Imbalanced datasets are pervasive in classification tasks and would cause degradation of the performance of classifiers in predicting minority samples. Oversampling is effective in resolving the class imbalance problem. However, existing oversampling methods generally introduce noise examples into original datasets, especially when the datasets contain class overlapping regions. In this study, a new oversampling method named Constrained Oversampling is proposed to reduce noise generation in oversampling. This algorithm first extracts overlapping regions in the dataset. Then Ant Colony Optimization is applied to define the boundaries of minority regions. Third, oversampling under constraints is employed to synthesize new samples to get a balanced dataset. Our proposal distinguishes itself from other techniques by incorporating constraints in the oversampling process to inhibit noise generation. Experiments show that it outperforms various benchmark oversampling approaches. The explanation for the effectiveness of our method is given by studying the impact of class overlapping on imbalanced learning.

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades. However, few papers systematically review these researches. By analyzing existing literature, this paper summarizes the process of fixture layout optimization and the methods applied. The process of optimization is made up of optimization objective setting, assembly variation/deformation modeling, and fixture layout optimization. This paper makes a review of the fixture layout for thin-walled parts according to these three steps. First, two different kinds of optimization objectives are introduced. Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives. Then, modeling methods for assembly variation and deformation are divided into two categories: Mechanism-based and data-based methods. Several common methods are discussed respectively. After that, optimization algorithms are reviewed systematically. There are two kinds of optimization algorithms: Traditional nonlinear programming and heuristic algorithms. Finally, discussions on the current situation are provided. The research direction of fixture layout optimization in the future is discussed from three aspects: Objective setting, improving modeling accuracy and optimization algorithms. Also, a new research point for fixture layout optimization is discussed. This paper systematically reviews the research on fixture layout optimization for thin-walled parts, and provides a reference for future research in this field.

In a ship assembly process, a large number of compliant parts are involved. The ratio of the part thickness to the length or the width is typically 0.001–0.012. Fixture design is a critical task in the ship assembly process due to its impact on the deformation and dimensional variation of the compliant parts. In current practice, fixtures are typically uniformly distributed under the part to be assembled, which is non-optimal, and large dimensional gaps may occur during assembly. This paper proposed a methodology for the optimal design of the fixture layout in the ship assembly process by systematically integrating direct stiffness method and simulated annealing algorithm, which aims to minimize dimensional gaps along the assembly interface to further improve the quality and efficiency of seam welding. The case study shows that the proposed method significantly reduced the dimensional gaps of the compliant curved panel parts in a ship assembly process.

Ship assembly involves thousands of large dimensional compliant metal plates. These compliant metal plates are fully welded together by seam welding in the assembly process. Different from the automobile and aerospace assembly process, the final variation of ship assembly is significantly influenced by the geometric nonlinearity and welding deformation generated during the seam welding process. This paper develops a nonlinear variation model (NVM) to consider the geometric nonlinearity, welding shrinkage, and angular distortion based on elastic mechanics. Furthermore, the nonlinear variation model is calibrated by the composite Gaussian process (CGP) to compensate for other factors that are not considered in the nonlinear variation model. The proposed model is validated by a case study on the deviation prediction of an assembly of two compliant metal plates and compared with the existing methods. The results show that the proposed model has a significant improvement in prediction accuracy of assembly deviation.