A Self-Configuring Membership-Function-Based Approach for Fuzzy Fatigue Reliability Optimization of Welded A-Type Frame Considering Multi-Source Uncertainties

Mi, Chengji; Li, Wentai; Xiao, Xuewen; Liu, Jinhua; Xin, Ming

doi:10.3390/app9112227

Cited by 4 publications

(4 citation statements)

References 21 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By utilizing the fuzzy fatigue reliability optimization based on evolutionary algorithms, its reliability ranged from 69.47% to 95.12% [108]. Also, it was inferred that a better welding process can help improve the fatigue performance of welded A-type frame in a heavy off-road mining truck [112].…”

Section: Resultsmentioning

confidence: 99%

“…In order to optimize fatigue reliability in welded A-type frames while weighing several sources of uncertainty, Mi et al [112] developed a solution, in which the following techniques were used: membership function, Latin hypercube sampling, response surface methodology, and genetic algorithm (GA). In another work by Zou et al [113], they created an intelligent approach for forecasting the life of aluminum welded connections after fatigue.…”

Section: Random/hybrid Based Methodsmentioning

confidence: 99%

“…Bridges [90,92,93], energy [94]. Steel [96,97,102,106,108,109,112,114,115,118,120,121,123,126,[129][130][131][132], aluminum [107,110], titanium [108], stainless steel [114,119], CFRP + steel [121],…”

Section: Regressionmentioning

confidence: 99%

See 2 more Smart Citations

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

View full text Add to dashboard Cite

In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Random/hybrid Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The fuzzy † Deceased on March 16, 2020. reliability model, based on the fuzzy set theory, is another widely accepted method in fatigue reliability considering the uncertainties. [7][8][9] Here, the fuzziness of the uncertain parameters is described by a membership function, and fatigue reliability is evaluated by the fatigue life under a certain degree of fuzziness. However, in the random reliability and the fuzzy reliability methods, or a combination of these two, 10,11 precise probability distribution functions or membership functions constitute the premises, on which the fatigue reliability analyses are based.…”

Section: Introductionmentioning

confidence: 99%

Weak ternary interval evaluation of fatigue life and its application to mining dump truck

et al. 2020

Fatigue Fract Eng Mat Struct

Self Cite

View full text Add to dashboard Cite

A method of weak ternary interval (TI) evaluation of fatigue life is proposed in this study to reasonably evaluate the nonprobabilistic reliability considering the interval uncertainty. The conventional interval is extended into a TI by introducing an expected value, that is, a value without uncertainty, as the third parameter of the interval to deal with the deviation between the expected value and the median value of the interval. Next, the TI of fatigue life is evaluated by introducing an index of attitude in the exponential function to weaken the unimportant upper bound and deal with the overconservative lower bound. Therefore, the evaluation is reflected in the form of an equivalent fatigue life as an indicator of the nonprobabilistic reliability. Next, the TIs of the fatigue parameters are obtained with limited fatigue test data from a practical engineering application. Finally, the proposed method is applied to a mining dump truck frame to demonstrate its significance and validity.

show abstract

Prediction of welded joint fatigue properties based on a novel hybrid SPDTRS-CS-ANN method

Feng

Zhao

et al. 2022

Engineering Fracture Mechanics

View full text Add to dashboard Cite

A Self-Configuring Membership-Function-Based Approach for Fuzzy Fatigue Reliability Optimization of Welded A-Type Frame Considering Multi-Source Uncertainties

Cited by 4 publications

References 21 publications

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Weak ternary interval evaluation of fatigue life and its application to mining dump truck

Prediction of welded joint fatigue properties based on a novel hybrid SPDTRS-CS-ANN method

Contact Info

Product

Resources

About