Design and finite element analysis of lightmass silicon carbide primary mirror

Han, Yuanyuan; Han, Jiecai; Zhang, Jian‐Han; Zhou, Yanjun

doi:10.1016/s1003-6326(06)60281-1

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…Since having no gaseous TiAl, the TiAl-based alloys must be decomposed into Ti vapor and Al vapor and then condense on the substrate surface when evaporation. Siegfried et al [5] indicated that the substrate temperature can determine the sticking coefficient of vapor atoms on the substrate surface and affect their deposition rate, so the few difference of compositional variation of interlamination should be attributed to the little temperature fluctuation on the substrate surface. XRD pattern shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Synthesis and Mechanical Property Evaluation of Ti-TiAl Metal-Intermetallic Microlaminate Composite

Wang

2010

AMR

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A microlaminate consisting of Ti and α2-Ti3Al+γ-TiAl has been synthesized using electron beam physical vapor deposition technique. The microstructure and various properties have been explored. It is demonstrated that the nano-hardness and elastic modulus of Ti layer and Ti-Al layer are of gradient distribution according to the distance to the interface layer which may be results from the pile-up effect and the stress concentration at interface. The presence of Ti layers will lead to the cracks stagger along the inter-laminar interface or the layer due to which micro laminate expresses a good characteristic of delayed fracture. The toughening mechanisms are that the crack deflection and micro-bridge connection caused by the toughening layers increases crack propagation resistance.

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Section: Resultsmentioning

confidence: 99%

Synthesis and Mechanical Property Evaluation of Ti-TiAl Metal-Intermetallic Microlaminate Composite

Wang

2010

AMR

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“…As the most promising material for space-based lightweight optical mirror, silicon carbide has become a favorite topic for analysis due to its outstanding properties, such as excellent specific stiffness, low density and thermal expansion coefficient, high elastic modulus and thermal stability, etc. [ 9 ]. Despite empirical research focused on the preparation process and the corresponding optical properties, detailed information on the forming process has not been paid enough attention.…”

Section: Introductionmentioning

confidence: 99%

A Finite Element Analysis for Improvement of Shaping Process of Complex-Shaped Large-Size Silicon Carbide Mirrors

et al. 2021

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In the gel-casting process, the proper selection of technological parameters is crucial for the final quality of a green body. In this work, the finite element method is used to investigate the mold characteristics in the gel-casting process, and the typical flow behaviors under different conditions are presented. Based on the distribution characteristics of temperature, pressure and flow field of gel polymer, the simulated results provide some possible reasons for the generation mechanisms of defects. Then, a series of simulations were performed to investigate the effect of process parameters on the molding quality of green gel-cast bodies. The results show that the decreasing loading speed can effectively reduce the number of defects and improve the molding quality. In addition, this paper presents a new technique by applying the exhaust hole to decrease the number of defects and, hence, improve structural integrity. The influence of the loading speed on the mold characteristics is well understood for the gating system with an exhaust hole, which suggests to us appropriate parameters for optimizing the molding design. This work provides a theoretical basis to explicate the generating mechanism of defects involved in the gel-casting process and acquires an optimized technique to produce a silicon carbide green body.

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“…With the continuous development of computer technology, finite element analysis (FEA) technology has been rapidly developed and widely used in many fields, including mechanical optimization and design [ 7 , 8 , 9 , 10 , 11 ]. In the optimized design of complex mechanical structures for finite element analysis, it is necessary to discretize the mechanical continuous structure into many units and a large number of calculations.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Zhang

Sun

2021

Materials

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Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

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Design and finite element analysis of lightmass silicon carbide primary mirror

Cited by 9 publications

References 9 publications

Synthesis and Mechanical Property Evaluation of Ti-TiAl Metal-Intermetallic Microlaminate Composite

Synthesis and Mechanical Property Evaluation of Ti-TiAl Metal-Intermetallic Microlaminate Composite

A Finite Element Analysis for Improvement of Shaping Process of Complex-Shaped Large-Size Silicon Carbide Mirrors

Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

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