No abstract

No abstract

Multi-Criteria Decision Making (MCDM) investigates the best available choice in the presence of multiple conflicting criteria, whereas the Collaborative Unbiased Rank List Integration (CURLI) method has been proposed recently and has been applied in various fields of daily life. However, most previous works concentrated on analyzing cases in which the factor of a criterion is a specific quantity. The present paper proposes an approach developed from the original CURLI method, named Improved CURLI. This improvement helps solve a problem when the factors of the criteria can be linguistic variables or a data set. The proposed method is applied to rank the alternatives for two case studies: choosing the best grinding wheel and the best service suppliers. The ranking results are compared to those obtained using other methods. Furthermore, sensitivity analysis is also conducted to examine the stability and reliability of the ranking results in various scenarios. The results demonstrate the validity of the Improved CURLI method and prove that it is applicable for making decisions in various fields.

This work addresses the prediction and optimization of average surface roughness (Ra) and maximum flank wear (Vbmax) of 6061 aluminum alloy during high-speed milling. The investigation was done using a DMU 50 CNC 5-axis machine with Ultracut FX 6090 fluid. Four factors were examined: the table feed rate, cutting speed, depth of cut, and cutting length. Three levels of each factor were examined to conduct 81 experiment runs. The response parameters in these experiments were measurements of Ra and Vbmax. We applied a two-pronged approach that combines machine learning (ML) and a Nondominated Sorting Genetic Algorithm (NSGA-II) to model and optimize Ra and Vbmax. Four ML models were used to predict Ra and Vbmax: linear regression (LIN), support vector machine regression (SVR), a gradient boosting tree (GBR), and an artificial neural network (ANN). The input variables were the significant factors that affect the surface quality and tool wear: the feed rate, depth of cut, cutting speed, and cutting time. Several quality metrics were employed to quantify the performance of the models, such as the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). As a result, SVR and ANN were found to have the best predictive performance for Ra and Vbmax. These models and the NSGA-II-based approach were then employed for multiobjective optimization of cutting parameters during high-speed milling of aluminum 6061. Fifty Pareto solutions were found with Ra in the range of 0.257 to 0.308 µm and Vbmax in the range of 136.198 to 137.133 μm. Experimental validations were then conducted to confirm that the optimum solution was within an acceptable error range. More precisely, the absolute percentage errors for Ra and Vbmax were 2.5% and 1.5%, respectively. This work proposes an effective strategy for efficiently combining machine learning techniques and the NSGA-II multiobjective optimization algorithm. The experimental validations have reflected the potential for applying this strategy in various machining-optimization problems.

The dynamic process of an underwater explosion (UNDEX) bubble in the vicinity of deformable structures is a complex phenomenon that has been studied by many researchers. The dynamic process of a UNDEX bubble is a complex transient problem that results in a highly distorted bubble and large deformation of the structure. The previous work has introduced various solutions for studying the interaction between the UNDEX bubble and deformable structure. The interaction between the bubble and nearby structures has been widely solved by the combination of the boundary element method (BEM) and the finite element method (FEM). However, this couple requires tight time-step controlling, long-time analysis, and large computer resources. Furthermore, this combination is not widely used as the FEM code in commercially available software for solving UNDEX bubble problems. This paper presents a coupled Eulerian-Lagrangian (CEL) approach in commercial software to deal with the fluid-structure interaction (FSI). The numerical model of a UNDEX bubble is first developed and verified by comparing results with experimental, BEM, and empirical data. Then both bubble behavior and structural deformation are examined in various case studies. The numerical results show that the stiffness of the structure has strongly influenced the bubble behavior and the water jet development. The pressure pulse becomes significantly large as the bubble collapse. Besides, this numerical approach also can reproduce crucial phenomena of a UNDEX bubble, such as the whipping effect and water jet attacks. Although the numerical model is developed using simplified boundary conditions, the proposed approach shows the feasibility of simulating the important features of a UNDEX bubble process as well as the response of nearby structures.