Application of genetic algorithms—determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile

Amiolemhen, P.E.; Ibhadode, Akii Okonigbon Akaehomen

doi:10.1016/j.ijmachtools.2004.02.001

Cited by 46 publications

(25 citation statements)

References 8 publications

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“…GA is nowadays widely used to solve optimization problems in different research fields, including machining [19][20][21]. In this paper, the simple genetic algorithm based on least mean square (LMS) curve fitting [22] is implemented.…”

Section: Fitting Methodsmentioning

confidence: 99%

Speed-varying cutting force coefficient identification in milling

Grossi

Sallese

Scippa

et al. 2015

Precision Engineering

104

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a b s t r a c tAccurate simulation of the machining process is crucial to improve milling performance, especially in High-Speed Milling, where cutting parameters are pushed to the limit.Various milling critical issues can be analyzed based on accurate prediction of cutting forces, such as chatter stability, dimensional error and surface finish. Cutting force models are based on coefficients that could change with spindle speed. The evaluation of these specific coefficients at higher speed is challenging due to the frequency bandwidth of commercial force sensors. On account of this, coefficients are generally evaluated at low speed and then employed in models for different spindle speeds, possibly reducing accuracy of results.In this paper a deep investigation of cutting force coefficient at different spindle speeds has been carried out, analyzing a wide range of spindle speeds: to overcome transducer dynamics issues, dynamometer signals have been compensated thanks to an improved technique based on Kalman filter estimator. Two different coefficients identification methods have been implemented: the traditional average force method and a proposed instantaneous method based on genetic algorithm and capable of estimating cutting coefficients and tool run-out at the same time.Results show that instantaneous method is more accurate and efficient compared to the average one. On the other hand, the average method does not require compensation since it is based on average signals. Furthermore a significant change of coefficients over spindle speed is highlighted, suggesting that speed-varying coefficient should be useful to improve reliability of simulated forces.

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Section: Fitting Methodsmentioning

confidence: 99%

Speed-varying cutting force coefficient identification in milling

Grossi

Sallese

Scippa

et al. 2015

Precision Engineering

104

View full text Add to dashboard Cite

show abstract

“…Wang (1993Wang ( , 2003 suggested a neural network based approach to optimize cutting parameters in multi-objective optimization. Amiolemhen et al (2004) proposed a methodology based on genetic algorithms to determine the cutting parameters in multi-pass machining operations by simultaneously considering single-pass finishing and multi-pass roughing operations. Quiza et al (2006) proposed a multi-objective optimization methodology to optimize the cutting parameters in turning processes using genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Hybrid evolutionary multi-objective optimization and analysis of machining operations

Deb

Datta

2012

Engineering Optimization

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Evolutionary multi-objective optimization (EMO) has received significant attention in recent studies in engineering design and analysis due to its flexibility, wide-spread applicability and ability to find multiple trade-off solutions. Optimal machining parameter determination is an important matter for ensuring an efficient working of a machining process. In this article, the use of an EMO algorithm and a suitable local search procedure to optimize the machining parameters (cutting speed, feed and depth of cut) in turning operations is described. Thereafter, the efficiency of the proposed methodology is demonstrated through two case studies -one having two objectives and the other having three objectives. Then, EMO solutions are modified using a local search procedure to achieve a better convergence property. It has been demonstrated here that a proposed heuristics-based local search procedure in which the problem-specific heuristics are derived from an innovization study performed on the EMO solutions is a computationally faster approach than the original EMO procedure. The methodology adopted in this article can be used in other machining tasks or in other engineering design activities.

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“…Amiolemhen et al optimized seven turning operations converting a cylindrical bar stock into a continuous finished profile to minimize unit production cost [5]. These operations included facing, turning, centering, drilling, boring, parting, and chamfering, with each operation subject to several practical constraints.…”

Section: A Turningmentioning

confidence: 99%

Session 6: Information security

Chen

Yang

2013

2013 IEEE Third International Conference on Information Science and Technology (ICIST)

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Machining parameter optimization plays an important role in satisfying quality requirements of machined parts at low production cost or time. It requires optimal selection of machining parameters and is essential for the process automation and implementation of a computer-integrated manufacturing system. In this research, a brief review of recent progress on the optimization of machining parameters is introduced. Some new machining practices developed in recent years are discussed including hard turning, dry cutting, high speed machining, and machining of difficult-to-machine materials. Modeling skills for creating relations between input parameters and output performances of machining processes and optimization techniques for solving optimal or near-optimal solutions are summarized and analyzed.

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Application of genetic algorithms—determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile

Cited by 46 publications

References 8 publications

Speed-varying cutting force coefficient identification in milling

Speed-varying cutting force coefficient identification in milling

Hybrid evolutionary multi-objective optimization and analysis of machining operations

Session 6: Information security

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