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- Li, Cheng-Wang, et al.
(författare)
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Comparison of mechanical properties of various joining processes of aluminum-titanium light alloy
- 2023
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Ingår i: Suxing Gongcheng Xuebao/Journal of Plasticity Engineering. - : Beijing Res. Inst. of Mechanical and Elec. Technology. - 1007-2012. ; 30:8, s. 138-145
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Tidskriftsartikel (refereegranskat)abstract
- 5A06 aluminum alloy and TA1 titanium alloy were selected for the connection tests of flow drill screw, ultrasonic clinch and ultrasonic self-piercing riveting. The influence of plate overlap methods on mechanical properties of each group of joints was studied by static tensile tests. The results show that the mechanical properties of the flow drill screw joints and the ultrasonic self-piercing riveted joints are significantly improved when the soft plate is on top during the riveting process. The plate overlap method has little effect on the ultrasonic clinch joint. Flow drill screw joints have the best cushioning and shock absorption performance, but the stability is poor, and it is more limited when applied to body structures. Ultrasonic clinch joints have low cost and simple process, but poor shear resistance and are not suitable for body structures subjected to high stress. Compared with the other two connection methods, the ultrasonic self-piercing riveted connection has the best stability, the best tensile resistance and shear stiffness, and is suitable for much of the structure in lightweight field of automobile body. © 2023 Beijing Res. Inst. of Mechanical and Elec. Technology. All rights reserved.
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| 2. |
- Yu, Xiao, et al.
(författare)
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Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM
- 2020
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Ingår i: IEEE Transactions on Reliability. - : Institute of Electrical and Electronics Engineers Inc.. - 0018-9529 .- 1558-1721. ; 69:1, s. 139-153
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Tidskriftsartikel (refereegranskat)abstract
- Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook that ranking a module with more defects incorrectly makes it difficult to successfully find all of the defects in the module due to fewer testing resources being allocated to the module, which results in much higher costs than incorrectly ranking the modules with fewer defects, and the numbers of defects in software modules are highly imbalanced in defective software datasets. Cost-sensitive learning is an effective technique in handling the cost issue and data imbalance problem for software defect prediction. However, the effectiveness of cost-sensitive learning has not been investigated in RODP models. Aims: In this article, we propose a cost-sensitive ranking support vector machine (SVM) (CSRankSVM) algorithm to improve the performance of RODP models. Method: CSRankSVM modifies the loss function of the ranking SVM algorithm by adding two penalty parameters to address both the cost issue and the data imbalance problem. Additionally, the loss function of the CSRankSVM is optimized using a genetic algorithm. Results: The experimental results for 11 project datasets with 41 releases show that CSRankSVM achieves 1.12%-15.68% higher average fault percentile average (FPA) values than the five existing RODP methods (i.e., decision tree regression, linear regression, Bayesian ridge regression, ranking SVM, and learning-to-rank (LTR)) and 1.08%-15.74% higher average FPA values than the four data imbalance learning methods (i.e., random undersampling and a synthetic minority oversampling technique; two data resampling methods; RankBoost, an ensemble learning method; IRSVM, a CSRankSVM method for information retrieval). Conclusion: CSRankSVM is capable of handling the cost issue and data imbalance problem in RODP methods and achieves better performance. Therefore, CSRankSVM is recommended as an effective method for RODP. © 1963-2012 IEEE.
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