Enhanced Multi-Objective Teaching-Learning-Based Optimization for Machining of Delrin

Natarajan, Elango; Kaviarasan, Varadaraju; Lim, Wei Hong; Tiang, Sew Sun; Tan, Teng Hwang

doi:10.1109/access.2018.2869040

Cited by 40 publications

(16 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem of Pareto Front (PF) has been solved by implementing modified version of TLBO and it is known as elitist TLBO (ETLBO) . This algorithm has also been used to multi‐objective optimization problems such as robot gripper, wind‐thermal emission, etc . After extensive study of the TLBO and its variants, it was found that this optimization technique is suitable for doing load balancing in fog infrastructure also.…”

Section: Comparison Of Mtlbo With Tlbo and Samentioning

confidence: 99%

Efficient content retrieval in fog zone using Nano‐Caches

Singh

Kumar

Sharma

2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano‐Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano‐Caches are integrated for delivering contents efficiently, using search‐based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning‐Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano‐Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning‐Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning‐Based Optimization (MTLBO) approach is better than Teaching Learning‐Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size.

show abstract

Section: Comparison Of Mtlbo With Tlbo and Samentioning

confidence: 99%

Efficient content retrieval in fog zone using Nano‐Caches

Singh

Kumar

Sharma

2019

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…For instance, utility company seeks to optimize the curtailment and scheduling of electricity loads during demand response events in order to minimize the total energy consumption and operating cost [1]- [4]. For manufacturing industry, process planners need to determine the optimal parameter settings based on multi-response machining models formulated for achieving the desired goals such as minimum surface roughness, minimum wear, maximum material removal rate, maximum microhardness, maximum lap shear and etc [5]- [7]. In the emerging field of artificial intelligence, the practitioners encounter numerous optimization problems such as…”

Section: Introductionmentioning

confidence: 99%

Hovering Swarm Particle Swarm Optimization

2021

Self Cite

View full text Add to dashboard Cite

PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.

show abstract

“…Many researches have also been conducted in the past using above methodologies in relation to metals, metal matrix composites, and nanocomposites. 10 -24 Elango et al 25 attempted to find the optimum control parameters for turning Delrin through TLBO algorithm. But the drilling characteristics of Delrin have not yet been addressed, which look to be a high requirement for industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Prediction of surface quality and optimization of process parameters in drilling of Delrin using neural network

Kaviarasan¹,

Venkatesan²,

Natarajan

2019

Progress in Rubber, Plastics and Recycling Technology

Self Cite

View full text Add to dashboard Cite

The high-temperature polymers like Acetal homopolymer (Delrin) currently have a wide variety of use. They are quite often utilized in traditional components to reduce weight, cost or meet a specific application requirement, and so on. Some of preferred uses of such polymers include aircraft interiors, wire insulation, wire couplings and fixtures, and so on, particularly at high-temperature applications. The machining process like drilling may affect the near net shape of the final product. This experimental study is done through modeling and optimization for identifying the suitable tool and optimum parameters for drilling of Delrin polymer under dry conditions to achieve high surface finish. The three levels of parameters such as spindle speed ( N), feed rate ( f), and tool point angle ( Θ) are taken as control parameters of the response variable. Two different commercially available tool materials namely high-speed steel drill tool and solid carbide tool are accounted in experiments. L27 orthogonal array is initially taken for the experimentation in CNC turning center with horizontal drilling setup. Artificial neural network is employed to sample, train, and test the input parameters in order to lessen the experimental error and measurement error of response variables. Response surface models are developed and optimal parameters toward the surface quality of the hole are determined through the desirability function approach. It is found that the surface generated under dry mode with speed of 1026 r/min, feed of 0.1 mm/min, point angle of 118° recorded the surface roughness of 0.699 µm, which is considered to be the best for drilling Delrin material.

show abstract

Enhanced Multi-Objective Teaching-Learning-Based Optimization for Machining of Delrin

Cited by 40 publications

References 40 publications

Efficient content retrieval in fog zone using Nano‐Caches

Efficient content retrieval in fog zone using Nano‐Caches

Hovering Swarm Particle Swarm Optimization

Prediction of surface quality and optimization of process parameters in drilling of Delrin using neural network

Contact Info

Product

Resources

About