A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations

Saravanan, R.; Asokan, P.; Sachidanandam, Meikandasivam

doi:10.1016/s0890-6955(02)00074-3

Cited by 118 publications

(53 citation statements)

References 7 publications

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“…Cus and Balic [18], and Saravanan et al [19], among others, proposed approaches based on GA to solve multi-objective optimization problems for machining processes while Liu and Wang used a modified genetic algorithm for the optimization of the cutting parameters in milling [20].…”

Section: Multi Objective Optimizationmentioning

confidence: 99%

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Hassine¹,

Barkallah²,

Bellacicco³

2015

Int. j. simul. model.

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As manufacturing converts raw materials into products, environmental wastes and emissions are simultaneously generated from the consumption of materials and energy during the manufacturing processes. Then, sustainable manufacturing is defined as the creation of manufactured products using processes that minimize negative environmental impacts, conserve energy and natural resources and that are safe on employees, communities and consumers. Such an approach requires a compromise between ecological and economic aspects to meet the pillars of sustainable development.This paper presents the implementation of particle swarm tool in order to solve multi-objective optimization for sustainable manufacturing. Hence, this study might serve as part of a global approach to model sustainable manufacturing. The main objective of this approach is to develop operations that allow production with respect of ecological, economic and technological constraints. We developed a case study on the cutting conditions during turning at the end of our study.

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Section: Multi Objective Optimizationmentioning

confidence: 99%

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Hassine¹,

Barkallah²,

Bellacicco³

2015

Int. j. simul. model.

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“…(Mukherjee and Ray 2006;Yusup et al 2012;Wen et al 1992;Rowe et al 1994;Saravanan et al 2002;Dhavalikar et al 2003;Mitra and Gopinath 2004;Baskar et al 2004;Krishna 2007;Pawar et al 2010;Rao and Pawar 2010). Now, NSTLBO algorithm is applied to solve the mutiobjective optimization problem in surface grinding process.…”

Section: Optimization Of Process Parameters Of Surface Grinding Processmentioning

confidence: 99%

“…The same problem was solved by Saravanan et al (2002) using GA. They used 2000 (i.e., a population size of 20 and no.…”

Section: Optimization Of Process Parameters Of Rough Grinding Processmentioning

confidence: 99%

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

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Multiobjective optimization aspects of a traditional machining process namely surface grinding and five modern machining processes namely, wire-electro discharge machining process, micro-wire-electric discharge machining process, laser cutting process, electrochemical machining process, and electrochemical discharge machining process are presented in this chapter. A posteriori multiobjective optimization algorithm named as nondominated sorting teaching-learning-based optimization (NSTLBO) algorithm is proposed to solve the multiobjective optimization problems of the machining processes. A Pareto optimal set of solutions along with a Pareto front is presented for each of the considered machining processes.

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“…Cai et al [3] described the structure, content, and relations employed in an intelligent grinding database developed to provide selective and/or optimal data to the operator. Saravanan et al also [4] described a GA based optimization procedure to optimize grinding conditions (wheel speed, workpiece speed, depth of dressing, and lead of dressing) using a multi-objective function model with a weighted approach for surface grinding. Out-of-roundness is a complex error resulting from grinding process.…”

Section: Introductionmentioning

confidence: 99%

Modeling and experimental analysis of wheel-work interface in the cylindrical plunge grinding process

Tawfeek

2014

IJET

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This paper presents a study of grinding wheel-workpiece interference in external cylindrical plunge grinding processes. This is to study the effect of workpiece surface memory on the workpiece roundness error after grinding. The study has been carried out theoretically on a model simulating cylindrical grinding process. The model takes contact stiffness, grinding wheel and workpiece wear into consideration. The proposed model was sued to predict the normal grinding forces in cylindrical grinding as a function of the previous height and number of waves of the initial profile. The new model has been validated by conducting experiments on a cylindrical grinding machine. Results indicate that the proposed model shows a good agreement with the experimental data obtained. The results of experiments indicate that the proposed modeling method is both feasible and reliable. The results showed that the theoretical model was effective studying the output of cylindrical grinding process. Normal grinding force, vibration level, and roundness error in cylindrical plunge grinding processes are dependent on the workpiece surface memory

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A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations

Cited by 118 publications

References 7 publications

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

Modeling and experimental analysis of wheel-work interface in the cylindrical plunge grinding process

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