Hybrid approach for genetic algorithm and Taguchi's method based design optimization in the automotive industry

Karen, İdris; Yıldız, Ali Rıza; Kaya, Necmettin; Öztürk, Nursel; Öztürk, Ferruh

doi:10.1080/00207540600619932

Cited by 82 publications

(15 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead of static loading, this paper takes cyclic loading on the torque arm into account (which is more representative of the actual loading conditions), along with the static failure criteria, and performs shape optimization of the torque arm under fatigue life and stress constraints. Note that the use of fatigue life in design optimization of automotive parts is considered in many studies, including Karen et al 45 and Yildiz and Lekesiz. 46 The initial geometry of the torque arm, along with the loading and boundary conditions corresponding to the actual working conditions, is shown in Figure 1.…”

Section: Problem Definitionmentioning

confidence: 99%

Design optimization of an automobile torque arm using global and successive surrogate modeling approaches

Acar

Tüten

Güler

2018

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

The design of lightweight automotive structures has become a prevalent practice in the automotive industry. This study focuses on design optimization of an automobile torque arm subjected to cyclic loading. Starting from an available initial design, the shape of the torque arm is optimized for minimum weight such that the fatigue life of the torque arm does not fall below that of the initial design and the maximum von Mises stress developed in the torque arm does not exceed that of the initial design. The stresses are computed using ANSYS finite element software, and the fatigue life is calculated using the Smith–Watson–Topper model. Surrogate-based optimization approach is used to reduce the computational cost. Optimization results based on global surrogate modeling and successive surrogate modeling approaches are compared. It is found that the successive surrogate modeling approach results in 28.7% weight reduction for the torque arm, whereas the global surrogate modeling approach results in 25.7% weight saving for the torque arm.

show abstract

Section: Problem Definitionmentioning

confidence: 99%

Design optimization of an automobile torque arm using global and successive surrogate modeling approaches

Acar

Tüten

Güler

2018

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…The nesting process is completed by an Artificial Intelligent (AI) algorithm which generates effective parts layout that includes necessary allowances to execute subsequent cutting process (Anand and Babu 2015). There are several AI algorithms such as Genetic Algorithm (GA), Simulated Annealing algorithm, Particle Swarm Optimization, Gravitational search algorithm, Cuckoo search algorithm etc., are used in literature for different engineering applications (Karen et al 2006;Kiani and Yildiz 2016;Yıldız et al 2016a, b;Yildiz and Saitou 2011;Yıldız and Lekesiz 2017). Detailed studies have been carried out to compare the performance of above mentioned AI algorithms (Karagöz and Yıldız 2017;Yildiz 2013).…”

Section: Introductionmentioning

confidence: 99%

Design of adaptable pin configuration machine bed optimized with genetic approach for sheet metal cutting process

Anand¹,

Udhayakumar

2017

J Intell Manuf

View full text Add to dashboard Cite

This paper proposes a novel design of machine bed used in laser, plasma and abrasive water jet (AWJ) cutting machines. During sheet metal cutting process, the laser/plasma beam pierces the sheet and further causes damage to the support bed. In contrast to the existing industrial practice of using fixed type support bed, the proposed adjustable pin type design bed adaptively provides support to the sheet by considering the parts layout that is being cut. In this design, the bed is formed with adjustable slats, in which the pins are inserted into the holes of the slats. By combining the dimensional data of machine bed and parts layout, an effective pin configuration is generated. The slats and position of the pins are represented in terms of genetic strings. The near optimal pin configuration is generated through a customized genetic algorithm. The objective is to minimize the damage caused by the tool to the bed and also to provide effective support to the different geometrical parts based on its centroid location. The effectiveness of the proposed approach is tested by combining the data of the bed and its different parts of the layout with irregular geometries. The results are promising and the uniqueness of the proposed approach is illustrated with different test cases.

show abstract

“…Over the previous few decades, to rise above the requirement of derivative information, a variety of nature inspired algorithms and their hybridized/embedded versions have been developed, for example, differential evolution (DE) (Das & Suganthan, 2010; Hamza, Abderazek, Lakhdar, Ferhat, & Yıldız, 2018; Kiani & Yildiz, 2016; Pholdee, Bureerat, & Yıldız, 2017; Yildiz, 2013; Zhang, Luo, & Wang, 2008), genetic algorithms (GA) (Karen, Yildiz, Kaya, Öztürk, & Öztürk, 2006; Kiani & Yildiz, 2016), particle swarm optimization (PSO)(Eberhart & Kennedy, 1995; Shi & Eberhart, 1998; Sun, Feng, & Xu, 2004; Xu & Sun, 2005; dos Santos Coelho, 2010; Kiani & Yildiz, 2016; Yildiz et al, 2019, Kumar et al, 2020a,b), gravitational search algorithm (GSA) (Karagöz & Yıldız, 2017; Rashedi, Nezamabadi‐Pour, & Saryazdi, 2009), grey wolf optimization (GWO) (Mirjalili, Mirjalili, & Lewis, 2014; Yildiz & Yildiz, 2018; Yildiz et al, 2019; Abderazek, Yildiz, & Mirjalili, 2020), harmony search (HS) (Yildiz & Öztürk, 2010), ant lion optimizer (ALO) (Mirjalili, 2015a; Yildiz et al, 2019; Abderazek et al, 2020), moth flame optimization algorithm (Mirjalili, 2015b, Yıldız & Yıldız, 2017; Yildiz et al, 2019; Abderazek et al, 2020; Yıldız, 2020a), multi verse optimizer (Abderazek et al, 2020), salp swarm algorithm (Yıldız & Yıldız, 2019; Yildiz et al, 2019; Abderazek et al, 2020), mine blast algorithm (Yildiz et al, 2019; Abderazek et al, 2020; Yıldız, 2020c), harris hawks optimization algorithm (Yıldız et al, 2019; Yıldız & Yıldız, 2019; Kurtuluş, Yıldız, Sait, & Bureerat, 2020), butterfly optimization algorithm (Yıldız et al, 2020a), henry gas solubility optimization algorithm (Yıldız et al, 2020b), grasshopper optimization algorithm (Mirjalili, Mirjalili, Saremi, Faris, & Aljarah, 2018; Yıldız & Yıldız, 2019), dragonfly algorithm (Yıldız & Yıldız, 2019), artificial bee colony algorithm (Yildiz et al, 2019), whale optimization algorithm (Yildiz & Yildiz, 2018; Yildiz et al, 2019; Yildiz,…”

Section: Introductionmentioning

confidence: 99%

Development of some techniques for solving system of linear and nonlinear equations via hybrid algorithm

et al. 2021

View full text Add to dashboard Cite

The objective of this article is to introduce several new methods or techniques for solving simultaneous linear and nonlinear system of equations with the help of a new hybrid algorithm based on advanced quantum behaved particle swarm optimization and the concept of binary tournamenting process. Depending on different options of binary tournamenting, six different variants of hybrid algorithms are proposed. To examine the effectiveness of the proposed hybrid algorithms five well known benchmark bound‐constrained optimization problems are solved. Among the six different variants of hybrid algorithms, the best algorithm is selected on the basis of their performances in these problems. This best algorithm is then applied in solving simultaneous linear and nonlinear system of equations transforming these equations into optimization problems. In case of linear system, just two systems are solved while in case of nonlinear system seven complicated problems are solved and finally a comparison of best found solutions are estimated with the same of existing algorithms provided in the literature.

show abstract

Hybrid approach for genetic algorithm and Taguchi's method based design optimization in the automotive industry

Cited by 82 publications

References 36 publications

Design optimization of an automobile torque arm using global and successive surrogate modeling approaches

Design optimization of an automobile torque arm using global and successive surrogate modeling approaches

Design of adaptable pin configuration machine bed optimized with genetic approach for sheet metal cutting process

Development of some techniques for solving system of linear and nonlinear equations via hybrid algorithm

Contact Info

Product

Resources

About