Quasi-static and dynamic loading responses and constitutive modeling of titanium alloys

Dynamic response of conventional and hot isostatically pressed Ti–6Al–4V alloys: experiments and modeling

In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based on the result of above indices was developed. Apart from CER, the total ranking system was also used to rank the obtained statistical indexes. As a result, both models presented good performance, however, according to the introduced ranking system, the PSO-ANN model could perform a better performance compared to ANN. According to R2 and RMSE values of (0.9717 and 0.1040) and (0.99131 and 0.0366) were found for training dataset and values of (0.9733 and 0.111) and (0.9899 and 0.0389) obtained for testing dataset, respectively, for the ANN and PSO-ANN approximation models, it can be resulted that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN.

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

The evaluation and precise prediction of safety factor (SF) of slopes can be useful in designing/analyzing these important structures. In this study, an attempt has been made to evaluate/predict SF of many homogenous slopes in static and dynamic conditions through applying various hybrid intelligent systems namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN and artificial bee colony (ABC)-ANN. In fact, ICA, PSO, GA and ABC were used to adjust weights and biases of ANN model. In order to achieve the aim of this study, a database composed of 699 datasets with 5 model inputs including slope gradient, slope height, friction angle of soil, soil cohesion and peak ground acceleration and one output (SF) was established. Several parametric investigations were conducted in order to determine the most effective factors of GA, ICA, ABC and PSO algorithms. The obtained results of hybrid models were check considering two performance indices, i.e., root-mean-square error and coefficient of determination $$(R^{2})$$
 . To evaluate capability of all hybrid models, a new system of ranking, i.e., the color intensity rating, was developed. As a result, although all predictive models are able to approximate slope SF values, PSO-ANN predictive model can perform better compared to others. Based on $$R^{2}$$
 , values of (0.969, 0.957, 0.980 and 0.920) were found for testing of ICA-ANN, ABC-ANN, PSO-ANN and GA-ANN predictive models, respectively, which show higher efficiency of the PSO-ANN model in predicting slope SF values.

Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

Deformation induced anisotropic responses of Ti–6Al–4V alloy Part II: A strain rate and temperature dependent anisotropic yield criterion

Titanium armor is being incorporated into the design of existing and future U.S. Army combat vehicles because it offers the best method of reducing vehicle weight and thus improving performance. Titanium’s excellent specific mechanical and ballistic properties, as well as ease of fabrication, will likely make it a part of the U.S. Army’s future combat system. How large a part it will be will depend on an overall cost/performance optimization.

Titanium armor applications in combat vehicles

The U.S. Army has been using more and more titanium to either increase armor or reduce the weight of current combat vehicles. Future plans call for the development of combat vehicles that are 30 percent lighter. To achieve this target, the future-vehicle hull and turret will have to be manufactured using more ballistically efficient materials than rolled homogeneous steel armor. Lowcost titanium, with its good mechanical, ballistic, and corrosion properties and acceptable fabricability, offers the overall best afternative to achieving this objective.

Low-cost titanium armors for combat vehicles

Purpose – The purpose of this paper is to discuss the recently developed multi-physics computational model for the conventional Gas Metal Arc Welding (GMAW) joining process that has been upgraded with respect to its predictive capabilities regarding the spatial distribution of the mechanical properties controlling the ballistic limit (i.e. penetration resistance) of the weld. Design/methodology/approach – The original model consists of five modules, each dedicated to handling a specific aspect of the GMAW process, i.e.: electro-dynamics of the welding-gun; radiation-/convection-controlled heat transfer from the electric arc to the workpiece and mass transfer from the filler-metal consumable electrode to the weld; prediction of the temporal evolution and the spatial distribution of thermal and mechanical fields within the weld region during the GMAW joining process; the resulting temporal evolution and spatial distribution of the material microstructure throughout the weld region; and spatial distribution ...

Modeling of ballistic-failure mechanisms in gas metal arc welds of mil a46100 armor-grade steel

A conventional gas metal arc welding (GMAW) butt-joining process has been modeled using a two-way fully coupled, transient, thermal-mechanical finite-element procedure. To achieve two-way thermal-mechanical coupling, the work of plastic deformation resulting from potentially high thermal stresses is allowed to be dissipated in the form of heat, and the mechanical material model of the workpiece and the weld is made temperature dependent. Heat losses from the deposited filler-metal are accounted for by considering conduction to the adjoining workpieces as well as natural convection and radiation to the surroundings. The newly constructed GMAW process model is then applied, in conjunction with the basic material physical-metallurgy, to a prototypical high-hardness armor martensitic steel (MIL A46100). The main outcome of this procedure is the prediction of the spatial distribution of various crystalline phases within the weld and the heat-affected zone regions, as a function of the GMAW process parameters. The newly developed GMAW process model is validated by comparing its predictions with available open-literature experimental and computational data.

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Gas Metal Arc Welding Process Modeling and Prediction of Weld Microstructure in MIL A46100 Armor-Grade Martensitic Steel

A multiphysics computational model has been developed for the conventional Gas Metal Arc Welding (GMAW) joining process and used to analyze butt-welding of MIL A46100, a prototypical high-hardness armor martensitic steel. The model consists of five distinct modules, each covering a specific aspect of the GMAW process, i.e., (a) dynamics of welding-gun behavior; (b) heat transfer from the electric arc and mass transfer from the electrode to the weld; (c) development of thermal and mechanical fields during the GMAW process; (d) the associated evolution and spatial distribution of the material microstructure throughout the weld region; and (e) the final spatial distribution of the as-welded material properties. To make the newly developed GMAW process model applicable to MIL A46100, the basic physical-metallurgy concepts and principles for this material have to be investigated and properly accounted for/modeled. The newly developed GMAW process model enables establishment of the relationship between the GMAW process parameters (e.g., open circuit voltage, welding current, electrode diameter, electrode-tip/weld distance, filler-metal feed speed, and gun travel speed), workpiece material chemistry, and the spatial distribution of as-welded material microstructure and properties. The predictions of the present GMAW model pertaining to the spatial distribution of the material microstructure and properties within the MIL A46100 weld region are found to be consistent with general expectations and prior observations.

Jonathan S. Montgomery

Papers

Titanium armor applications in combat vehicles

Low-cost titanium armors for combat vehicles

Modeling of ballistic-failure mechanisms in gas metal arc welds of mil a46100 armor-grade steel

Gas Metal Arc Welding Process Modeling and Prediction of Weld Microstructure in MIL A46100 Armor-Grade Martensitic Steel

Multiphysics Modeling and Simulations of Mil A46100 Armor-Grade Martensitic Steel Gas Metal Arc Welding Process