Multi-objective structural optimization of hub unit bearing using response surface methodology and genetic algorithm

Lü, Xingcai; Xie, Xue‐Jun

doi:10.1299/jamdsm.2014jamdsm0035

Cited by 4 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Finite Element Model is one of the commonly used and mature optimization models, which is efficient and less loss [Lu and Xie, 2014]. According to the design requirements, the analysis model of the FRS was established.…”

Section: Pre-stressed Modal Analysismentioning

confidence: 99%

Dynamic analysis and structure optimization of a floating ring system in dry gas seal

Liu¹,

Liu²,

Yin³

et al. 2018

JAMDSM

View full text Add to dashboard Cite

Section: Pre-stressed Modal Analysismentioning

confidence: 99%

Dynamic analysis and structure optimization of a floating ring system in dry gas seal

Liu¹,

Liu²,

Yin³

et al. 2018

JAMDSM

View full text Add to dashboard Cite

“…Thus, to effectively solve a problem of two or more objectives is of practical importance. Some studies show the results of recent effort in developing efficient schemes (Wang and Chang, 2004;Hirani and Suh, 2005;Wang, 2005;Bhat and Barrans, 2008;Wang and Cha, 2010;Lu and Xie, 2014) for solving multiobjective optimization problems (MOOPs). To minimize the execution time many of these optimization analyses were carried out by using parallel computing or approximating the computationally intensive function with a surrogate model (Li, et al, 2008;Srirat, et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Multiobjective design of porous air bearing using group inching fortification method

Wang

Chen

2015

JAMDSM

View full text Add to dashboard Cite

This study presents a new approach, group inching fortification (GIF) method, to deal with multiobjective optimization problems found in various mechanical designs. The GIF method is exemplified by a four-factor porous air bearing design. In the GIF method the initial group of designs in Pareto rank 1 is used as the basis to inch up the formation of Pareto solution set, which is fortified over the search process by uniting superior or non-dominated solutions from base-point exploration moves. In this study, a comparison of the GIF method with genetic algorithm (GA) and hyper-cube dividing method (HDM) for the same air bearing design is presented. The results show that the Pareto solution set obtained by the GIF method has more design selections with a wider coverage (breadth). Equally important, the number of objective-function calls required (179, 736, and 3600 for GIF, HDM, and GA, respectively) in the GIF method is significantly reduced. In this study, the GIF method is suggested to be terminated when all the designs are non-dominated by each other, a criterion which is very difficult to achieve by using the GA and HDM. This study proposes an effective design tool which is easy-to-implement for solving the problems with multiple objectives.

show abstract

Multi-objective optimization of turning process for hardened material based on hybrid approach

Park

Nguyen

2016

JAMDSM

View full text Add to dashboard Cite

Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. This paper systematically investigates the turning process of the hardened material via process modeling, numerical experiments, and a hybrid algorithm. The objectives of this work are to reduce the specific cutting energy (SCE) and improve the energy efficiency (EF) based on the turning conditions optimization. The machining simulations were performed in conjunction response surface methodology (RSM) to generate the quadratic mathematical models of the specific cutting energy and energy efficiency in terms of machining parameters, including cutting speed, feed rate, nose radius, edge radius, and rake angle. An analysis of variance (ANOVA) was then adopted to examine the model adequacy and significant parameters. Subsequently, an evolutionary algorithm, namely non-dominated sorting genetic algorithm-II (NSGA-II) was used to find a much better spread of design solutions and better convergence near the true Pareto optimal front. A quantitative approach, namely entropy method was conducted to calculate the weight factors of multiple responses. In the last step, a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was applied as to determine the best compromise solution. It was indicated that the energy efficiency was significantly improved using the optimal machining parameters and the specific cutting energy was effectively decreased in comparison with initial values. Moreover, the integrative approach performed very well in optimum performance of the machining process. Therefore, this work is expected as a contribution to improve the machining efficiency of the turning process of hardened steels.

show abstract

Multi-objective structural optimization of hub unit bearing using response surface methodology and genetic algorithm

Cited by 4 publications

References 13 publications

Dynamic analysis and structure optimization of a floating ring system in dry gas seal

Dynamic analysis and structure optimization of a floating ring system in dry gas seal

Multiobjective design of porous air bearing using group inching fortification method

Multi-objective optimization of turning process for hardened material based on hybrid approach

Contact Info

Product

Resources

About