Multi-Performance Optimization in Friction Stir Welding of AA6082/B4C Using Genetic Algorithm and Desirability Function Approach for Aircraft Wing Structures

Sandeep, Renangi; Jeevanantham, A.K.; Manikandan, M.; Tofil, Szymon

doi:10.1007/s11665-021-05842-w

Cited by 8 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on their research, these algorithms can provide inexpensive and accurate results [446]. Bio-inspired algorithms such as GA also have applications in optimizing process parameters of welding in aircraft wing structures [447]. Some other in-detail structural specifications can also be optimized, including aeroelasticity characteristics and elastic deformation in classic aluminum-based structures or even composite materials [448].…”

Section: Structure Designmentioning

confidence: 99%

“…Publication Year Application Algorithm [445] 2005 Component (rib, wing, etc.) design GA [446] 2009 Pressure bulkhead design GA, PSO, CO [447] 2021 Welding process optimization GA [448] 2015 Elastic optimization PSO [449] 2018 Stiffened panels optimization HS [450] 2014 Aeroelastic composite wing design BCO [451] 2014 Aeroelastic tailoring and scaling BFO…”

Section: Referencementioning

confidence: 99%

See 1 more Smart Citation

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

View full text Add to dashboard Cite

This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

show abstract

Section: Structure Designmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…The selected optimization approach had become the preferred strategy for solving nonlinear and complex problems of both single and multi-response optimizations due to its ease of use. 23 The RSM-based regression model, that is, Equation 3, was used as the objective function for the optimization. The constraints of process parameters such as TRS, WS and TRS are provided in Supplementary Table 5.…”

Section: Optimization Of Fsw Process Parametersmentioning

confidence: 99%

Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

Sandeep

Natarajan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

Self Cite

View full text Add to dashboard Cite

Vehicle weight has been a critical concern in the aerospace and automobile industries for decades. Integrating dissimilar aluminium and polymer hybrid structures is beneficial for weight reduction without affecting structural performance. In the present work, aluminium alloy 7475 and polyphenylene sulfide (PPS) sheets were joined using the friction stir welding (FSW) technology in lap joint configuration. A series of FSW experiments have been performed by the design matrix developed using response surface methodology. Tensile lap shear strength (TLS) is calculated for each experimental run. In this study, an attempt has been made to assess the potential of machine learning algorithms to predict the TLS of the joint. It was found that the support vector machine (SVM) model with RBF kernel was the most effective for predicting the TLS. Furthermore, FSW process parameters are optimized by means of the desirability approach. The optimal set to attain maximum TLS is identified as the tilt angle of 2°, welding speed of 5.12 mm/min and tool rotational speed of 1185.92 r/min. Finally, a confirmation test was performed to validate the optimal set and the adequacy of the developed SVM model. From the confirmation test, the error percentage between experimental and prediction values is less than 5%. Metallographic analysis revealed that the joining mechanism is the macro and micromechanical interlocking assisted by chemical bonding.

show abstract

“…Optimization is the process of making the process better by using the input parameters to get the best output values. Heuristic Algorithms (HA) are basically a trial-and-error method, intended to solve a problem quicker and more effectively than traditional approaches [14,54]. Genetic algorithms (GA), simulated annealing (SA), ant colonies, tabu search, and so on are some of the heuristic optimization algorithms.…”

Section: Optimizationmentioning

confidence: 99%

“…A GA is a meta-heuristic algorithm, search-based algorithm, and optimization technique [54,55]. GA is based on ideas about the genetic processes of biological organisms [55].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Tribological performance evaluation of h-BN nanoparticle reinforced AA 7075 and as-cast AA7075 using Taguchi and genetic algorithm

Beyanagari

Kandasamy

2021

Surf. Topogr.: Metrol. Prop.

View full text Add to dashboard Cite

In addition to the properties of reinforcement, the tribological parameters (temperature, applied load, and sliding distance) play a vital role in determining the tribological characteristics of the composites. Though there are studies related to the AA7075/h-BN composites, the influence of tribological parameters on the friction and wear characteristics is not addressed. In this regard, a solemn attempt has been made in this research work to study the role of tribological parameters on the tribological characteristics of AA7075/h-BN composites. Taguchi method and genetic algorithm are employed to predict the impact of tribological parameters on the wear resistance of AA7075 and AA7075/h-BN composites. The formulation of genetic algorithm models along with regression analysis is used for estimating the effect of each input parameter on the tribological behaviour of the AA7075 MMCs. The microstructure of AA7075/h-BN composite is analysed and found that the formation of rosette-like dendrites with increased mechanical and tribological properties. The optimum wear rate and Coefficient of Friction (CoF) values of AA 7075/h-BN composite is obtained with input process parameters at a temperature of 30 °C, applied load 10 N, and sliding distance of 1000 m respectively. The further results show that the temperature and load play a vital role in the specific wear rate and coefficient of friction of AA7075 MMCs, leading to improved tribological performance characteristics.

show abstract

Multi-Performance Optimization in Friction Stir Welding of AA6082/B4C Using Genetic Algorithm and Desirability Function Approach for Aircraft Wing Structures

Cited by 8 publications

References 33 publications

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

Tribological performance evaluation of h-BN nanoparticle reinforced AA 7075 and as-cast AA7075 using Taguchi and genetic algorithm

Contact Info

Product

Resources

About