Simple Approximate Solutions for Dynamic Response of Suspension System

An approximate simplified analytic solution is proposed for the one DOF (degree of freedom) static and dynamic displacements alongside the stiffness (dynamic and static) and damping coefficients (minimum and maximum/critical values) of a parallel spring-damper suspension system connected to a solid mass-body gaining its energy by falling from height h. The analytic solution for the prescribed system is based on energy conservation equilibrium, considering the impact by a special G parameter. The formulation is based on the works performed by Timoshenko (1928), Mindlin (1945), and the U. S. army-engineering handbook (1975, 1982). A comparison between the prescribed studies formulations and current development has led to qualitative agreement. Moreover, quantitative agreement was found between the current prescribed suspension properties approximate value - results and the traditionally time dependent (transient, frequency) parameter properties. Also, coupling models that concerns the linkage between different work and energy terms, e.g., the damping energy, friction work, spring potential energy and gravitational energy model was performed. Moreover, approximate analytic solution was proposed for both cases (friction and coupling case), whereas the uncoupling and the coupling cases were found to agree qualitatively with the literature studies. Both coupling and uncoupling solutions were found to complete each other, explaining different literature attitudes and assumptions. In addition, some design points were clarified about the wire mounting isolators stiffness properties dependent on their physical behavior (compression, shear tension), based on Cavoflex catalog. Finally, the current study aims to continue and contribute the suspension, package cushioning and containers studies by using an initial simple pre – design analytic evaluation of falling mass- body (like cushion, containers, etc.).

From Visual Grading and Dynamic Modulus of European Beech (Fagus sylvatica) Logs to Tensile Strength of Boards

The aim of the present paper is to assess the non-destructive indicating properties of Slovenian beech (Fagus sylvatica) logs and correlate them with the mechanical properties of the final product, which is boards. Beech logs were visually graded according to the standard procedure and vibrational frequencies were measured. Logs were further on sawn into boards which were also non-destructively tested in wet and dry conditions. Finally, the boards were experimentally tested in tension. Special focus was directed towards visual parameters of the beech logs and their influence on the overall quality of the output material. The longitudinal natural frequencies of the logs were studied as potential indicating properties. The results showed that a majority of the visual log grading parameters do not result in good quality timber in terms of strength and stiffness properties, and only few are decisive for the final classification. The coefficient of determination of the static MOE vs. dynamic MOE of logs was r2=0.13, whereas vs. the MOE of wet boards was r2=0.49. Using a few visual characteristics in combination with dynamic measurements of logs and of wet boards could help to increase the yield of high quality beech wood.

Analysis of effect of variation of Honeycomb core cell size and sandwich panel width on the stiffness of a sandwich structure

A sandwich structure consists of three main parts i.e. the facing skins, the core and the adhesive. It acts in a way similar to that of the I- Beam. In this research, a sandwich structure has been designed with a regular hexagon honey-comb core made up of Kevlar® and face sheet of carbon fiber. The design has been modelled and the model has also been validated with the experimental and analytical method. Six different configurations of sandwich structures have been proposed. Out of these six, three configurations have the varying cell size i.e. 3.2 mm, 4 mm and 4.8 mm and the other three configurations have the varying panel width i.e. 40 mm, 45 mm and 50 mm keeping rest of the design parameters unchanged. Using ANSYS, analysis has been performed for all these six configurations and equivalent stiffness has been calculated. It has been observed that the honeycomb core cell size does not have a significant effect on the stiffness properties of a composite sandwich panel. The analysis also reveals that with the increased panel width the stiffness of composite panel increases significantly.

REACTIVE DYE PRINTING ON COTTON FABRIC USING MODIFIED STARCH OF WILD TARO CORMS AS A NEW THICKENING AGENT

This study investigated the use of a thickening agent derived from modified starch of wild taro corms in the screen printing of cotton fabric using reactive dye. The best conditions for developing the print paste and steaming time in order to obtain maximum color yield were established. The results revealed impressive color fastness properties in the printed samples; although, the printed fabric possessed slightly lesser tensile and tear strength, in comparison with the unprinted fabric. The printed fabric also exhibited increased bending stiffness properties. Largely, this study reveals that the printing paste containing the thickening agent derived from carboxymethyl starch within wild taro corms can be utilized in the printing of cotton fabric using reactive dye.

Investigation of the Effects of Environmental Fatigue on the Mechanical Properties of GFRP Composite Constituents Using Nanoindentation

Background Fatigue failure criteria for fibre reinforced polymer composites used in the design of marine structures are based on the micromechanical behaviour (e.g. stiffness properties) of their constituents. In the literature, there is a lack of information regarding the stiffness degradation of fibres, polymer matrix and fibre/matrix interface regions affected by environmental fatigue. Objective The aim of present study is to characterize the stiffness properties of composite constituents using the nanoindentation technique when fatigue failure of composites is due to the combined effect of sea water exposure and cyclic mechanical loads. Methods In the present study, the nanoindentation technique was used to characterize the stiffness properties of composite constituents where the effects of neighbouring phases, material pile up and viscoplasticity properties of the polymer matrix are corrected by finite element simulation. Results The use of finite element simulation in conjunction with nanoindentation test data, results in more accurate estimation of projected indented area which is required for measuring the properties of composite constituents. In addition, finite element simulation provides a greater understanding of the stress transfer between composite constituents during the nanoindentation process. Conclusions Results of nanoindentation testing on the composite microstructure of environmentally fatigue failed composite test coupons establish a strong link to the stiffness degradation of the fiber/matrix interface regions, verifying the degradation of composite constituents identified by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) analysis.

DEEP LEARNING FRAMEWORK FOR WOVEN COMPOSITE ANALYSIS

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG