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  1. 1
    Optimization of turning parameters using ant colony optimization

    Optimization of turning parameters using ant colony optimization by Mohamad Nazri, Semoin

    Published 2008
    “…The results indicate that the proposed ant colony framework is effective to optimized turning parameter. Lastly, ACO algorithm was successfully optimize depth of cut, cutting speed, feed rate and minimized production cost per unit.…”
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  2. 2
    Optimization of cnc turning parameters for minimizing temperature rise in aluminum using a genetic algorithm

    Optimization of cnc turning parameters for minimizing temperature rise in aluminum using a genetic algorithm by Mimi Muzlina, Mukri

    Published 2024
    “…In the second optimization process, machining parameters such as cutting speed, feed rate, and depth of cut are optimized using a multi-objective genetic algorithm to concurrently lower temperature rise and surface roughness. …”
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  3. 3
    Optimization of turning parameters using genetic algorithm method

    Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

    Published 2008
    “…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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  4. 4
    Machining optimization using Firefly Algorithm / Farhan Md Jasni

    Machining optimization using Firefly Algorithm / Farhan Md Jasni by Md Jasni, Farhan

    Published 2020
    “…Based on the previous research on the success of Firefly Algorithm, this approach will be able to optimize the machining parameter of milling operation. …”
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  5. 5
    Optimization machining parameters in pocket milling using genetic algorithm and mastercam

    Optimization machining parameters in pocket milling using genetic algorithm and mastercam by Abdullah, Haslina, Isa, Nurshafinaz, Zakaria, Mohamad Shukri

    Published 2023
    “…Mastercam software has been used to verify the algorithm's results by applying the optimum parameter generated by GA in the Mastercam. …”
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  6. 6
    Optimization of milling parameters using ant colony optimization

    Optimization of milling parameters using ant colony optimization by Mohd Saupi, Mohd Sauki

    Published 2008
    “…The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation.…”
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  7. 7
    Optimization of Machining Parameters in Turning Operation Using PSO and AIS Algorithms: A Survey

    Optimization of Machining Parameters in Turning Operation Using PSO and AIS Algorithms: A Survey by Abbas, Adnan Jameel, Minhat, Mohamad, Abd Rahman, Md Nizam

    Published 2012
    “…This study deals with different machining performance in turning operation like surface roughness, material removal rate , tool wear , tool life, production cost, machining time and cutting temperature. Most papers in the field of turning parameters optimization are based on (PSO) algorithms, but only a few efforts that are using (AIS) algorithms. …”
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  8. 8
    Multi-objectives process optimization in end milling process of aluminium alloy 6061-T6 using genetic algorithm

    Multi-objectives process optimization in end milling process of aluminium alloy 6061-T6 using genetic algorithm by W., Safiei, Rahman, M. M., M.Y., Ali

    Published 2024
    “…Therefore, this paper aims to obtain optimum conditions of ethe nd milling process for three cutting inserts with multi-objective parameters using a combination of mathematical modelling and genetic algorithm. …”
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  9. 9
    Parametric Optimization of End Milling Process Under Minimum Quantity Lubrication With Nanofluid as Cutting Medium Using Pareto Optimality Approach

    Parametric Optimization of End Milling Process Under Minimum Quantity Lubrication With Nanofluid as Cutting Medium Using Pareto Optimality Approach by Najiha, M. S., M. M., Rahman, K., Kadirgama

    Published 2016
    “…In this paper a genetic algorithm based multi-objective optimization approach is applied in order to predict the optimal machining parameters for the end milling process of aluminium alloy 6061 T6 combined with minimum quantity lubrication (MQL) conditions using waterbased TiO2 nanofluid as cutting fluid. …”
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  10. 10
    Impact of evolutionary algorithm on optimization of nonconventional machining process parameters

    Impact of evolutionary algorithm on optimization of nonconventional machining process parameters by B V, Raghavendra, R Annigiri, Anandkumar, Srikatamurthy, JS

    Published 2025
    “…Using a Python environment, three evolutionary algorithms such as, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Firefly Algorithm (FA), were simulated to evaluate their effectiveness in minimizing surface roughness (Ra). …”
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  11. 11
    Optimisation of laser cutting parameters of oil palm wood / Harizam Mohd Zin

    Optimisation of laser cutting parameters of oil palm wood / Harizam Mohd Zin by Harizam, Mohd Zin

    Published 2013
    “…The potential of genetic algorithm in optimization was utilized in the proposed hybrid model to minimize the error prediction for regions of cutting conditions away from the Taguchi based factor level points. …”
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  12. 12
    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end-milling of Inconel 718 using coated WC-co inserts

    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end-milling of Inconel 718 using coated WC-co inserts by Ishtiyaq, M. H., Amin, A. K. M. Nurul, Karim, A.N. Mustafizul, Ginta, Turnad Lenggo, Patwari, Muhammed Anayet Ullah

    Published 2007
    “…RSM has been utilized to develop an efficient mathematical model for surface roughness in terms of cutting parameters. For this purpose, a number of machining experiments based on statistical central composite design of experiments method are carried out. …”
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  13. 13
    Prediction of Optimum Cutting Conditions in Dry Turning Operations of S45C Mild Steel using AIS and PSO Intelligent Algorithm

    Prediction of Optimum Cutting Conditions in Dry Turning Operations of S45C Mild Steel using AIS and PSO Intelligent Algorithm by Minhat, Mohamad, Abd Rahman, Md Nizam, Abbas, Adnan Jameel

    Published 2014
    “…The suggested system is based on Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) intelligent algorithms. …”
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  14. 14
    Experimental Investigation and Optimization of Minimum Quantity Lubrication for Machining of AA6061-T6

    Experimental Investigation and Optimization of Minimum Quantity Lubrication for Machining of AA6061-T6 by Najihah, Mohamed, M. M., Rahman, K., Kadirgama

    Published 2015
    “…Process parameters including the cutting speed, depth of cut, feed rate and MQL flow rate are selected for study to develop an optimization model for flank wear based on the genetic algorithm. …”
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  15. 15
    Computational inteligence in optimization of machining operation parameters of ST-37 steel

    Computational inteligence in optimization of machining operation parameters of ST-37 steel by Golshan, Abolfazl, Ghodsiyeh, Danial, Gohari, Soheil, Ayob, Amran, Baharudin, B. T. Hang Tuah

    Published 2013
    “…The cutting parameters used in this experimental study include cutting speed, feed rate, depth of cut and rake angle. …”
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  16. 16
    Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy

    Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy by Hossain, Mohammad Ishtiyaq, Amin, A. K. M. Nurul, Patwari, Anayet Ullah

    Published 2008
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  17. 17
    Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network

    Modeling, Testing and Experimental Validation of Laser Machining Micro Quality Response by Artificial Neural Network by Sivarao, Subramonian

    Published 2009
    “…Therefore, prediction of laser machining cut quality, namely surface roughness was carried out using machine learning techniques based on Quick Back Propagation Algorithm using ANN. …”
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  18. 18
    Sustainable optimization of dry turning of stainless steel based on energy consumption and machining cost

    Sustainable optimization of dry turning of stainless steel based on energy consumption and machining cost by Bagaber, Salem Abdullah, A. R., Yusoff

    Published 2018
    “…In this work, the influence of cutting parameters, namely, cutting speed, feed rate, and depth of cut, on energy, cost, and tool wear are first analyzed. …”
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  19. 19
    A GA-based optimization for Fourth-order Sallen-Key low-pass filter

    A GA-based optimization for Fourth-order Sallen-Key low-pass filter by Neoh, Siew Chin, Dr., Teoh, Yeong Yeap

    Published 2013
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  20. 20
    Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology

    Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

    Published 2015
    “…Optimization of thin film coating parameters is important in identifying the required output.Two main issues of the process of physical vapor deposition (PVD) are manufacturing costs and customization of cutting tool properties.The aim of this study is to identify optimal PVD coating process parameters.Three process parameters were selected, namely nitrogen gas pressure (N2),argon gas pressure (Ar),and Turntable Speed (TT),while thin film grain size of titanium nitrite (TiN) was selected as an output response.Coating grain size was characterized using Atomic Force Microscopy (AFM) equipment.In this paper,to obtain a proper output result,an approach in modeling surface grain size of Titanium Nitrite (TiN)coating using Response Surface Method (RSM) has been implemented. …”
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