Genetic Algorithm Applied to Investigate Cutting Process Parameters Influence on Workpiece Price Formation

Librantz, André Felipe Henriques; Coppini, Nivaldo Lemos; Baptista, Elesandro Antônio; Araújo, Sidnei Alves de; Rosa, Aparecida de Fátima Castello

doi:10.1080/10426914.2010.512817

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…The process capability index C pk obtained by the proposed method was 1.73 (at the best state of quality), which was better than the process capability index C pk of 1.65 obtained by the Taguchi method [5,6,12,16,19].…”

Section: Resultsmentioning

confidence: 86%

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Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Wang¹

2014

Materials and Manufacturing Processes

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The light guide plate (LGP) printing process is used to obtain the optimal optical brightness, which requires the optimization of the LGP printing process parameters. This study analyzed the optical brightness optimization parameter design. This study integrated the back-propagation neural network (BPN) and a revised genetic algorithm (GA) to identify the optimal experimental level combination of the optical brightness parameter design. The obtained optical brightness process capability C pk by the proposed method was 1.73, which was better than the process capability C pk of 1.65 obtained by using the Taguchi method.

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Section: Resultsmentioning

confidence: 86%

“…The trust radius was then automatically readjusted using fuzzy logic according to the prediction accuracy. The approximation model was repeatedly evolved and searched recursively until convergence [5][6][7][8][9][10][11][12][13].…”

Section: Lgp Testmentioning

confidence: 99%

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Wang¹

2014

Materials and Manufacturing Processes

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“…It should be noted that the choice of using GA and CW to compose the optimization model to obtain better routes was because both algorithms have shown good results for routing problems and other related combinatorial optimization problems. Nevertheless, the use of heuristics to populate the initial GA population with feasible solutions has proven to be a good alternative for solving routing problems [34,[41][42][43]. In addition, we tested other metaheuristics such as Simulated Annealing-SA [44][45][46] and Tabu Search-TS [45,46], but the results obtained in preliminary experiments do not indicate improvements in using them.…”

Section: Proposed Ai-based Approach For Economic and Environmental As...mentioning

confidence: 99%

Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations

Oliveira Neto,

de Araujo,

Gomes

et al. 2023

Sensors

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The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.

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“…Such growth has been motivated mainly by the availability of computers with greater processing capacities and the increase in the number of software packages and libraries that facilitate the use of the technique (Bhapkar et al, 2019). It should be noted that speci cally for optimization applications, other AI techniques such as genetic algorithms (Librantz et al, 2011;Fernandes et al 2020;Tsao et al, 2020) and simulated annealing (Almeida et al, 2014;Tsao et al, 2020) are more appropriate and therefore they have been widely used in such applications.…”

Section: Ann In the Textile Industrymentioning

confidence: 99%

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

Rosa

Guerhardt

Júnior

et al. 2021

Preprint

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This work explores the modeling and optimization of the conditions to obtain a set of blue pigments for dyeing reactive cotton, by means of an approach that combines the techniques response surface methodology (RSM) and artificial neural network (ANN). By means of RSM technique the interactions and the effects of the main process variables (factors) on the behavior of coloristic intensity (K.S-1) were investigated. For this, a 26 central composite rotational design (CCRD) was carried out considering the factors temperature, NaCl, Na2CO3, NaOH, processing time and RB5 concentration. The results obtained show that all investigated factors have considerable effect on the behavior of K.S-1. The data produced in the dyeing experiments were used to build and train a Multilayer Perceptron ANN (MLP-ANN) to predict K.S-1, being the input layer of the MLP-ANN designed according to the results achieved by the RSM. The non-linear behavior of dyeing with RB5 was successfully modeled by a three-layer MLP-ANN comprising 6 input neurons, 15 hidden neuros, and 1 output neuron to indicate the value of K.S-1. The results achieved in the performed simulations confirmed the ANN effectiveness to predict K.S-1 values in RB5 the dyeing process, with high coefficient of determination (R2=0.942). The developed approach allowed the composition of a table containing optimized conditions to obtain a set of colors of the blue palette using RB5 dye, varying from sky blue to oxford blue, which will facilitate the assembly of the dyes. Finally, the experiments conducted in this work allowed the development of a computational tool to support the dyeing process, saving chemical inputs and time in cotton dyeing with specific dyestuff.

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Genetic Algorithm Applied to Investigate Cutting Process Parameters Influence on Workpiece Price Formation

Cited by 14 publications

References 22 publications

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

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