The present research focuses on the tribological behavior of the AA5083 alloy-based hybrid surface composite using aluminosilicate and multi-walled-carbon nanotube through friction stir processing for automotive applications. The friction stir processing parameters (tool rotation and traverse speed) are varied based on full factorial design to understand their influence on the tribological characteristics of the developed hybrid composite. The surface morphology and composition of the worn hybrid composite are examined using a field-emission scanning electron microscope and an energy-dispersive x-ray spectroscope. No synergistic interaction is observed between the wear rate and friction coefficient of the hybrid composite plate. Also, adhesive wear is the major wear mechanism in both base material and hybrid composite. The influence of friction stir process parameters on wear rate and the friction coefficient is analyzed using the hybrid polynomial and multi-quadratic radial basis function. The models are utilized to optimize the friction stir processing parameters for reducing the rate of wear and friction coefficient using multi-quadratic RBF algorithm optimization.

The CSR rule was defined by IACS as the unified rule for a commercial ship like a bulk carrier and a tanker. It have been required more strict conditions for various parts like loading conditions, the local and girder strength, fatigue strength, FEM for the ship rule. It was changed in many parts of the ship rules. In this paper, the mid-parts of 17.5K DWT bulk carrier were optimized by the CSR rule. On the other hand, the modified artificial life algorithms with multi-object functions were developed for optimizing the scantling. It is possible to find multi-global optimum solutions in the multi-object functions. And it is faster and efficient than the artificial life algorithm. First, to be optimizing the scantling and the weight by CSR rule, that is calculated by the CSR rule. The next, the result is re-calculated by the modified artificial life algorithm with multi-object functions. The optimized results which are satisfied with the CSR rule like the minimum size and the thickness of stiffener and the minimum cost have been searched by the optimizing algorithm. And the results have been compared with the non-optimizing results.

의사 결정 나무 알고리즘은 관심이 되는 집단을 몇 개의 소집단으로 분류하거나 특성을 예측하기 위한 데이터 마이닝 분석 기법중 하나이다. 이 기법은 각 업종별 특성을 분석하여 업종별 차이점을 찾기 위해 사용되었다. 여기에서 사용되어진 의사결정 알고리즘으로는 C4.5알고리즘을 사용하였다. 트리는 이득율(Gain Ratio)에 의해서 Top-Down방식으로 구성하게 되었다. 본 연구에서 사용된 데이터는 2003년, 2004년에 발생 되어진 데이터로 총 25,159개의 데이터를 대상으로 정제과정을 거쳐 24,887개의 데이터를 사용하였고, 한 개의 종속 변수와 8개의 독립 변수로 이루어져 있다. 총 222개의 트리 노드가 만들어 졌고 최종 노드(Leaf Node)는 총 151개가 생성되었다. 생성된 트리 결과에 대한 정확성 측정을 위해 정확도(Accuracy), 오분류 확률(Misclassification Rate)을 계산하였다.