Extraction of mechanical properties of foot plantar tissues using ultrasound indentation associated with genetic algorithm

2007 ◽  
Vol 18 (8) ◽  
pp. 1579-1586 ◽  
Author(s):  
Hang-yin Ling ◽  
Pong-chi Choi ◽  
Yong-ping Zheng ◽  
Kin-tak Lau
Keyword(s):  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Ultrasound Indentation

Study on the Mechanical Properties of Tissue-Mimicking Phantom Composites Using Ultrasound Indentation

2007 ◽  
Vol 334-335 ◽  
pp. 133-136
Author(s):  
Hang Yin Ling ◽  
P. Carrie Choi ◽  
Y.P. Zheng ◽  
Alan Kin Tak Lau
Keyword(s):  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Soft Tissue ◽  
Soft Tissues ◽  
Load Cell ◽  
Indentation Technique ◽  
Deformation Curves ◽  
Ultrasound Indentation ◽  
Indentation Process

This paper demonstrates the use of ultrasound (US) indentation technique for estimating the mechanical properties of tissue- mimicking phantom composites. A tissue-mimicking phantom composite is used to simulate two-layer soft tissue in human. Investigation on the mechanical properties of the phantom composites is extremely important for the understanding of the viscoelastic behaviours of soft tissues and the validation of our proposed US indentation system. The hand-held indentation probe embedded with a US transducer and a load cell together with a US pulser/ receiver. The output of the whole indentation process can be illustrated as force-deformation curves. The mechanical properties of the phantom composites can be estimated by analyzing the force-deformation curves using genetic algorithm (GA).

scholarly journals Structure optimization of woven fabric composites for improvement of mechanical properties using a micromechanics model of woven fabric composites and a genetic algorithm

2021 ◽  
Vol 30 ◽  
pp. 263498332110061
Author(s):  
Gunyong Hwang ◽  
Dong Hyun Kim ◽  
Myungsoo Kim
Keyword(s):  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Elastic Modulus ◽  
Fiber Composite ◽  
Woven Fabric ◽  
Cross Sectional ◽  
Micromechanics Model ◽  
Woven Fabric Composites ◽  
Fabric Composites ◽  
Out Of Plane

This research aims to optimize the mechanical properties of woven fabric composites, especially the elastic modulus. A micromechanics model of woven fabric composites was used to obtain the mechanical properties of the fiber composite, and a genetic algorithm (GA) was employed for the optimization tool. The structure of the fabric fiber was expressed using the width, thickness, and wave pattern of the fiber strands in the woven fabric composites. In the GA, the chromosome string consisted of the thickness and width of the fill and warp strands, and the objective function was determined to maximize the elastic modulus of the composite. Numerical analysis showed that the longitudinal mechanical properties of the strands contributed significantly to the overall elastic modulus of the composites because the longitudinal property was notably larger than the transverse property. Therefore, to improve the in-plane elastic modulus, the resulting geometry of the composites possessed large volumes of related strands with large cross-sectional areas and small strand waviness. However, the numerical results of the out-of-plane elastic modulus generated large strand waviness, which contributed to the fiber alignment in the out-of-plane direction. The findings of this research are expected to be an excellent resource for the structural design of woven fabric composites.

scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

scholarly journals Modeling and Composition Design of Low-Alloy Steel’s Mechanical Properties Based on Neural Networks and Genetic Algorithms

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5316
Author(s):  
Zhenlong Zhu ◽  
Yilong Liang ◽  
Jianghe Zou
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Genetic Algorithm ◽  
Alloy Steel ◽  
Deep Neural Network ◽  
Treatment Process ◽  
Mechanical Performance ◽  
Low Alloy Steel ◽  
Forward Selection

Accurately improving the mechanical properties of low-alloy steel by changing the alloying elements and heat treatment processes is of interest. There is a mutual relationship between the mechanical properties and process components, and the mechanism for this relationship is complicated. The forward selection-deep neural network and genetic algorithm (FS-DNN&GA) composition design model constructed in this paper is a combination of a neural network and genetic algorithm, where the model trained by the neural network is transferred to the genetic algorithm. The FS-DNN&GA model is trained with the American Society of Metals (ASM) Alloy Center Database to design the composition and heat treatment process of alloy steel. First, with the forward selection (FS) method, influencing factors—C, Si, Mn, Cr, quenching temperature, and tempering temperature—are screened and recombined to be the input of different mechanical performance prediction models. Second, the forward selection-deep neural network (FS-DNN) mechanical prediction model is constructed to analyze the FS-DNN model through experimental data to best predict the mechanical performance. Finally, the FS-DNN trained model is brought into the genetic algorithm to construct the FS-DNN&GA model, and the FS-DNN&GA model outputs the corresponding chemical composition and process when the mechanical performance increases or decreases. The experimental results show that the FS-DNN model has high accuracy in predicting the mechanical properties of 50 furnaces of low-alloy steel. The tensile strength mean absolute error (MAE) is 11.7 MPa, and the yield strength MAE is 13.46 MPa. According to the chemical composition and heat treatment process designed by the FS-DNN&GA model, five furnaces of Alloy1–Alloy5 low-alloy steel were smelted, and tensile tests were performed on these five low-alloy steels. The results show that the mechanical properties of the designed alloy steel are completely within the design range, providing useful guidance for the future development of new alloy steel.

Compositional Optimum Design of the Ceramic Composite with Immune Genetic Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 1562-1567 ◽  
Author(s):  
Hui Fa Zhang ◽  
Chong Hai Xu ◽  
Xing Hai Wang ◽  
Jing Jie Zhang
Keyword(s):  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Fracture Toughness ◽  
Composite Material ◽  
Flexural Strength ◽  
Optimum Design ◽  
Ceramic Composite ◽  
Immune Algorithm ◽  
Immune Genetic Algorithm ◽  
Optimum Material

In this paper, the immune genetic algorithm (IGA) is used to optimize the compositions of the Al2O3/SiC/Ti(C,N) ceramic composite material. Corresponding to the optimal mechanical properties such as fracture toughness, hardness and flexural strength, the optimum material compositions have been achieved. The convergent speed of IGA is faster than that of the single immune algorithm, and the number of iteration is also reduced obviously. As a result, the efficiency of optimization is increased.

Optimization of hanger arrangement in pedestrian tied arch bridge with sparse hanger system

2019 ◽  
Vol 22 (12) ◽  
pp. 2594-2604 ◽  
Author(s):  
Yonggang Tan ◽  
Yuanbin Yao
Keyword(s):  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Numerical Analysis ◽  
Mechanical Behavior ◽  
Live Load ◽  
Optimization Approach ◽  
Dense Network ◽  
Arch Bridge ◽  
Arch Bridges ◽  
Tied Arch Bridge

The hanger arrangement has a decisive influence on the mechanical behavior of the tied arch bridge with network hanger system. Many investigations on highway or railway tied arch bridges show that the arch bridges with dense network hangers are superior to those with vertical hangers under larger live load. However, numerous dense inclined hangers lower the esthetic effect of the bridge, especially for pedestrian tied arch bridges. Consequently, the sparse inclined hanger system is recommended in the design of pedestrian tied arch bridges. However, the amount of possible schemes of the hanger arrangement grows rapidly with the number of hangers increasing beyond 10, rendering great difficulties in searching for proper schemes. In this article, a dimensionless optimization approach based on genetic algorithm is proposed in searching for hanger arrangement schemes. Numerical analysis indicates that the proposed method is effective in the optimization of pedestrian tied arch bridge with sparse inclined hanger system, and some of the feasible hanger arrangement solutions show more excellent mechanical properties.

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