crisp black/white transition and is accurate in minimal length control.

scholarly journals Isogeometric topology optimization based on energy penalization for symmetric structure

2020 ◽  
Vol 15 (1) ◽  
pp. 100-122 ◽  
Author(s):  
Xianda Xie ◽  
Shuting Wang ◽  
Ming Ye ◽  
Zhaohui Xia ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
Asymmetric Structure ◽  
Numerical Instability ◽  
Penalization Method ◽  
Symmetric Structure ◽  
Symmetric Constraint ◽  
Objective Value ◽  
Symmetric Structures ◽  
Moving Morphable Components

AbstractWe present an energy penalization method for isogeometric topology optimization using moving morphable components (ITO-MMC), propose an ITO-MMC with an additional bilateral or periodic symmetric constraint for symmetric structures, and then extend the proposed energy penalization method to an ITO-MMC with a symmetric constraint. The energy penalization method can solve the problems of numerical instability and convergence for the ITO-MMC and the ITO-MMC subjected to the structural symmetric constraint with asymmetric loads. Topology optimization problems of asymmetric, bilateral symmetric, and periodic symmetric structures are discussed to validate the effectiveness of the proposed energy penalization approach. Compared with the conventional ITO-MMC, the energy penalization method for the ITO-MMC can improve the convergence rate from 18.6% to 44.5% for the optimization of the asymmetric structure. For the ITO-MMC under a bilateral symmetric constraint, the proposed method can reduce the objective value by 5.6% and obtain a final optimized topology that has a clear boundary with decreased iterations. For the ITO-MMC under a periodic symmetric constraint, the proposed energy penalization method can dramatically reduce the number of iterations and obtain a speedup of more than 2.

Machine Learning to Aid Tuning of Numerical Parameters in Topology Optimization

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Matthew E. Lynch ◽  
Soumalya Sarkar ◽  
Kurt Maute
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Optimization Problems ◽  
Bayesian Optimization ◽  
Computational Time ◽  
Optimization Approach ◽  
Learning Approaches ◽  
Solution Quality ◽  
Topology Optimization Problem ◽  
Tuning Parameters

Abstract Recent advances in design optimization have significant potential to improve the function of mechanical components and systems. Coupled with additive manufacturing, topology optimization is one category of numerical methods used to produce algorithmically generated optimized designs making a difference in the mechanical design of hardware currently being introduced to the market. Unfortunately, many of these algorithms require extensive manual setup and control, particularly of tuning parameters that control algorithmic function and convergence. This paper introduces a framework based on machine learning approaches to recommend tuning parameters to a user in order to avoid costly trial and error involved in manual tuning. The algorithm reads tuning parameters from a repository of prior, similar problems adjudged using a dissimilarity metric based on problem metadata and refines them for the current problem using a Bayesian optimization approach. The approach is demonstrated for a simple topology optimization problem with the objective of achieving good topology optimization solution quality and then with the additional objective of finding an optimal “trade” between solution quality and required computational time. The goal is to reduce the total number of “wasted” tuning runs that would be required for purely manual tuning. With more development, the framework may ultimately be useful on an enterprise level for analysis and optimization problems—topology optimization is one example but the framework is also applicable to other optimization problems such as shape and sizing and in high-fidelity physics-based analysis models—and enable these types of advanced approaches to be used more efficiently.

Improved proportional topology optimization algorithm for minimum volume problem with stress constraints

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenming Cheng ◽  
Hui Wang ◽  
Min Zhang ◽  
Run Du
Keyword(s):  
Topology Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Weighting Function ◽  
Target Material ◽  
Stress Constraints ◽  
Convergence Speed ◽  
Minimum Volume ◽  
Content Type ◽  
Density Filter

Purpose The purpose of this paper is to propose an improved proportional topology optimization (IPTO) algorithm for tackling the stress-constrained minimum volume optimization problem, which can meet the requirements that are to get rid of the problems of numerical derivation and sensitivity calculation involved in the process of obtaining sensitivity information and overcome the drawbacks of the original proportional topology optimization (PTO) algorithm. Design/methodology/approach The IPTO algorithm is designed by using the new target material volume update scheme and the new density variable update scheme and by introducing the improved density filter (considering the weighting function based on the Gaussian distribution) and Heaviside-type projection operator on the basis of the PTO algorithm. The effectiveness of the IPTO algorithm is demonstrated by solving the stress-constrained minimum volume optimization problems for two numerical examples and being compared with the PTO algorithm. Findings The results of this paper show that the uses of the proposed strategies contribute to improving the optimized results and the performance (such as the ability to obtain accurate solutions, robustness and convergence speed) of the IPTO algorithm. Compared with the PTO algorithm, the IPTO algorithm has the advantages of fast convergence speed, enhancing the ability to obtain accurate solutions and improving the optimized results. Originality/value This paper achieved the author’s intended purpose and provided a new idea for solving the stress-constrained optimization problem under the premise of avoiding obtaining sensitivity information.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

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