Optimization of Machining Parameters for Milling Operations Using Non-conventional Methods

Baskar, N.; Asokan, P.; Prabhaharan, G.; Saravanan, R.

doi:10.1007/s00170-003-1939-9

Cited by 94 publications

(40 citation statements)

References 14 publications

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“…Baskar et al [22] compared the performance of four non-conventional methods: Ant Colony Algorithm, GA, PSO and Tabu Search. These methods were applied to determine the optimal process parameters in order to optimize time, cost and profit rates.…”

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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show abstract

“…A genetic algorithm along with the Taguchi method was used to optimize the parameters of submerged arc welding in the hard facing process (Patnaik et al 2007). Recently, optimization of machining parameters for the milling operation and optimization of friction welding parameters was carried out using a particle swarm optimization algorithm (Baskar et al 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

Multi Objective Optimization of Flux Cored Arc Weld Parameters Using Hybrid Grey - Fuzzy Technique

Satheesh

Dhas

2014

Jou. Eng. Res.

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Abstract:In the present work, an attempt has been made to use the grey-based fuzzy logic method to solve correlated multiple response optimization problems in the field of flux cored arc welding. This approach converts the complex multiple objectives into a single grey-fuzzy reasoning grade. Based on the grey-fuzzy reasoning grade, optimum parameters are identified. The significant contributions of parameters are estimated using analysis of variance (ANOVA). This evaluation procedure can be used in intelligent decision making for a welding operator. The proposed and developed method has good accuracy and competency. The proposed technique provides manufacturers who develop intelligent manufacturing systems a method to facilitate the achievement of the highest level of automation.

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“…Saravanan R Baskar N., Asokan P., and Prabhaharan, G. [7] showed significant improvement in conventional milling process optimization by using various non-conventional optimization techniques such as PSO & They compared their ISSN: 2231-5381 http://www.ijettjournal.org…”

Section: Issn: 2231-5381mentioning

confidence: 99%

Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

Bahirje¹,

Potdar²

2014

IJETT

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Abstract-a review of publications associated with the optimization of milling conditions by particle swarm analysis method. Milling is the most common form of machining, a material removal process, which can create a variety of features on a part by cutting away the unwanted material In order to optimize the cutting conditions, the empirical relationships between input and output variables should be established in order to predict the output. Optimization of these predictive models helps us to select appropriate input variables for achieving the best output performance. In this review paper the study is covered regarding the optimization of different input parameters and results are analyzed.

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Optimization of Machining Parameters for Milling Operations Using Non-conventional Methods

Cited by 94 publications

References 14 publications

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Multi Objective Optimization for Sustainable Manufacturing, Application in Turning

Multi Objective Optimization of Flux Cored Arc Weld Parameters Using Hybrid Grey - Fuzzy Technique

Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

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