Stiffness-Oriented Structure Topology Optimization for Hinge-Free Compliant Mechanisms Design

This paper presents a stiffness-oriented structure topology optimization (TO) method for the design of a continuous, hinge-free compliant mechanism (CM). A synthesis formulation is developed to maximize the mechanism’s mutual potential energy (MPE) to achieve required structure flexibility while maximizing the desired stiffness to withstand the loads. Different from the general approach of maximizing the overall stiffness of the structure, the proposed approach can contribute to guiding the optimization process focus on the desired stiffness in a specified direction by weighting the related eigen-frequency of the corresponding eigenmode. The benefit from this is that we can make full use of the material in micro-level compliant mechanism designs. The single-node connected hinge issue which often happened in optimized design can be precluded by introducing the eigen-frequency constraint into this synthesis formulation. Several obtained hinge-free designs illustrate the validity and robustness of the presented method and offer an alternative method for hinge-free compliant mechanism designs.

A novel lattice structure topology optimization method with extreme anisotropic lattice properties

This research presents a lattice structure topology optimization (LSTO) method that significantly expands the design space by creating a novel candidate lattice that assesses an extremely large range of effective material properties. About the details, topology optimization is employed to design lattices with extreme directional tensile or shear properties subject to different volume fraction limits and the optimized lattices are categorized into groups according to their dominating properties. The novel candidate lattice is developed by combining the optimized elementary lattices, by picking up one from each group, and then parametrized with the elementary lattice relative densities. In this way, the LSTO design space is greatly expanded for the ever increased accessible material property range. Moreover, the effective material constitutive model of the candidate lattice subject to different elementary lattice combinations is pre-established so as to eliminate the tedious in-process repetitive homogenization. Finally, a few numerical examples and experiments are explored to validate the effectiveness of the proposed method. The superiority of the proposed method is proved through comparing with a few existing LSTO methods. The options of concurrent structural topology and lattice optimization are also explored for further enhancement of the mechanical performance.

DEEP LEARNING FOR TOPOLOGY OPTIMIZATION OF TRIPLY PERIODIC MINIMAL SURFACE BASED GYROID-LIKE STRUCTURES

Triply periodic minimal surfaces (TPMS) are non-intersecting complex geometrical surfaces that can be used in unit cell design of cellular structures. TPMS possess attractive properties like large surface area to volume ratio and mathematically controlled geometry which find them applications in catalytic converters, cocontinuous composites, thermal and permeability management, to name a few. The advent of additive manufacturing eased the manufacture of these structures which were previously challenging with traditional methods of manufacturing. Design of TPMS unit-cell based materials involves topology optimization to achieve the desired physical properties depending on the specific application of the structure. Topology optimization, in turn, involves the objective function evaluations for each iteration till converging to an optimal design and this may pose a computational burden when the function evaluations are time consuming finite element or computational fluid simulations. This can be alleviated by employing machine learning based methods for the optimization process. Deep learning using convolutional neural networks (CNN) have effectively been used for prediction of optimal topologies required for desired properties thus eliminating any objective function evaluations. In this paper, we explore the use of 3D CNN models for topology optimization of a TPMS based unit cell. The Solid Isotropic Material Penalization density method in topology optimization is employed on energy based homogenized unit cell properties. The unit cell that is obtained satisfying a desired mechanical property along with their topology parameters is then learnt to build a CNN model which can then be used to predict the optimal unit cell design for any topology parameters. The class of TPMS used in this work is Gyroids. The CNN model is tested for errors in prediction using mean square error metric and dice coefficient of the 2D slices of unit cell. The results indicate that the model can predict the ground truth accurately with few data points. This showed a promising approach in the area of TPMS based unit cell design using CNN.

The evolutionary history of topological variations in the CPA/AT transporters

CPA/AT transporters are made up of scaffold and a core domain. The core domain contains two non-canonical helices (broken or reentrant) that mediate the transport of ions, amino acids or other charged compounds. During evolution, these transporters have undergone substantial changes in structure, topology and function. To shed light on these structural transitions, we create models for all families using an integrated topology annotation method. We find that the CPA/AT transporters can be classified into four fold-types based on their structure; (1) the CPA-broken fold-type, (2) the CPA-reentrant fold-type, (3) the BART fold-type, and (4) a previously not described fold-type, the Reentrant-Helix-Reentrant fold-type. Several topological transitions are identified, including the transition between a broken and reentrant helix, one transition between a loop and a reentrant helix, complete changes of orientation, and changes in the number of scaffold helices. These transitions are mainly caused by gene duplication and shuffling events. Structural models, topology information and other details are presented in a searchable database, CPAfold (cpafold.bioinfo.se).

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

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.

From Structure Topology to Chemical Composition. XXIX. Revision of the Crystal Structure of Perraultite, NaBaMn4Ti2(Si2O7)2O2(OH)2F, a Seidozerite-Supergroup TS-Block Mineral from the Oktyabr'skii Massif, Ukraine, and Discreditation of Surkhobite

ABSTRACT The crystal structure of perraultite from the Oktyabr'skii massif, Donetsk region, Ukraine (bafertisite group, seidozerite supergroup), ideally NaBaMn4Ti2(Si2O7)2O2(OH)2F, Z = 4, was refined in space group C to R1 = 2.08% on the basis of 4839 unique reflections [Fo > 4σFo]; a = 10.741(6), b = 13.841(8), c = 11.079(6) Å, α = 108.174(6), β = 99.186(6), γ = 89.99(1)°, V = 1542.7(2.7) Å3. Refinement was done using data from a crystal with three twin domains which was part of a grain used for electron probe microanalysis. In the perraultite structure [structure type B1(BG), B – basic, BG – bafertisite group], there is one type of TS (Titanium-Silicate) block and one type of I (Intermediate) block; they alternate along c. The TS block consists of HOH sheets (H – heteropolyhedral, O – octahedral). In the O sheet, the ideal composition of the five [6]MO sites is Mn4 apfu. There is no order of Mn and Fe2+ in the O sheet. The MH octahedra and Si2O7 groups constitute the H sheet. The ideal composition of the two [6]MH sites is Ti2 apfu. The TS blocks link via common vertices of MH octahedra. The I block contains AP(1,2) and BP(1,2) cation sites. The AP(1) site is occupied by Ba and the AP(2) site by K > Ba; the ideal composition of the AP(1,2) sites is Ba apfu. The BP(1) and BP(2) sites are each occupied by Na > Ca; the ideal composition of the BP(1,2) sites is Na apfu. We compare perraultite and surkhobite based on the work of Sokolova et al. (2020) on the holotype sample of surkhobite: space group C , R1 = 2.85 %, a = 10.728(6), b = 13.845(8), c = 11.072(6) Å, α = 108.185(6), β = 99.219(5), γ = 90.001(8)°, V = 1540.0(2.5) Å3; new EPMA data. We show that (1) perraultite and surkhobite have identical chemical composition and ideal formula NaBaMn4Ti2(Si2O7)2O2(OH)2F; (2) perraultite and surkhobite are isostructural, with no order of Na and Ca at the BP(1,2) sites. Perraultite was described in 1991 and has precedence over surkhobite, which was redefined as “a Ca-ordered analogue of perraultite” in 2008. Surkhobite is not a valid mineral species and its discreditation was approved by CNMNC IMA (IMA 20-A).