Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm

2017 ◽  
Vol 34 (4) ◽  
pp. 1034-1053 ◽  
Author(s):  
Dalian Yang ◽  
Yilun Liu ◽  
Songbai Li ◽  
Jie Tao ◽  
Chi Liu ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Prediction Accuracy ◽  
Artificial Bee Colony ◽  
7075 Aluminum Alloy ◽  
Prediction Ability ◽  
Content Type ◽  
Abc Algorithm ◽  
Bee Colony

Purpose The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods. Design/methodology/approach The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed. Findings The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly. Originality/value A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.

Acceleration of fatigue crack growth due to occasional mode II loading in 7075 aluminum alloy

2014 ◽  
Vol 123 ◽  
pp. 126-136 ◽  
Author(s):  
H. Matsunaga ◽  
M. Makizaki ◽  
D.F. Socie ◽  
K. Yanase ◽  
M. Endo
Keyword(s):  
Aluminum Alloy ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Mode Ii ◽  
7075 Aluminum Alloy ◽  
Mode Ii Loading

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

Experimental and numerical investigation of fatigue crack growth in aluminum plates repaired by FML composite patch

2014 ◽  
Vol 5 (4) ◽  
pp. 242-252 ◽  
Author(s):  
M. Rahmani Kalestan ◽  
H. Moayeri Kashani ◽  
A. Pourkamali Anaraki ◽  
F. Ashena Ghasemi
Keyword(s):  
Aluminum Alloy ◽  
Fatigue Crack ◽  
Fatigue Life ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
High Strength ◽  
Notched Specimen ◽  
Content Type ◽  
Composite Patch ◽  
Crack Angle

Purpose – The purpose of this paper is to use the fiber metal laminates (FML) composites as a patch for repairing a single notched specimen made of AL1035 aluminum alloy. The FML composite patch was bonded on one side of the cracked specimens by adhesive Araldite 2015. Then the fatigue crack growth tests were conducted on the specimens and the effects of both FML patch lay-up sequence and pre-crack angle on the fatigue life were investigated. Finally, the effect of repairing on the fracture parameters (SIF and crack propagation direction) at the crack front has also been calculated using three-dimensional finite element analysis. Design/methodology/approach – The fatigue crack growth tests were conducted on the specimens and the effects of both FML patch lay-up sequence and pre-crack angle on the fatigue life were investigated. Findings – The results show that the fatigue life of the patched specimens with inclined crack increased approximately 2-6.02 times compared to the un-patched specimens. In addition, the fatigue crack growth rate decreased significantly when the patch was used. Generally, the FML patch with Plate-Fiber-Fiber-AL lay-up has more efficiency than other lay-up sequences. Originality/value – Recently, composite patches are used in the structure repair processes to increase the service life of cracked components. The bonded patch method is one of the efficient methods among repairing methods. Today, the FMLs are used in the aircraft structures as a replacement of high-strength aluminum alloys due to their lightweight and high-strength properties. Many researches have been performed on single and double side repaired panels using composite patches. In this study, the FML composites have been used as a patch for repairing a single notched specimen made of AL1035 aluminum alloy.

Environmental Effects on Fatigue Crack Growth in 7075 Aluminum Alloy

2011 ◽  
Vol 43 (8) ◽  
pp. 2799-2809 ◽  
Author(s):  
A. Bonakdar ◽  
F. Wang ◽  
J. J. Williams ◽  
N. Chawla
Keyword(s):  
Aluminum Alloy ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Environmental Effects ◽  
7075 Aluminum Alloy

A new fuzzy random multi-objective portfolio model with different entropy measures using fuzzy programming based on artificial bee colony algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue Deng ◽  
Xiaolei He ◽  
Cuirong Huang
Keyword(s):  
Artificial Bee Colony ◽  
Fuzzy Programming ◽  
Optimal Portfolio ◽  
Content Type ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Proposed Model ◽  
Entropy Measures ◽  
Fuzzy Random

PurposeThis paper proposes a fuzzy random multi-objective portfolio model with different entropy measures and designs a hybrid algorithm to solve the proposed model.Design/methodology/approachBecause random uncertainty and fuzzy uncertainty are often combined in a real-world setting, the security returns are considered as fuzzy random numbers. In the model, the authors also consider the effects of different entropy measures, including Yager's entropy, Shannon's entropy and min-max entropy. During the process of solving the model, the authors use a ranking method to convert the expected return into a crisp number. To find the optimal solution efficiently, a fuzzy programming technique based on artificial bee colony (ABC) algorithm is also proposed.Findings(1) The return of optimal portfolio increases while the level of investor risk aversion increases. (2) The difference of the investment weights of the optimal portfolio obtained with Yager's entropy are much smaller than that of the min–max entropy. (3) The performance of the ABC algorithm on solving the proposed model is superior than other intelligent algorithms such as the genetic algorithm, differential evolution and particle swarm optimization.Originality/valueTo the best of the authors' knowledge, no effect has been made to consider a fuzzy random portfolio model with different entropy measures. Thus, the novelty of the research is constructing a fuzzy random multi-objective portfolio model with different entropy measures and designing a hybrid fuzzy programming-ABC algorithm to solve the proposed model.

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