Applied Mechanics and Materials Vols. 110-116

Abstract: Thermoelectric coolers are often used as reliable energy converters in a large range of applications. For design considerations, it is crucial to establish an effective methodology to determine and optimize the TEC performance within the cooling system constraints. For this purpose, firstly, three approaches are used to obtain the internal parameters of a given thermoelectric module. For these three estimating procedures, the simulated pumping powers are in the sequence of method III < method II < method I for each temperature difference. Hence, good precision of the simulated data are obtained by averaging the results of these three methods. Then, design optimizations of a thermoelectric assembly are conducted to obtain the device parameter, efficiency, and maximum power, respectively. Results show it is simple and effective way for design of a thermoelectric cooling system.

Abstract: This paper presents a system modeling based control scheme of an ultra precision positioning system for a grating ruling machine. Since the positioning system having a long stroke with ultra precision, the positioning system consists of a coarse positioning stage driven by a servo motor and a fine positioning stage driven by a piezoelectric ceramic. In order to improve positional accuracy and remove the noise components of motion, a hybrid control scheme based on the system modeling is implemented. Considering position-dependent and time-dependent behaviors of the stages, a model based LQ controller is utilized to the coarse stage and a PID feedback controller based on neural network is utilized to the fine stage. Experiment results reveal the efficient and robust of the control scheme and show that the positional accuracy has been readily achieved within 8.6 nm.

Abstract: In this paper, a Bayesian criterion-based method called the Lv measure, as well as its calibration distribution, is introduced and applied to model optimization of structural equation models with mixed continuous and categorical data. A simulation study is presented to illustrate the satisfactory performance of the Lv measure in model optimization.

Abstract: Adaptive Response Surface Methodology (ARSM) is a new developed method sequentially estimates the optimum of a complex function in a gradually reduced design space. In this paper, previously developed approach in ARSM has been compare and contrast with a new approach proposed by authors: using inherited Latin Hypercube Design (LHD) points and generating new Maximin LHD points by solving facility location problems in each iteration. Minimum of the response surface which is a second order approximate model based on LHD points in each iteration is an estimation of the real optimum of the complex function. The computation results reveal that the new suggested approach demonstrate more precise estimation of minimum with less number of function calls compare to the ARSM with random LHDs which is an improvement in the efficiency and accuracy of ARSM.

Abstract: A stochastic approximation expectation maximum (EM) algorithm is proposed to obtain Maximum likelihood (ML) estimation of nonlinear random effect models in which the manifest variables are distributed as a reproductive dispersion model (RDM) and may be missing with ignorable missingness mechanism in this paper. A method composed of simulation step as well as stochastic approximation step is used to obtain the conditional expectation, whereas the M-step is executed via the method of conditional maximization. The most attractive point of this approach is that it is novel and non-trivial, which can be used to obtain the ML estimates and the estimation of standard errors simultaneously. Moreover, A model selection criterion is developed. Empirical results are used to illustrate the usefulness of the methodologies proposed above.

Abstract: Removed due to author request.

Abstract: Welding is a reliable and efficient joining process in which the coalescence of metals is achieved by fusion. Welding is widely employed in diverse structures such as ships, aircraft, marine structures, bridges, ground vehicles, pipelines and pressure vessels. When two dissimilar plates are joined by welding process, a very complex thermal cycle is applied to the weldment, which further causes inhomogeneous plastic deformation and residual stress in and around fusion zone and heat affected zone (HAZ). Presence of residual stresses may be beneficial or harmful for the structural components depending on the nature and magnitude of residual stresses. In this study, a finite element analysis has been carried out to analyze the thermo-mechanical behaviour and effect of residual stress state in butt-welded in low carbon steel plates. A coupled thermal mechanical three dimension finite element model was developed. Finite element method based software SolidWorks Simulation, was then used to evaluate transient temperature and residual stress during butt welding of two plates. Plate thickness of 8 mm were used which are normally joined by multi-pass operation by Manual Metal Arc Welding (MMAW) process. During each pass, attained peak temperature and variation of residual stresses in plates has also been studied. The results obtained by finite element method agree well with those from X-ray diffraction method as published by Murugan et al. for the prediction of residual stresses.

Abstract: Artificial Neural Network (ANN) model is used to predict the suspended sediment load for the survey data collected on daily basis in the river Mahanadi. Genetic algorithm has been used to find the optimal level of process parameters such as water discharge and temperature for a minimum sedimentation load condition. Optimal level of process parameters obtained from the GA has been used in a trained neural network to obtain the sedimentation load condition. A comparative analysis is then made between GA and ANN for achieving minimum sedimentation load with the given process parameters.

Abstract: Reverse logistics (RL), which refers to the distribution activities involved in product returns, has recently received much attention because many companies are using it as a strategic tool to serve their customers and can generate good revenue. An efficient reverse distribution structure may lead to a significant return on investment as well as a significantly increased competitiveness in the market. Therefore, analysis of the interaction among the major barriers, which hinder or prevent the application of reverse logistics, is a crucial issue. Existing models have focused on diagnosing these barriers independently. As a result, the holistic view in understanding the barriers is not accounted for. This paper utilizes the Interpretive Structural Modeling (ISM) methodology to understand the mutual influences among the barriers so that those driving barriers, which can aggravate few more barriers and those independent barriers, which are most influenced by driving barriers, are identified.

Abstract: The aim of this research is to investigate numerically the neck-spinning process of a tube at elevated temperature. The commercial software Abaqus/Explicit was adopted in the simulation. For the construction of the material model, special uni-axial tensile tests were conducted at elevated temperature and various strain rates, since the material is sensitive to strain rates at high temperature. The influence of the element type and mass scaling factor were investigated through numerical simulation. Full-integration shell element is the better choice in the simulation of the neck-spinning process at elevated temperature. The use of suitable mass scaling factor will make the analysis more efficiency. Comparisons between experimental and simulation results on thickness distribution and the outer contour of the spun tube are discussed. Good agreement was found between experimental and simulation results.