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  1. 1
    Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm

    Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm by Mimi Muzlina, Mukri, Nor Atiqah, Zolpakar, Pathak, Sunil

    Published 2023
    “…In this work, Multi-Objective Genetic Algorithm (MOGA) optimization is used to find the combination of machining parameters at different levels of hardness of 20, 36, and 43 to obtain minimum surface roughness and minimum cutting temperature in turning operation. …”
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  2. 2
    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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  3. 3
    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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  4. 4
    Optimization of machining parameters in turning for different hardness using multi-objective genetic algorithm / Mimi Muzlina Mukri, Nor Atiqah Zolpaka and Sunil Pathak

    Optimization of machining parameters in turning for different hardness using multi-objective genetic algorithm / Mimi Muzlina Mukri, Nor Atiqah Zolpaka and Sunil Pathak by Mukri, Mimi Muzlina, Zolpaka, Nor Atiqah Zolpaka, Pathak, Sunil

    Published 2023
    “…In this work, Multi-Objective Genetic Algorithm (MOGA) optimization is used to find the combination of machining parameters at different levels of hardness of 20, 36, and 43 to obtain minimum surface roughness and minimum cutting temperature in turning operation. …”
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  5. 5
    Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm

    Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm by Kamaruddin, Shafie, Ridzuan, Arman Hilmi, Sukindar, Nor Aiman

    Published 2025
    “…The focus of this study is to determine the optimum input parameter of the 3D printer using the Bees Algorithm (BA). This study uses the Bees Algorithm to predict the best combination parameters to optimise the surface roughness of parts printed by a fused deposition modelling (FDM) machine. …”
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  6. 6
    Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm

    Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm by Al Hazza, Muataz, Adesta, Erry Yulian Triblas, Superianto, M. Y., Riza, Muhammad

    Published 2012
    “…Two objectives have been considered, minimum cutting temperature and minimum arithmetic mean roughness (Ra). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed…”
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    Proceeding Paper
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  7. 7
    Surface roughness optimization based on hybrid harmony search and artificial bee colony algorithm in electric discharge machining process

    Surface roughness optimization based on hybrid harmony search and artificial bee colony algorithm in electric discharge machining process by Deris A.M., Solemon B.

    Published 2023
    Conference Paper
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  8. 8
    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization

    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization by Nooraziah Ahmad, Tiagrajah V. Janahiraman

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  9. 9
    Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining

    Artificial bee colony in optimizing process parameters of surface roughness in end milling and abrasive waterjet machining by Norfadzlan, Bin Yusup

    Published 2012
    “…Optimizing the process parameters is essential in order to provide a better quality and economics machining. This research develops an optimization algorithm using artificial bee colony (ABC) algorithm to optimize the process parameters that will lead to minimum surface roughness (Ra) value for both end miling and abrasive waterjet machining. …”
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    Final Year Project Report / IMRAD
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  10. 10
    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 coate... by Ishtiyaq, M. H., Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  11. 11
    Surface roughness optimization in end milling using the multi objective genetic algorithm approach

    Surface roughness optimization in end milling using the multi objective genetic algorithm approach by Al Hazza, Muataz Hazza Faizi, Adesta, Erry Yulian Triblas, Riza, Muhammad, Mohammad Yuhan, Suprianto

    Published 2012
    “…This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. …”
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  12. 12
    Machining surface roughness monitoring using acoustic emission method

    Machining surface roughness monitoring using acoustic emission method by Mohd Syazlan, Mohd Hatta

    Published 2010
    “…This thesis is to investigate the machining surface roughness monitoring using acoustic emission method. …”
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    Undergraduates Project Papers
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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
    “…AIS and PSO results have been experimentally trained to find cutting zone temperature, surface roughness and cutting time by using the parameters directly on a CNC turning machine. …”
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    Conference or Workshop Item
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  14. 14
    Prediction and investigation of surface response in high speed end milling of Ti-6Al-4V and optimization by genetic algorithm

    Prediction and investigation of surface response in high speed end milling of Ti-6Al-4V and optimization by genetic algorithm by Alam, Md. Shah, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah, Konneh, Mohamed

    Published 2010
    “…Machining was performed on a five-axis NC milling machine with a high speed attachment, using spindle speed, feed rate, and depth of cut as machining variables. …”
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  15. 15
    Graphical user interface for surface roughness prediction of CNC milling machine / Muhammad Qayyum Nor Asffan

    Graphical user interface for surface roughness prediction of CNC milling machine / Muhammad Qayyum Nor Asffan by Nor Asffan, Muhammad Qayyum

    Published 2020
    “…It can function to analyse data, develop algorithms even create models and applications. In this research, MATLAB Graphical User Interface (GUI) is used to develop a user- friendly program that can predict surface roughness of Aluminium 6061 in CNC milling machine. …”
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    Student Project
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  16. 16
    Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]

    Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.] by V. Chowdary, Boppana, Jahoor,, Riaz, Ali,, Fahraz, Trishel, Gokool

    Published 2019
    “…Surface roughness is often used as a measure to identify surface integrity of machined parts. …”
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  17. 17
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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  18. 18
    Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

    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, Muhammed Anayet Ullah

    Published 2011
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  19. 19
    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization

    A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization by Ahmad N., Janahiraman T.V.

    Published 2023
    Conference Paper
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  20. 20
    Predictive modelling of machining parameters of S45C mild steel

    Predictive modelling of machining parameters of S45C mild steel by Abbas, Adnan Jameel

    Published 2016
    “…The PSO1 algorithm which used first main temperature objective function gives the best roughness value (0.52 μm) compared with other algorithms, followed by the AIS2 and PSO2 that give (0.86 μm). …”
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