Process parameters optimization for micro end-milling operation for CAPP applications

Kumar, S. P. Leo; Jerald, Jason; Kumanan, S.; Nargundkar, Aniket

doi:10.1007/s00521-014-1683-0

Cited by 16 publications

(4 citation statements)

References 24 publications

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“…Comparison Tables 9, 10 and 11 exhibit solutions consisting of mean and best solution along with standard deviation for 30 trials of each objective function of algorithms for solving micro-turning, micro-milling and micro-drilling processes. For micro-drilling processes, LAB obtained comparable results with Mulit-CI [59], GA [42], SA [36] and variations of CI as well as outperforming GA, SA and PSO in convergence rate. However, for micro-turning and micro-milling with 0.7mm and with 1mm tool diameter for machining time (M t ) LAB could compete with other algorithms but could not produce superior results.…”

Section: Solutions To Micro-machining Problemsmentioning

confidence: 85%

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LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Reddy

Kulkarni

Krishnasamy

et al. 2022

Preprint

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This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behaviour exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (Leader, Advocate, Believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. The LAB algorithm was applied for solving engineering problems, including abrasive water jet machining, electric discharge machining, micromachining processes and turning of titanium alloy in a minimum quantity lubrication environment. The results were compared with other algorithms such as Firefly Algorithm, Cohort Intelligence, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimisation and Multi-Cohort Intelligence. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features and limitations of the LAB algorithm are also discussed.

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Section: Solutions To Micro-machining Problemsmentioning

confidence: 85%

“…Process parameters x 1 (in rpm) as cutting speed, x 2 (in mm/min) as feed and process responses surface roughness R a and machining time M t with two milling cutters with diameters 0.7mm and 1mm are considered for micro-milling process [41]. The formulated regression model for the above process is adopted here:…”

Section: Micro-machining Processesmentioning

confidence: 99%

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Reddy

Kulkarni

Krishnasamy

et al. 2022

Preprint

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“…Leo Kumar et al conducted an experimental study to explore how cutting parameters impact surface quality. They employed a genetic algorithm (GA) to determine the optimal process settings that yield an excellent surface finish while minimizing machining time [29]. Neuro-fuzzy systems integrate artificial neural networks and fuzzy logic to build intelligent systems capable of managing complex and uncertain information.…”

Section: State-of-the-art Review Of Electrochemical Machining Inconel...mentioning

confidence: 99%

Multi-Response Optimization of Electrochemical Machining Parameters for Inconel 718 via RSM and MOGA-ANN

Saha,

Mondal,

Čep

et al. 2024

Machines

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Inconel 718’s exceptional strength and corrosion resistance make it a versatile superalloy widely adopted in diverse industries, attesting to its reliability. Electrochemical machining (ECM) further enhances its suitability for intricate part fabrication, ensuring complex shapes, dimensional accuracy, stress-free results, and minimal thermal damage. Thus, this research endeavors to conduct a novel investigation into the electrochemical machining (ECM) of the superalloy Inconel 718. The study focuses on unraveling the intricate influence of key input process parameters—namely, electrolytic concentration, tool feed rate, and voltage—on critical response variables such as surface roughness (SR), material removal rate (MRR), and radial overcut (RO) in the machining process. The powerful tool, response surface methodology (RSM), is used for understanding and optimizing complex systems by developing mathematical models that describe the relationships between input and response variables. Under a 95% confidence level, analysis of variance (ANOVA) suggests that electrolyte concentration, voltage, and tool feed rate are the most important factors influencing the response characteristics. Moreover, the incorporation of ANN modeling and the MOGA-ANN optimization algorithm introduces a novel and comprehensive approach to determining the optimal machining parameters. It considers multiple objectives simultaneously, considering the trade-offs between them, and provides a set of solutions that achieve the desired balance between MRR, SR, and RO. Confirmation experiments are carried out, and the absolute percentage errors between experimental and optimized values are assessed. The detailed surface topography and elemental mapping were performed using a scanning electron microscope (SEM). The nano/micro particles of Inconel 718 metal powder, obtained from ECM sludge/cakes, along with the released hydrogen byproducts, offer promising opportunities for recycling and various applications. These materials can be effectively utilized in powder metallurgy products, leading to enhanced cost efficiency.

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“…Process parameters x 1 (in rpm) as cutting speed, x 2 (in mm/min) as feed and process responses surface roughness R a and machining time M t with two milling cutters with diameters 0.7mm and 1mm are considered for micro-milling process [40]. The formulated regression model for the above process is adopted here:…”

Section: Micro-machining Processesmentioning

confidence: 99%

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Reddy¹,

Kulkarni²,

Krishnasamy³

et al. 2022

Preprint

View full text Add to dashboard Cite

This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behaviour exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (Leader, Advocate, Believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. Besides benchmark problems, the LAB algorithm was applied for solving challenging engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes, and process parameter optimization for turning titanium alloy in a minimum quantity lubrication environment. The results were superior to the other algorithms compared such as Firefly Algorithm, Variations of Co-hort Intelligence, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimisation, and Multi-Cohort Intelligence. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features of the LAB algorithm along with its limitations are also discussed.

show abstract

Process parameters optimization for micro end-milling operation for CAPP applications

Cited by 16 publications

References 24 publications

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Multi-Response Optimization of Electrochemical Machining Parameters for Inconel 718 via RSM and MOGA-ANN

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

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