Optimization of Railway Bogie Snubber Spring with Grasshopper Algorithm

Neve, Abhishek G; Kakandikar, Ganesh M.; Kulkarni, Omkar; Nandedkar, Vilas M.

doi:10.1007/978-981-15-1097-7_80

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…The substitute model was utilized to optimize the performance of the micro-spring, resulting in an 80% improvement in performance. Neve, Abhishek G. et al [15], the grasshopper enhancement process is utilized to improve the design of the snubber spring (through CATIA V5), which is then assessed using ANSYS 17.0. This approach simulates the natural behavior of grasshoppers and models the solution to an optimization issue.…”

Section: Introductionmentioning

confidence: 99%

Development of a computational system to design a helical spring

Kamil,

Abdulshaheed,

Kadhom

2024

IRASE

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Springs are the most basic mechanical elements used in transmission mechanisms. The rapid development of the computer and cellular industry has encouraged spring manufacturers to develop the industry to produce very small springs. Most computer-aided design programs for mechanical parts provide the possibility of designing these parts, as these programs include different types of decisions. All these decisions require coordinates for geometric data as well as metadata. The paper aims to develop software programs to design and analyze springs as one of the most significant mechanical elements used. This paper aims to develop a design software of a helical spring system, where this software is built using a computer program in the language of Visual Basic Version 5. When the user enters data into the system, the system will perform a series of complex calculations in the system, then provide a detailed report on all the engineering dimensions of the spring, and test its efficiency. The output of the software shows the required spring wire parameters. The software was tested with test data from the open literature, and the required wire spring parameters were obtained.

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Section: Introductionmentioning

confidence: 99%

Development of a computational system to design a helical spring

Kamil,

Abdulshaheed,

Kadhom

2024

IRASE

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“…They included the Atom Search Optimization (ASO) [5], Beetle Antennae Search (BAS) [5], Sunflower Optimization (SFO) [7], Mayfly Algorithm (MA) [8], Parasitism Predation Algorithm (PPA) [9], Slime Mould Algorithm (SMA) [10] and Tunicate Swarm Algorithm (TSA) [11]. In cuckoo algorithm [12] study is performed on egg laying pattern of cuckoo bird, in particle swarm optimization [13] research on swarm behaviour is observed, also behavioural pattern of firefly [14], grasshopper [15][16][17][18], salp swarm [19,20] and bat [21] is studied.…”

Section: Introductionmentioning

confidence: 99%

Mayfly optimization algorithm: a review

Bogar,

Shirodkar,

Kulkarni

et al. 2024

J. mechatron., artif. intell., eng.

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This paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on .One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future.

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“…An optimization problem is proposed and optimized using the bat optimization algorithm. Nowadays, there are many evolutionary and Bat inspired optimization algorithms available, such as Grey Wolf Optimizer (GWO) [2], Particle Swarm Optimization (PSO) [3], Ant Colony Optimization (ACO) [4], Grasshopper Optimization Algorithm (GOA) [5], Firefly Algorithm (FA) [6], Salp Swarm Algorithm (SSA) [7], Cuckoo Search Algorithm (CS) [8], Genetic Algorithm (GA) [9], etc. Socially inspired algorithms such as Cohort Intelligence (CI) [10] and ideology algorithm [11] are also algorithms that help find the best parameters for the problem.…”

Section: Introductionmentioning

confidence: 99%

Process parameters optimization by bat inspired algorithm of CNC turning on EN8 steel for prediction of surface roughness

Burande¹,

Kulkarni²,

Jawade³

et al. 2021

J. mechatron., artif. intell., eng.

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Due to its high precision, productivity, and surface quality, computer numerical control turning (CNC) is a desirable processing tool in the traditional processing area. CNC machining procedures have a huge number of process parameters, making it challenging to find the best combination of parameters for increased accuracy. In this research work, the Taguchi method and ANOVA were used to study the effects of CNC machining parameters in EN8 steel turning: Surface roughness (Ra) value of component affected due to cutting speed, depth of cut and feed rate. Three-level three-parameter experimental design, using Minitab 17 software using L9 orthogonal array, using coated carbide insert cutting tools, using signal-to-noise ratio (S/N) to study the performance characteristics of EN8 steel turning. In this study, statistical approaches such as the signal-to-noise ratio (S/N ratio) and analysis of variance (ANOVA) were used to explore the effects of cutting speed, depth of cut, and feed rate on surface roughness. Natureinspired algorithms play a vital role in solving real life. In this study, the bat algorithm can be used to predict the optimal surface value (Ra) and process parameters. Verify the results by conducting confirmation experiments. The current research shows that the feed rate is the most important factor affecting the surface roughness (Ra) of EN8 steel turning.

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Optimization of Railway Bogie Snubber Spring with Grasshopper Algorithm

Cited by 7 publications

References 17 publications

Development of a computational system to design a helical spring

Development of a computational system to design a helical spring

Mayfly optimization algorithm: a review

Process parameters optimization by bat inspired algorithm of CNC turning on EN8 steel for prediction of surface roughness

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