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Accurate prediction of residual stress requires precise knowledge of the constitutive behavior of as-quenched material. This study aims to model the flow stress behavior for as-quenched Al-Mg-Si alloy.

In the present work, the flow behavior of as-quenched Al-Mg-Si alloy is studied by the hot compression tests at various temperatures (573–723 K), strain rates (0.1–1 s⁻¹) and cooling rates (1–10 K/s). Flow stress behavior is then experimentally observed, and an Arrhenius model is used to predict the flow behavior. However, due to the fact that materials parameters and activation energy do not remain constant, the Arrhenius model has an unsatisfied prediction for the flow behavior. Considering the effects of temperatures, strain rates and cooling rates on constitutive behavior, a revised Arrhenius model is developed to describe the flow stress behavior.

The experimental results show that the flow stress increases by the increasing cooling rate, increasing strain state and decreasing temperature. In comparison to the experimental data, the revised Arrhenius model has an excellent prediction for as-quenched Al-Mg-Si alloy.

With the revised Arrhenius model, the flow behaviors at different quenching conditions can be obtained, which is an essential step to the residual stress prediction when the model is implemented in a finite element code, e.g. ABAQUS, in the future.

With the discussion on the linear relationship of determined material parameters, this study aims to propose a new method to analyze the deformation mechanism.

A modified constitutive model based on the hyperbolic sine Arrhenius equation has been established, which is applied to describe the flow behavior of Ti-6Al-4V alloy during the superplastic forming (SPF).

The modified constitutive model in this work has a good ability to describe the flow behavior for Ti-6Al-4V in SPF. Besides, a deformation map of titanium material is obtained based on the parameters. As the supplement, finite element models of high-temperature tensile tests are carried out as the application of the constitutive model.

The relationship between constitutive model parameters and forming mechanism is established, which is a new angle in rheological behavior research and constitutive model analysis.

The purpose of this paper is to present designs for an accelerated life test (ALT).

Bayesian methods and simulation Monte Carlo Markov Chain (MCMC) methods were used.

In the paper a Bayesian method based on MCMC for ALT under EW distribution (for life time) and Arrhenius models (relating the stress variable and parameters) was proposed. The paper can conclude that it is a reasonable alternative to the classical statistical methods since the implementation of the proposed method is simple, not requiring advanced computational understanding and inferences on the parameters can be made easily. By the predictive density of a future observation, a procedure was developed to plan ALT and also to verify if the conformance fraction of the manufactured process reaches some desired level of quality. This procedure is useful for statistical process control in many industrial applications.

The results may be applied in a semiconductor manufacturer.

The Exponentiated‐Weibull‐Arrhenius model has never before been used to plan an ALT.

The aim of this paper is to develop a suitable artificial neural network (ANN) model that fits best in predicting the experimental flow stress values to the closet proximity for mechanically alloyed Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite.

The ANN model is implemented on neural network toolbox of MATLAB^® using feed‐forward back propagation network and logsig functions. A set of 80 training data and 20 testing data were used in the ANN model. The layout of the network is arranged with three input parameters that include temperature, strain and strain rate, one hidden layer with 22 neurons and one output parameter consisting of flow stress. Flow stress was also predicted using Arrhenius constitutive model.

Based on the comparison of the predicted results using ANN model and Arrhenius constitutive model, it was observed that the ANN model has higher accuracy and could be used to estimate the flow stress values during hot deformation of Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite.

The ANN trained with feed forward back propagation algorithm developed, presents the excellent performance of flow stress prediction of Al6063/0.75Al2O3/0.75Y2O3 hybrid nanocomposite with minimum error rates.

This article describes reliability analysis and failure cause determination in encapsulated semiconductor devices. Such devices were subjected to a destructive life test using temperature as an accelerating stress. Once the devices had failed, the failure data were statistically analysed with the aid of a digital computer. The failed devices were then decapsulated and failure causes were determined using different types of microscopy. The article gives detailed information about the tests and analysis mentioned above.

The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods.

First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data.

The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R² is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R² are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties.

This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

The purpose of this study is to present and demonstrate a numerical method for solving chemically reacting flows. These are important for energy conversion devices, which rely on chemical reactions as their operational mechanism, with heat generated from the combustion of the fuel, often gases, being converted to work.

The numerical study of such flows requires the set of Navier-Stokes equations to be extended to include multiple species and the chemical reactions between them. The numerical method implemented in this study also accounts for changes in the material properties because of temperature variations and the process to handle steep spatial fronts and stiff source terms without incurring any numerical instabilities. An all-speed numerical framework is used through simple low-dissipation advection upwind splitting (SLAU) convective scheme, and it has been extended in a multi-component species framework on the in-house density-based flow solver. The capability of solving turbulent combustion is also implemented using the Eddy Dissipation Concept (EDC) framework and the recent k-kl turbulence model.

The numerical implementation has been demonstrated for several stiff problems in laminar and turbulent combustion. The laminar combustion results are compared from the corresponding results from the Cantera library, and the turbulent combustion computations are found to be consistent with the experimental results.

This paper has extended the single gas density-based framework to handle multi-component gaseous mixtures. This paper has demonstrated the capability of the numerical framework for solving non-reacting/reacting laminar and turbulent flow problems. The all-speed SLAU convective scheme has been extended in the multi-component species framework, and the turbulent model k-kl is used for turbulent combustion, which has not been done previously. While the former method provides the capability of solving for low-speed flows using the density-based method, the later is a length-scale-based method that includes scale-adaptive simulation characteristics in the turbulence modeling. The SLAU scheme has proven to work well for unsteady flows while the k-kL model works well in non-stationary turbulent flows. As both these flow features are commonly found in industrially important reacting flows, the convection scheme and the turbulence model together will enhance the numerical predictions of such flows.

Lattice Boltzmann method (LBM) has made great success in computational fluid dynamics, and this paper aims to establish an efficient simulation model for the polymer injection molding process using the LBM. The study aims to validate the capacity of the model for accurately predicting the injection molding process, to demonstrate the superior numerical efficiency in comparison with the current model based on the finite volume method (FVM).

The study adopts the stable multi-relaxation-time scheme of LBM to model the non-Newtonian polymer flow during the filling process. The volume of fluid method is naturally integrated to track the movement of the melt front. Additionally, a novel fractional-step thermal LBM is used to solve the convection-diffusion equation of the temperature field evolution, which is of high Peclet number. Through various simulation cases, the accuracy and stability of the present model are validated, and the higher numerical efficiency verified in comparison with the current FVM-based model.

The paper provides an efficient alternative to the current models in the simulation of polymer injection molding. Through the test cases, the model presented in this paper accurately predicts the filling process and successfully reproduces several characteristic phenomena of injection molding. Moreover, compared with the popular FVM-based models, the present model shows superior numerical efficiency, more fit for the future trend of parallel computing.

Limited by the authors’ hardware resources, the programs of the present model and the FVM-based model are run on parallel up to 12 threads, which is adequate for most simulations of polymer injection molding. Through the tests, the present model has demonstrated the better numerical efficiency, and it is recommended for the researcher to investigate the parallel performance on even larger-scale parallel computing, with more threads.

To the authors’ knowledge, it is for the first time that the lattice Boltzmann method is applied in the simulation of injection molding, and the proposed model does obviously better in numerical efficiency than the current popular FVM-based models.

The purpose of this paper is to provide a systematic method to perform long‐term reliability assessment of gold (Au) and copper (Cu) ball bonds in fineline ball grid array package. Also with the aim to study the apparent activation energies (E_aa) and its associated wearout mechanisms of both Au and Cu wire in semiconductor device packaging. This paper discusses the influence of wire type on the long‐term reliability and mechanical performance after several component reliability stress tests.

A fineline ball grid array (FBGA) package with Cu and Au wire bonds was assembled with green molding compound and substrate. Samples are subjected for long‐term high temperature storage bake test at elevated temperatures of 150°C, 175°C and 200°C. Long‐term reliability plots (lognormal plots) are established and E_aa of both ball bonds are determined from Arrhenius plots. Detailed failure analysis has been conducted on failed sample and HTSL failure mechanisms have been proposed.

Reliability results show Au ball bond in FBGA package is observed with higher hour‐to‐failure compared to Cu ball bonds. The E_aa value of high temperature storage life (HTSL) reliability for Au ball bond is lower than Cu ball bond. Typical HTSL failure mechanism of Au ball bond is induced by micro‐voiding and AuAl intermetallic compound (IMC) micro‐cracks while CuAl IMC micro‐cracking (induced by Cl⁻ corrosion attack and micro‐cracking) caused wearout opens in Cu ball bond. These test results affirm the test‐to‐failure data collected is a useful method for lifetime prediction and E_aa calculation.

The paper reveals higher reliability performance of Cu ball bond in FBGA flash memory package which can be deployed in flash memory FBGA packaging with optimised package bill of materials.

The test‐to‐failure methodology is a useful technique for wearout reliability prediction and E_aa calculation.

The purpose of this paper was to develop a physics-based mathematical model to estimate the amount of substrate metal lost during the wet soldering process.

A mathematically rigorous model depicting the actual physics of the substrate/solder interaction and dissolution has been proposed to simulate the dissolution of the substrate metal in the liquid lead-free solder. The basic mass diffusion equation with the implementation of interface reaction kinetics was solved numerically using the finite volume approach. The moving interface was tracked by utilizing the coordinate transformation technique.

It was observed that the process of metal dissolution in the liquid solder was governed by two important parameters, viz., interface kinetics and long-range diffusion in the liquid solder. Non-equilibrium behavior was observed in the early stage of the process. The early stage of the dissolution process was seen as governed by interface kinetics, while diffusion became the rate-controlling mechanism at the later phase of soldering.

Substrate dissolution can be accurately estimated for a particular substrate–solder combination and for the given process conditions. This early estimation will help in ensuring the reliability and health of the solder joint.

A model based on actual physics is proposed, and interface reaction kinetics has been introduced to capture the actual behavior of the process. The model will serve as the basis for two- and three-dimensional analysis, including the formation of an intermetallic compound in the solder joint.