• Transactions of the Korean Society of Automotive Engineers
• /
• v.18 no.5
• /
• pp.25-30
• /
• 2010

The authors have studied on the corrosion resistance of the hot stamped steel sheets for the application to automotive parts. Recently automotive companies have focused on the hot stamped parts to meet the light weighting needs and the safe reason. Because of the cost reduction of the hot stamped parts, automotive companies increase the usage of the coated steel sheets, especially Al-Si coated steel sheets. The coated layer of Al-Si coated steel sheets contains up to 50% of Fe, which was diffused from the steel sheet, after hot stamping. The hot stamped steel sheet was not phosphated due to the oxidation layer of the coating, however, the result of the water resistance test is similar to that of the conventional GA steel sheets. The pitting depth and the weight reduction of the coated layer of hot stamped steel sheets are less than those of GA steel.

• Corrosion Science and Technology
• /
• v.10 no.2
• /
• pp.47-51
• /
• 2011

Main issues in the automotive industry are the reduction of vehicle body weight for energy savings and improvement of crashworthiness for passenger safety. In order to address both these issues, there has recently been increasing application of galvannealed advance high strength steel (GA AHSS) sheets for automobiles. However, GA AHSS sheets have some surface defects such as coating bare spots due to the addition of solid-solution strengthening elements, which result in the deterioration of the galvannealing reaction. In this study, the effects of galvannealed manufacturing conditions on surface and mechanical properties, resistance spot weldability on a laboratory scale, and GA 980 MPa steel sheets produced by commercial continuous galvannealing line (CGL) were investigated.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.13 no.3
• /
• pp.337-349
• /
• 2000

Genetic Algorithms(GA) together with simulated annealing are often called methods of last resorts since they can be applicable to any kind of problems, particularly those to which no sophisticated procedures are applicable or feasible. The design of structures is primarily the process of selecting a section for each member from those available in the market, resulting in the problem of combinatorial nature. Therefore it is usual for the design space to include astronomical number of designs making the search in the space often impossible. In this work, Genetic Algorithms and some related technique are introduced and applied to the design of steel frameworks. In problems with a small number of design variables, GA found true global optima. GA also found true optima for the continuous variable test problems and proved their applicability to structural optimization. For those problems of real size, however, it appears to be difficult to expect GA to find optimum or even near optimum designs. The use of G bit improvement added to ordinary GA has shown much better results and draws attention for further research.

• Transactions of Materials Processing
• /
• v.14 no.8 s.80
• /
• pp.696-703
• /
• 2005

In the modern days, a galvannealed sheet steel (GA) instead of a cold rolled steel sheet has been widely used as an alternative to extend the life of automotive body. Accordingly, the mechanical properties of GA for automobiles were taken into account and studied by examining their variation with annealing temperature. To clarify the effect of surface features on the mechanical and frictional properties of GA, the several tests such as nanoindentation, Vickers hardness and nano scratch test were executed. The frictional characteristics of coating layers of GA were examined through nano scratch test in this study. The friction coefficient of coating layers on the surface was obtained from the nano scratch. The variation of friction coefficient versus velocity and pressure was taken into consideration in this paper. Hardness and elastic modulus of coating layer were increased as increasing annealing temperature.

• Steel and Composite Structures
• /
• v.15 no.5
• /
• pp.519-538
• /
• 2013

The design optimization of a cold-formed steel portal frame building is considered in this paper. The proposed genetic algorithm (GA) optimizer considers both topology (i.e., frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables. Previous GAs in the literature were characterized by poor convergence, including slow progress, that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is shown to be an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Thus, population diversity is maintained and premature convergence is reduced significantly. Through benchmark examples, it is shown that the efficient GA proposed generates optimal solutions more consistently. A parametric study was carried out, and the results included. They show significant variation in the optimal topology in terms of pitch and frame spacing for a range of typical column heights. They also show that the optimized design achieved large savings based on the cost of the main structural elements; the inclusion of knee braces at the eaves yield further savings in cost, that are significant.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2005.10a
• /
• pp.113-117
• /
• 2005

In the modern days, a galvannealed sheet steel (GA) instead of a cold rolled steel sheet has been widely used as an alternative to extend the life of automotive body. Accordingly, the mechanical properties of GA for automobiles were taken into account and studied by comparing with the temperature variation on annealing in this study. To clarify the effect of surface features in the mechanical and frictional properties of GA, the several tests such as nanoindentation, victors hardness and nano scratch test were executed. The developed neural networks apply also to obtain reliable mechanical properties of the thin films. Load-displacement curve was computed by the analysis procedure and compared with experimental results. The frictional characteristics of coating layers in GA were verified though nano scratch test in this study. The friction coefficient of coating layers on the surface was obtained from the nano scratch. The variation of friction coefficient versus velocity and pressure was taken into consideration in this paper.

• Journal of Korean Society of Steel Construction
• /
• v.18 no.2
• /
• pp.147-160
• /
• 2006

GA-based fuzzy optimum design algorithm incorporated with the refined plastic hinge analysis method is presented in this study. In the refined plastic hinge analysis method, geometric nonlinearity is considered by using the stability functions of the beam-column members. Material nonlinearity is also considered by using the gradual stiffness degradation model, which considers the effects of residual stresses, moment redistribution through the occurence of plastic hinges, and the geometric imperfections of the members. In the genetic algorithm, the tournament selection method and the total weight of the steel frames. The requirements of load-carrying capacity, serviceability, ductility, and constructabil ity are used as the constraint conditions. In fuzzy optimization, for crisp objective function and fuzzy constraint s, the tolerance that is accepted is 5% of the constraints. Furthermore, a level-cut method is presented from 0 to 1 at a 0 .2 interval, with the use of the nonmembership function, to solve fuzzy-optimization problems. The values of conventional GA optimization and fuzzy GA optimization are compared in several examples of steel structures.

• Corrosion Science and Technology
• /
• v.10 no.1
• /
• pp.1-5
• /
• 2011

The major purpose of the present study is to evaluate the performance of various galvanized (GI) or galvannealed (GA) mild steels and AHSS in stamping applications. Finite Element Analysis (FEA) of selected stamping operations was conducted to estimate the critical pressure boundary conditions that exist in practice. Using this information, laboratory tribotests, e.g. Twist Compression (TCT), Deep Drawing (DDT) and Strip Drawing (SDT) Tests, were developed to evaluate the performance of selected lubricants and die materials/coatings in forming galvanized steels of interest. The sheet materials investigated included mild steels and AHSS (e.g. DP600 GI/GA, DP780 GI/GA, TRIP780 GA and DP980 GI/GA). Experimental results showed that galvanized material resulted in more galling, while galvannealed material showed more powdering and flaking. The surface roughness and chemical composition of galvanized sheet materials affected the severity of galling under the same testing conditions, i.e. lubricants and die materials/coatings. The results of this study helped to determine the critical interface pressure that initiates lubricant failure and galling in stamping selected galvanized sheet materials. Thus, to prevent or postpone the critical interface conditions, the results of this study can be used to select the optimum combination of galvanized sheet, die material, die coating and lubricant for forming structural automotive components.

• Steel and Composite Structures
• /
• v.47 no.6
• /
• pp.759-779
• /
• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Steel and Composite Structures
• /
• v.19 no.2
• /
• pp.503-519
• /
• 2015

This paper presents an optimization process using Genetic Algorithm (GA) for minimum weight by selecting suitable standard sections from a specified list taken from American Institute of Steel Construction (AISC). The stress constraints obeying AISC-LRFD (American Institute of Steel Construction-Load and Resistance Factor Design), lateral displacement constraints being the top and inter-storey drift, mid-span deflection constraints for the beams and geometric constraints are considered for optimum design by using GA that mimics biological processes. Optimum designs for three different space frames taken from the literature are carried out first without considering concrete slab effects in finite element analyses for the constraints above and the results are compared with the ones available in literature. The same optimization procedures are then repeated for the case of space frames with composite (steel and concrete) beams. A program is coded in MATLAB for the optimization processes. Results obtained in the study showed that consideration of the contribution of the concrete on the behavior of the floor beams results with less steel weight and ends up with more economical designs.