scholarly journals Topology Optimization and Lightweight Design of Stamping Dies for Forming Automobile Panels

2021 ◽  
Author(s):  
Ting Su ◽  
Tao He ◽  
Renqi Yang ◽  
Maojun Li
Keyword(s):  
Topology Optimization ◽  
Stress Distribution ◽  
Lightweight Design ◽  
Structural Safety ◽  
Time Saving ◽  
Forming Simulation ◽  
Structure Topology ◽  
Stamping Die ◽  
Structure Reliability ◽  
Manufacturing Techniques

Abstract The accurate prediction of deformation and stress distribution on the stamping die components is critical to guarantee structure reliability and lightweight design. This work aims to propose a new method based on numerical simulation for predicting die structural behaviors and reducing total weight. The sheet metal forming simulation was firstly conducted to obtain the accurate forming contact force during stamping process. The linear static structural analysis with different load cases was then performed to investigate the deformation and stress distribution on die structure. Topology optimization was employed to realize lightweight design while ensuring structural safety. Redesign process for die structures was conducted according to both manufacturing techniques and initial optimized results to guarantee the manufacturability of new structures. The proposed methodology has several advantages of decreasing model scale, precluding intricate contact condition settings as well as time-saving. A long beam stamping die used for forming automobile panels was selected to validate the proposed methodology, and around 18% weight reduction was achieved.

scholarly journals An efficient approach to reliability-based topology optimization for the structural lightweight design of planar continuum structures

2021 ◽  
Vol 37 ◽  
pp. 270-281
Author(s):  
Fangfang Yin ◽  
Kaifang Dang ◽  
Weimin Yang ◽  
Yumei Ding ◽  
Pengcheng Xie
Keyword(s):  
Topology Optimization ◽  
Lightweight Design ◽  
Integrated Design ◽  
Optimization Methods ◽  
Filter Function ◽  
Continuum Structures ◽  
Performance Indexes ◽  
Reliability Method ◽  
Economic Indexes ◽  
The Impact

Abstract In order to solve the application restrictions of deterministic-based topology optimization methods arising from the omission of uncertainty factors in practice, and to realize the calculation cost control of reliability-based topology optimization. In consideration of the current reliability-based topology optimization methods of continuum structures mainly based on performance indexes model with a power filter function. An efficient probabilistic reliability-based topology optimization model that regards mass and displacement as an objective function and constraint is established based on the first-order reliability method and a modified economic indexes model with a composite exponential filter function in this study. The topology optimization results obtained by different models are discussed in relation to optimal structure and convergence efficiency. Through numerical examples, it can be seen that the optimal layouts obtained by reliability-based models have an increased amount of material and more support structures, which reveals the necessity of considering uncertainty in lightweight design. In addition, the reliability-based modified model not only can obtain lighter optimal structures compared with traditional economic indexes models in most circumstances, but also has a significant advantage in convergence efficiency, with an average increase of 44.59% and 64.76% compared with the other two reliability-based models. Furthermore, the impact of the reliability index on the results is explored, which verifies the validity of the established model. This study provides a theoretical reference for lightweight or innovative feature-integrated design in engineering applications.

Topology Optimization of Blank Geometry for the Sheet Forming Process

2012 ◽  
Author(s):  
Saber DorMohammadi ◽  
Mohammad Rouhi ◽  
Masoud Rais-Rohani
Keyword(s):  
Topology Optimization ◽  
Volume Fraction ◽  
Structural Response ◽  
Sheet Forming ◽  
Forming Process ◽  
Exchange Method ◽  
Forming Simulation ◽  
Blank Shape Optimization ◽  
Blank Shape ◽  
Consistent Subset

The newly developed element exchange method (EEM) for topology optimization is applied to the problem of blank shape optimization for the sheet-forming process. EEM uses a series of stochastic operations guided by the structural response of the model to switch solid and void elements in a given domain to minimize the objective function while maintaining the specified volume fraction. In application of EEM to blank optimization, a sheet forming simulation model is developed using Abaqus/Explicit. With the goal of minimizing the variability in wall thickness of the formed component, a subset of solid (i.e., high density) elements with the highest increase in thickness is exchanged with a consistent subset of void (i.e., low density) elements having the highest decrease in thickness so that the volume fraction remains constant. The EEM operations coupled with finite element simulations are repeated until the optimum blank geometry (i.e., boundary and initial thickness) is found. The developed numerical framework is applied to blank optimization of a benchmark problem. The results show that EEM is successful in generating the optimum blank geometry efficiently and accurately.

Thermo-Structural Coupled Topology Optimization of Micro-Capacitive Accelerometer

2012 ◽  
Vol 433-440 ◽  
pp. 3080-3085 ◽  
Author(s):  
Huan Yuan Chen ◽  
Yong Jun Xie ◽  
Dong Song Yan ◽  
Hao Liu ◽  
Jing Ming Li
Keyword(s):  
Topology Optimization ◽  
Elastic Beam ◽  
Optimal Topology ◽  
Beam Structure ◽  
Innovative Design ◽  
Maximum Deformation ◽  
Structure Topology ◽  
Capacitive Accelerometer ◽  
Limit Value ◽  
Moving Asymptotes

In order to enhance the working performance of micro-capacitive accelerometer in high temperature environment, the structure topology optimization of a micro-capacitive accelerometer is proposed. After the study of thermo-structural coupled governing equations and sensitivity analysis, the mass-block and elastic-beam structure of comb micro-capacitive accelerometer topology optimization model is established. Then the optimal topology forms of mass-block and elastic-beam structure are obtained with the MMA (method of moving asymptotes) method. At last, the calculating results indicate that the maximum deformation at acceleration detection direction is only 22nm at the operating temperature range of 0~300°C, which less than the maximum deformation of the limit value (25nm), and provides a reliable way for innovative design of micro-capacitive accelerometer.

scholarly journals EXPERIMENTAL INVESTIGATION AND SIMULATION OF 3D-PRINTED LATTICE STRUCTURES

2019 ◽  
Vol 25 ◽  
pp. 52-57
Author(s):  
Eva Heiml ◽  
Anna Kalteis ◽  
Zoltan Major
Keyword(s):  
Mechanical Performance ◽  
Bulk Material ◽  
Deformation Behaviour ◽  
Lightweight Design ◽  
Thermoplastic Polyurethane ◽  
Selective Laser Sintering ◽  
Structural Performance ◽  
Lattice Structures ◽  
3D Printed ◽  
Manufacturing Techniques

Lattice structures are currently of high interest, especially for lightweight design. They generally have better structural performance per weight than parts made of bulk material. With conventional manufacturing techniques they are difficult to produce, but with additive manufacturing (AM) fabricationisfeasible. To better understand their behaviour under various loading conditions two lattice structures in different configurations were observed. For each structure three different test specimens were designed and manufactured using selective laser sintering (SLS). To investigate the mechanical performance under large deformations the specimens were made of a thermoplastic polyurethane(TPU), which shows a hyperelastic material behaviour. Beside the experimental observations also finite element analyses (FEA) were conducted to investigate the deformation behaviour in more detail.

scholarly journals Deep Neural Network and Monte Carlo Tree Search applied to Fluid-Structure Topology Optimization

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Audrey Gaymann ◽  
Francesco Montomoli
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Topology Optimization ◽  
Deep Neural Network ◽  
Level Set Methods ◽  
Test Case ◽  
Tree Search ◽  
Fluid Structure ◽  
Monte Carlo Tree Search ◽  
Structure Topology

Abstract This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods. The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver. The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code.

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