Optimal process parameters for minimizing the surface roughness in CNC lathe machining of Co28Cr6Mo medical alloy using differential evolution

Nee, Chew Ying; Saad, Mohd Sazli; Nor, Azuwir Mohd; Zakaria, Mohd Hafiz; Baharudin, Mohamad Ezral

doi:10.1007/s00170-018-1817-0

Cited by 21 publications

(7 citation statements)

References 19 publications

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“…The steps included in the DE are initial population generation, mutation, recombination, and selection through repeated trials until the stopping criteria are obtained [31], [32]. The initial population is modi ed s per the Eq.…”

Section: Differential Evolutionmentioning

confidence: 99%

Micro-slit fabrication using magnetic-assisted ECDM and parametric optimization by metaheuristic algorithms

Gehlot

Jha

Jain

2023

J Braz. Soc. Mech. Sci. Eng.

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Micro-slit fabrication of more than 1000 microns thick workpieces using the ECDM process is a complicated task since the ejection e ciency to remove the adhered particle dissolved from the workpiece in the electrolyte decreases with a higher depth of cut. Here micro-slit fabrication of more than 1200 µm thickness was carried out with magnetic eld application on the ECDM. The magnetic eld induces MHD convection on the electrolyte ow around the tool, which creates vorticity on the electrolyte. The centrifugal force created by the uid when it rotates under MHD convection removes the material at a higher depth of cut. The machining performance in terms of maximum slit-cut thickness improves with increasing the magnetic eld strength up to the optimum level because it induces the centrifugal force on the bubble, which enhances the gas lm formation and discharge frequency. The experiments were conducted to fabricate a micro-slit with Taguchi L18 design and observe the effect of voltage, concentration, magnetic strength, and feed rate on MRR and width overcut. The magnetic eld contributes signi cantly to the machining rate and overcut improvements. The metaheuristic algorithms, i.e., particle swarm optimization, differential evolution, and TLBO, applied and compared the optimal results of response parameters. The results reveal that the optimum value of MRR and WOC are the same, but the time and the iterations necessary to reach the optimal solution differ.

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Section: Differential Evolutionmentioning

confidence: 99%

Micro-slit fabrication using magnetic-assisted ECDM and parametric optimization by metaheuristic algorithms

Gehlot

Jha

Jain

2023

J Braz. Soc. Mech. Sci. Eng.

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“…This technique resulted in shorter machining time, minimized programmer input and increased quality. Nee, et al, (2018) performed experiments to observe the correlation between the best possible feed-rate, spindle rotational speed, depth of cut and tool-tip radius with the amount of surface roughness in the structure. They reduced the surface roughness to the lowest levels, inorder to derive the optimal cutting parameters.…”

Section: Process Performancementioning

confidence: 99%

The Impact of Learning on the Selection of Cost-Effective Machining and Production Process

Hossain¹

2022

Preprint

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<p>Informed production decision-making requires a structured understanding of the production process and costs associated. This research proposes a decision support system incorporating the learning effects on selecting cost-effective machining production process. The approach is applied to the case on manufacturing wind turbine blades. The machining industry has experienced a surge in demand for complex machining applications and an availability of a wide range of new machining technologies. Producing complex structures requires experienced CNC programmers that perform extensive strategy planning, design and optimization. The system developed considers the production processes, cost items, power learning curve and experience of the programmers. This enables managers to evaluate between various production processes and select the most cost-efficient process. An analytical approach to evaluate cost efficiency between 3-axis CNC and 5-axis CNC machines. The result is a user-friendly planning and selection tool aiding in answering critical managerial questions related to cost-effectiveness.</p><div><br></div>

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“…The developed association between variables can be used for better control and productivity of the process. Researchers have employed this technique to model various advanced manufacturing processes empirically [39][40][41][42][43][44]. In the present study, the association between the process control parameters and the response of the process is generalized by the following expression [45]:…”

Section: Nonlinear Regression Analysismentioning

confidence: 99%

An experimental investigation for parametric appraisal of electrohydrodynamic-driven microfabrication approach using teaching and learning-based optimization

Das

Roy

Murmu

2020

J Braz. Soc. Mech. Sci. Eng.

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Electrohydrodynamic (EHD)-based microfabrication process is a novel high-resolution pattern generation technology for micro/nanoscale system fabrication. Achieving tiny droplets during deposition has been a critical issue for the further development of this new emerging technology. It is imperative to identify the proper fabrication parameters to get the desired resolution operation. In the current study, first, an experimental EHD microfabrication setup is developed, and then experiments are carried out to search for an operating condition, which can promote high-resolution droplet generation. The experiments are planned by design of experiment scheme. The influence of the operating control parameters such as nozzle to substrate gap, voltage and the supply rate of the material on deposited droplets diameter is investigated in the current work. Characterization study reveals that nozzle to substrate gap and supply rate have a positive effect on deposition behavior, whereas voltage has a negative association with the droplet resolution. For better control of the EHD process, a causal relationship is developed between process control and performance parameter. A metaheuristic search algorithm named teaching and learning-based optimization (TLBO) is employed to find out the optimal operating environment of the EHD process. The results are compared with the traditional robust parameter design approach, and the authors found that TLBO performs more effectively for resolution improvement in the EHD-driven microfabrication process. Moreover, the sequence in which the control parameters govern the fabrication process is identified and reported.

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Optimal process parameters for minimizing the surface roughness in CNC lathe machining of Co28Cr6Mo medical alloy using differential evolution

Cited by 21 publications

References 19 publications

Micro-slit fabrication using magnetic-assisted ECDM and parametric optimization by metaheuristic algorithms

Micro-slit fabrication using magnetic-assisted ECDM and parametric optimization by metaheuristic algorithms

The Impact of Learning on the Selection of Cost-Effective Machining and Production Process

An experimental investigation for parametric appraisal of electrohydrodynamic-driven microfabrication approach using teaching and learning-based optimization

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