Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization

Ghosh, Gourhari; Mandal, Prosun; Mondal, Sankar Prasad

doi:10.1007/s00170-017-1417-4

Cited by 91 publications

(38 citation statements)

References 45 publications

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“…Reprinted by permission from Springer Nature: Int J Adv Manuf. Ghosh et al 7 Copyright Springer-Verlag London (2017).…”

Section: Prediction Of Surface Roughnessmentioning

confidence: 99%

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

Zhong

2021

Advances in Mechanical Engineering

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This article discusses the surface roughness of wood and advanced engineering materials after machining and its prediction. The topics are surface roughness of precision-machined advanced engineering materials, machining of WC and Inconel, rapidly solidified Al alloys, surface roughness of wood materials, and prediction of surface roughness. Findings include that ductile streaks on silicon and glass surfaces ground or lapped with inexpensive machines largely reduced the polishing time to obtain the required surface roughness. Abrasive jet machining could remove the patterns from recycled wafers and improve the surface roughness. The roughness of WC-Co coatings was significantly improved by using the method of fast regime fluidized bed machining. As beryllium is a toxic element, the rapidly solidified Al alloy may be a good insert material to replace BeCu. Higher bonding strengths resulted from rougher surfaces of wood samples. Wood samples had reduced bonding strengths after soaking in water. Optimum artificial neural networks (ANNs) with necessary inputs could accurately predict the roughness values. ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs. Minimum quantity lubrication is a hot research topic to minimize the amount of the fluid for cost and environmental considerations.

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“…Reprinted by permission from Springer Nature: Int J Adv Manuf. Ghosh et al 7 Copyright Springer-Verlag London (2017).…”

Section: Prediction Of Surface Roughnessmentioning

confidence: 99%

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

Zhong

2021

Advances in Mechanical Engineering

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“…The input parameters were the track pad stiffness and the sleeper spacing. Kumar and Jha (2019) used genetic algorithms to perform multi-objective optimization of a vortex finder, with the Euler number and collection efficiency as the target performance parameters. Liu et al (2019) used the four size parameters of the drone as the input parameters of the genetic algorithm and the ratio of the lift coefficient and the drag coefficient as the target performance parameters.…”

Section: Introductionmentioning

confidence: 99%

Structural optimization of a new type of lever-assisted gear reducer based on a genetic algorithm

et al. 2021

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Abstract. Gear reducers are critical for speed and torque transmissions between motors and manipulators. With the development of robotic research, many new requirements, such as low speed and heavy load, have been proposed for the design of gear reducers used in the joints. To meet these challenges, here, we present the design of a new gear reducer based on a spherical motion sub-lever drive mechanism. Our lever-based gear reducer can transmit the speed and torque from the input shaft to the output shaft through a fixed-axis gear train transmission, lever transmission, and internal translational gear transmission. Compared with traditional gear reducers, our lever-based reducer has stronger load capacities and is suitable for low-speed and heavy-load scenarios. The design parameters of the lever drive mechanism were optimized via finite element analysis and a genetic algorithm, and the assembly of the lever drive mechanism was further simplified. We found the dimensions of the lever are critical for improving the overall performance of this reducer. In addition, the transmission ability of this reducer was demonstrated by a physical prototype. This reducer will find many applications in robotic joints, cranes, and mine hoists.

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“…The advents of global evolutionary methods such as PSO, ACO and other algorithms have determined the suitable approaches to handle complex optimization problems such as function minimization problems [17], [18], ANNs, expert systems and fuzzy systems, are the most promising applications of global optimization [19], [20]. In the recent decades, PSO has been widely implied to broaden the learning factors, weight minimization, and architecture of ANNs [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization With Probability Sequence for Global Optimization

et al. 2020

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Particle Swarm Optimization (PSO) has been frequently employed to solve diversified optimization problems. Choosing initial placement for population plays an important role in meta-heuristic methods since they can significantly converge. In this study, probability distribution has been introduced to enhance the diversity of swarm and convergence speed. Population initialization method based on uniform distribution is normally used when there is no preceding knowledge available regarding the candidate solution. In this paper, a new approach to initialize population is proposed using probability sequence Weibull marked as (WI-PSO) that applies the probability distribution to generate numbers at random locations for swarm initialization. The proposed method (WI-PSO) is tested on sixteen well-known unimodal and multi-modal benchmark test functions broadly adopted by the research community and its encouraging performance is investigated and compared with the Exponential distribution based PSO (E-PSO), Beta distribution based PSO (BT-PSO), Gamma distribution based PSO (GA-PSO) and Log-normal distribution based PSO (LN-PSO). Artificial Neural Networks (ANNs) have become the most powerful tool for classification of complex benchmark problems. We have experimented the proposed method (WI-PSO) for weight optimization of a feed-forward neural network to ensure its purity and have compared with conventional back-propagation algorithm (BPA), E-PSO, BT-PSO, GA-PSO and LN-PSO. Due to flexible behaviour in the degree of freedom, the experimental results infer the perfection and dominance of the Weibull based population initialization. The result exhibits the anticipation of influence exerted by the proposed technique on all sixteen objective functions and eight real-world benchmark data sets.

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Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization

Cited by 91 publications

References 45 publications

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

Structural optimization of a new type of lever-assisted gear reducer based on a genetic algorithm

Particle Swarm Optimization With Probability Sequence for Global Optimization

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