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Journal ArticleDOI

Genetic Algorithm and its Applications to Mechanical Engineering: A Review

01 Jan 2015-Materials Today: Proceedings (Elsevier)-Vol. 2, pp 2624-2630

TL;DR: Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
Abstract: Genetic Algorithm is optimization method based on the mechanics of natural genetics and natural selection. Genetic Algorithm mimics the principle of natural genetics and natural selection to constitute search and optimization procedures.GA is used for scheduling to find the near to optimum solution in short time. In a genetic algorithm representation is done with variable length of sub-chromosome.GA is developed to generate the optimal order scheduling solution. GA is used as tool in different processes to optimize the process parameters. This paper reviews the genetic algorithms that are designed for solving multiple problems in applications of material science and manufacturing in field of mechanical engineering. Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
Topics: Meta-optimization (70%), Genetic representation (66%), Genetic operator (65%), Genetic algorithm (65%), Cultural algorithm (64%)
Citations
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Journal ArticleDOI
Bong Seong Oh1, Junhyun Cho1, Bongsu Choi1, Hong Wone Choi2  +2 moreInstitutions (2)
Abstract: The design parameters of heat pumps are related to each other nonlinearly or in a complicated manner; therefore, it is difficult to determine the optimal combination of design parameters, such as superheat, subcooling, and refrigerant type, analytically. To address this limitation, three representative heuristic algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA), are applied to optimize a heat pump under the given process conditions. Heuristic algorithms are driven based on randomness; thus, the consistency of the calculation results and computational time represent the decision criteria for the appropriate optimizer. The GA is unsuitable as a heat pump optimizer because it requires an excessive number of iterations. In contrast, PSO and SA have a similar capability of consistency and calculation time with a rational number of iterations. In conclusion, PSO exhibits a slightly better consistency and use of computational resources; therefore, PSO is selected as the heat pump design optimization algorithm in this study. The novelty of this work lies in that the related design parameters of the heat pump are simultaneously globally optimized with minimal physical background, and the heuristic algorithm that is most applicable to heat pump design optimization is determined.

Journal ArticleDOI
Qicheng Ding1, Wei Wang1, Jiexiong Ding1, Jing Zhang1  +4 moreInstitutions (2)
Abstract: In the five-axis CNC machining process, the dynamic tracking error due to servo dynamic performance deficiency is a main cause of processing inaccuracy during precise high-speed machining. The rotation tool center point (RTCP) test is commonly used to measure the dynamic performance of five-axis machine tools. The key to the capability of the RTCP test is axis motion planning in the test process. However, the axis motion plans for RTCP tests are usually based on simple motion instruction or engineering experience; the mechanism of the discrepancy between different axis motion plans is unclear. In this study, the axis motion planning process for RTCP dynamic performance tests is analyzed, and a novel axis motion planning method is proposed. The axis motion planning process is directly connected to the mechanism of dynamic tracking error; error observability is used as the index to guide RTCP axis motion planning. A modified genetic algorithm is used to select the sensitive rotary axis position and velocity combos; cubic spline interpolation is used to plan the axis motions based on the sensitive position and velocity combos.

Journal ArticleDOI
Yan Lin1, Xing'ang Xu1, Chao Ye1Institutions (1)
TL;DR: A weighted impulse (WI) index is proposed to evaluate the performance of the adaptive general scale transformation SR (AGSTSR) in rotating machinery fault diagnosis and an intelligent optimization algorithm is used to obtain the optimal WI.
Abstract: Stochastic resonance (SR) provides the enhancement of fault characteristic signals with the assistance of noise. The performance of adaptive SR must be evaluated using an appropriate index to automatically enhance various characteristic signals. This paper proposes a weighted impulse (WI) index to evaluate the performance of the adaptive general scale transformation SR (AGSTSR) in rotating machinery fault diagnosis and uses an intelligent optimization algorithm to obtain the optimal WI. The comparison results in the simulation experiment revealed that the proposed WI-based AGSTSR method presented the fault characteristics more clearly than the adaptive SR method based on the impulse index or weighted kurtosis index. Moreover, the proposed method offered the best anti-noise performance in the simulation experiment. Finally, two experimental case studies validated that the proposed method can adaptively realize early fault diagnosis of rotating machinery through the analysis of weak fault characteristics.

Journal ArticleDOI
Abstract: A multidisciplinary design procedure was presented to fabricate hybrid composite sandwich (HCS) panels used in radomes based on conducting several mechanical and electromagnetic experiments. Accordingly, six specimens made of E-glass/aramid/polyester face sheets with different stacking sequences and polyvinyl chloride foam core were prepared using the vacuum-assisted resin transfer molding process. Afterward, three-point bending and low-velocity impact tests were performed and several mechanical characteristics were reported for specimens. Besides, electromagnetic wave transmission performance of the HCSs was experimentally and numerically determined. The accuracy of the simulated results was confirmed by free-space measurement method. The most efficient design of the hybrid stacking sequence was subsequently found by implementing the complex proportional assessment method and considering the mechanical and electromagnetic characteristics together with the moisture resistance of the specimens. Eventually, a new embedded frequency-selective surface (FSS) was designed for the radome using a genetic algorithm to maintain the transmission rate in the desired band at the order of higher than 90 percent. The comparison of the new FSS performance with conventional FSSs in the literature exhibited that the present optimization procedure not only achieves the maximum transmission at the resonance frequency but also enhances the transmission loss outside of the desired frequency band.

1 citations


Journal ArticleDOI
20 Sep 2021-Energies
TL;DR: A comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities are provided.
Abstract: Electric power reliability is one of the most important factors in the social and economic evolution of a smart city, whereas the key factors to make a city smart are smart energy sources and intelligent electricity networks. The development of cost-effective microgrids with the added functionality of energy storage and backup generation plans has resulted from the combined impact of high energy demands from consumers and environmental concerns, which push for minimizing the energy imbalance, reducing energy losses and CO2 emissions, and improving the overall security and reliability of a power system. It is now possible to tackle the problem of growing consumer load by utilizing the recent developments in modern types of renewable energy resources (RES) and current technology. These energy alternatives do not emit greenhouse gases (GHG) like fossil fuels do, and so help to mitigate climate change. They also have in socioeconomic advantages due to long-term sustainability. Variability and intermittency are the main drawbacks of renewable energy resources (RES), which affect the consistency of electric supply. Thus, utilizing multiple optimization approaches, the energy management system determines the optimum solution for renewable energy resources (RES) and transfers it to the microgrid. Microgrids maintain the continuity of power delivery, according to the energy management system settings. In a microgrid, an energy management system (EMS) is used to decrease the system’s expenses and adverse consequences. As a result, a variety of strategies and approaches are employed in the development of an efficient energy management system. This article is intended to provide a comprehensive overview of a range of technologies and techniques, and their solutions, for managing the drawbacks of renewable energy supplies, such as variability and load fluctuations, while still matching energy demands for their integration in the microgrids of smart cities.

References
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01 Jan 2014
Abstract: Within the last three decades, a solid and real amount of research efforts has been directed at the application of soft computing (SC) techniques in engineering. This paper provides a systematic review of the literature originating from these efforts which focus on materials engineering (ME) particularly sheet metals. The primary aim is to provide background information, motivation for application and an exposition to the methodologies employed in the development of soft computing technologies in engineering. Our review shows that all the works on the application of SC to sheet metal have reported excellent, good, positive or at least encouraging results. Our appraisal of the literature suggest that the interface between material engineering and intellectual systems engineering techniques, such as soft computing, is still blur. The need to formalize the computational and intelligent system engineering methodology used in sheet material, therefore, arises. We also provide a brief exposition to our on-going efforts in this direction. Although our study focuses on materials engineering in particular, we think that our findings applies to other areas of engineering as well.

2 citations


Journal ArticleDOI
Abstract: This paper aims to improve the global optimization operation efficiency in engineering by establishing the Kriging model to simplify the calculated mathematics model. In the case of the large ball mill, this paper presents in detail the application of the stress–strength distribution interference theory to calculate the reliability of gear transmission, establishes the Kriging model for function fitting, and uses genetic algorithm to globally optimize the volume and reliability of large ball mill gear transmission. The optimal result based the Kriging model is contrasted with the Monte Carlo Method in terms of calculation accuracy, greatly improving the efficiency of calculation.

43 citations


Journal ArticleDOI
Abstract: Perforated plate heat exchangers (PPHEs) come under the category of compact heat exchanger; offering high effectiveness, large surface area per unit volume (as high as 6000 m2/m3) and better flow characteristics. PPHEs are constructed of alternately arranged perforated plates and spacers. Heat exchanging fluids flow through the holes of the plates and exchange heat by conduction through the plate material. Spacers help minimizing axial conduction and reheadering of fluids intermittently. Design of a compact heat exchanger is targeted for high effectiveness, low volume and minimum pressure drop. Performance of a PPHE depends on many design variables such as plate thickness, spacer thickness, pore diameter, porosity etc. For a given heat duty, these parameters can be optimized for maximizing effectiveness, minimizing volume and minimizing or limiting pressure drop.In this paper an attempt has been made for optimization of the design variables of a PPHE so that effectiveness of the heat exchanger per unit volume is maximized under the constraints of fluid pressure drop and length of the heat exchanger. Unlike the conventional approach, importance is given to the length of the heat exchanger which is limited to the available space inside the vacuum chamber of the diffusion bounding machine or the space available in a specific application. Using the given length of the heat exchanger and allowable pressure drop, the problem has been defined in unconstrained form and solved by Genetic algorithm.

4 citations


Dissertation
01 Jan 2013
Abstract: Productivity and quality are two important aspects have become great concerns in today’s competitive global market. Every production/manufacturing unit mainly focuses on these areas in relation to the process as well as product developed. Electrical discharge machining (EDM) process, even now it is an experience process, wherein still the selected parameters are often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. Material Removal Rate (MRR) during the process has been considered as productivity estimate with the aim to maximize it. With an intention of minimizing surface roughness is been taken as most important output parameter. These two opposite in nature requirements have been simultaneously satisfied by selecting an optimal process environment (optimal parameter setting). Objective function is obtained by Regression Analysis and Analysis of Variance. Then objective function is optimized using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness improved using optimized machining parameters.

1 citations


Journal ArticleDOI
Doriana M. D’Addona1, Roberto Teti1Institutions (1)
Abstract: An optimization paradigm based on genetic algorithms (GA) for the determination of the cutting parameters in machining operations is proposed. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. In order to find optimal cutting parameters during a turning process, the genetic algorithm has been used as an optimal solution finder. Process optimization has to yield minimum production time, while considering technological and material constrains.

99 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
202123
20207
201910
201810
20173
20162