Earticle

In electric vehicles, lightweight design is an important development objective for improving energy efficiency. Seat frame materials that are important for weight reduction use steel. In this study, weight optimization analysis was conducted by applying Almag, an aluminu-magnesium alloy material that offers excellent weight reduction while maintaining structural strength, to the seat frame. First, key stiffness members among the seat components were identified. Static strength analysis and natural frequency analysis were then performed on the steel seat frame. Based on the analysis results, a static optimization analysis was carried out for the application of the Almag material to achieve displacement levels equivalent to the static strength analysis. In addition, a dynamic optimization analysis was performed to maximize the natural frequency. Through these analyses, the optimal thicknesses of the seat back and cushion frame were determined.

Recently, Car weight reduction has become an important development goal to improve fuel efficiency. Car seat frame is a key part of the weight reduction. Existing steel seat frames have the advantages of high rigidity and durability, but have the disadvantage of heavy weight. Recently, Almag material, which are alloy of aluminum and magnesium, is attracting attention because of excellence in strength and weight reduction. At first, the core stiffness members of the seat frame are selected to optimize the weight of the seat frame. And then strength analysis and natural frequency analysis are performed for the existing steel seat frame and Almag seat frame. Based on these analysis results, optimal thickness of the Almag seat frame are determined by an automation program using a genetic algorithm.

The sub-frame is located on the lower body of a monocoque type vehicle and serves as an engine and suspension, and is an important object part that receives a lot of load. The existing press-type sub-frame has a large number of parts for assembling, which causes an increase in cost. Changing the machining form of this part from the existing press-type machining method to the hydro-forming machining method has the advantage of reducing the cost and weight at the same time due to the reduction of the process. Therefore, in this study, the purpose of this study is to change the design so that the sub-frame of the existing press type can be changed to the hydro-forming process method. To this end, we intend to present a design method by analyzing the effect on the rigidity of the sub-frame using the existing machining method through shape optimization analysis.

Particle swarm optimization is the optimization technique motivated by swarm intelligence and aims to find the best solution in the swarm. Aging leader and challengers with Particle swarm optimization (ALC-PSO) is a population based optimization method which introduced the concept of aging and challenger generation in the PSO technique. This variant of PSO has been successful in preventing premature convergence of PSO and maintaining swarm diversity. In this paper, we briefly reviewed the inertia weight parameter and its strategies in PSO and experimentally analyzed the effect of inertia weight strategies on ALC-PSO performance. Comparison is drawn between PSO and ALC-PSO based on these strategies. Results are obtained using five different benchmark functions.

Particle Swarm Optimization is a popular heuristic search technique developed by Eberhart and Kennedy in 1995 which takes its inspiration from the social and cognitive learning of birds or fishes. This algorithm comprises the involvement of swarm intelligence technique for optimization. The most widely accepted variation of the basic PSO technique is PSO with Inertia weight which substantially controls the convergence behaviour and exploration exploitation trade-off in the basic PSO technique. From its initialization onwards a huge range of modifications of Inertia Weight strategy have been recommended. This paper involves the use of PSO with varying values of inertia weight for solving the Travelling Salesman Problem. An analysis of how different inertia weight values effect the solution in terms of time complexity, space complexity and convergence in carried out in order to know the value best suited for setting up the inertia weight.