Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process

Wibowo, Antoni; Desa, Mohammad Ishak

doi:10.1016/j.eswa.2012.04.004

Cited by 27 publications

(21 citation statements)

References 10 publications

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“…Finally the mutation operator modifies each new solution with a low probability (mutation rate). Readers interested in the applications of GA in machining optimization are referred to [43][44][45][46].…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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Section: Optimization Algorithmsmentioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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“…The development of a quantitative translation between them enables the comparison of clinical trials in particular those requiring a longitudinal design over time (van Buuren et al, 2001).…”

Section: Applications Of Cross-calibrationmentioning

confidence: 99%

“…The data may be selfreported or may consist of self-responses/assessments. The challenge herein lies in assessing the different ways individuals apply and interpret categorical response scales (Salomon et al, 2004;Murray et al, 2002;van Buuren & Hopman-Rock, 2001). However, the calibration of such variables requires that the mapping process be customized to fit the nature of their relationship.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithms for cross-calibration of categorical data

Aboukhamseen

M’Hallah

2017

J. Mod. App. Stat. Meth.

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The probabilistic problem of cross-calibration of two categorical variables is addressed. A probabilistic forecast of the categorical variables is obtained based on a sample of observed data. This forecast is the output of a genetic algorithm based approach, which makes no assumption on the type of relationship between the two variables and applies a scoring rule to assess the fitness of the chromosomes. It converges to a good-quality point probability forecast of the joint distribution of the two variables. The proposed approach is applied both at stationary points in time and across time. Its performance is enhanced when additional sampled data is included, and can be designed with different scoring rules or made to account for missing data.

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“…Special algorithm learning based neural network integrating feature selection and classification was presented in [6] and genetic algorithms with neural networks for modelling of different processes parameters were described in [7]. A technique developed using hybridization of kernel principal component analysis (KPCA) based nonlinear regression and GAs to estimate the optimum values of the three parameters such that the estimated surface roughness is as low as possible was presented by Wibowo and Desa in [8].…”

Section: Introductionmentioning

confidence: 99%

Genetic Based Approach to Predicting the Elongation of Drawn Alloy

Gusel¹,

Rudolf²,

Brezočnik³

2015

Int. j. simul. model.

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This paper describes a genetic based approach for the modelling of elongation in cold drawn copper alloy. Genetic programming is one of the most general genetic based methods and was used in our research. It is an automated evolutionary computation method for creating a working computer programme from a problem's high-level statement. Genetic programming does this by breeding a population of computer programmes genetically using the principles of Darwinian's natural selection and biologically inspired operations. In our research, material was formed by drawing using different process parameters and then determining elongation of the specimens. On the basis of a training data set, various different genetic models for the elongation distribution were developed during simulated evolution. The accuracies of the best models were proved by a testing data set and comparison between the genetic and regression models was carried out.

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Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process

Cited by 27 publications

References 10 publications

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Genetic algorithms for cross-calibration of categorical data

Genetic Based Approach to Predicting the Elongation of Drawn Alloy

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