Hang Yang^1,2,3, Wei Zhai³, Ma Li^1,*, Damiano Pasini^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.010893

Abstract Stimuli-responsive materials can alter their physicochemical properties, e.g., shape, color, or stiffness, upon exposure to an external trigger, e.g., heat, light, or humidity, exhibiting environmental adaptability. Among them, shape memory materials are limited by their multi-shape memory effect and the complex thermomechanical programming. In this work, we harness the distinct temperature-dependent elastic moduli of two 3D-printable polymers, that do not rely upon their intrinsic shape memory effect and compositional alteration to generate robust and simplified multi-shape memory responses in a variety of stimuli-responsive mechanical metamaterials, bypassing the typical intricate programming of conventional multi-shape memory polymers.… More >

Jiazhao Huang¹, Xiaoying Qi¹, Chu Long Tham¹, Hang Li Seet¹, Sharon Mui Ling Nai¹, David William Rosen^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.012040

Abstract The emergence of 4D printing introduces stimuli-responsive, shape-changing capabilities through additive manufacturing (AM) and smart materials, has advanced the field of soft robotics. However, there are currently lack of methods or tools that capable of aiding in the generative design of 4D AM structures. The current generative design procedure for 4D AM structures often lacks transferability among various structures due to limited understanding of shape memory material behaviors for soft robotics. To develop such a digital library, investigation of fundamental elements, such as material properties of shape memory materials, geometry parameters of design primitives, and… More >

Hsin-Yu Chen, Nien-Ti Tsou^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.3, pp. 55-55, 2019, DOI:10.32604/icces.2019.05403

Abstract Shape memory alloys has been widely applied on actuators and medical devices. The transformation temperature and microstructural evolution play the crucial factors and dominate the behavior of shape memory alloys. In order to understand the influence of the composition of the Ni-Ti on the two factors, molecular dynamics (MD) is adopted to simulate the temperature-induced phase transformation in the current study. In addition, the results are post-processed by the martensite variant identification method. The method allows to reveal the detailed microstructural evolution and the volume fraction of each variant/phase in each case of the composition More >

CMC-Computers, Materials & Continua, Vol.10, No.1, pp. 65-96, 2009, DOI:10.3970/cmc.2009.010.065

Abstract This work proposed an alternative formulation for the prediction of martensite start temperature (Ms) of Fe-Mn-Si shape memory alloys (SMAs) depending on the various compositions and heat treatment techniques by using Neural Network (NN) and genetic programming (GP) soft computing techniques. The training and testing patterns of the proposed NN and GP formulations are based on well established experimental results from the literature. The NN and GP based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R2=0.955 for GEP and 0.999 for NN). More >