Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time

Raja, S. Bharathi; Baskar, N.

doi:10.1016/j.eswa.2011.11.110

Cited by 98 publications

(29 citation statements)

References 17 publications

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“…Many researchers have developed surface roughness prediction models in face milling using AI (Srinivasa Pai et al 2002;Vosniakos 2002, 2003;Saglam and Unuvar 2003;Bruni et al 2008;El-Sonbaty et al 2008;Lela et al 2009;Muñoz-Escalona and Maropoulos 2010;Razfar et al 2011;Bharathi Raja and Baskar 2012;Grzenda et al 2012;Bajić et al 2012;Kovac et al 2013;Simunovic et al 2013;Grzenda and Bustillo 2013;Elhami et al 2013;Saric et al 2013;Rodríguez et al 2017;Simunovic et al 2016;Selaimia et al 2017;Svalina et al 2017). Srinivasa Pai et al (2002 presented an estimation of flank wear in face milling based on the radial basis function (RBF) of neural networks using acoustic emission signals, surface roughness, and cutting conditions (cutting speed and feed).…”

Section: Introductionmentioning

confidence: 99%

“…Razfar et al (2011) presented an approach that defines the optimum cutting parameters that provide minimum surface roughness in face milling of X20Cr13 steel by combining an ANN and the harmony search algorithm. Bharathi Raja and Baskar (2012) conducted experimental studies of the influence of machining parameters such as the cutting speed, feed rate, and depth of cut on the surface roughness of aluminum and the provision of design surface roughness in face milling using Particle Swarm Optimization (PSO). Grzenda et al (2012) presented a new strategy for improving the AI models for predicting surface roughness using small datasets tested in high-torque milling operations.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N , in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power-that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements. Keywords

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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“…Moreover, statistical approaches such as Taguchi method [15][16][17], Response Surface Methodology [10,18], Grey Relational Analysis (GRA) [14,[19][20][21]; soft computing techniques (ANN & ANFIS [22]) and artificial intelligence such as Genetic Algorithm (GA) [23], Particle Swarm Optimization (PSO) [24,25], and Teachinglearning-based optimization (TLBO) [26] techniques have been used to optimize the process parameters which influence tribological and machining behaviour of composites. However, it is observed that most of the results are not favourable due to the uncertainty associated with the process variables.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Tribological performance of Coconut Shell Ash Particle Reinforced Al-Si-Fe Composites using Grey-Fuzzy Approach

Raju¹,

Rao²

2017

Tribol. Ind.

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“…PSO was initially designed to simulate flock of birds seeking for food. A scenario of group of birds randomly searching for food in an area demonstrates the PSO process (Raja and Baskar, 2012). In this analogy, there is only one piece of food in the area being searched.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Survey: Particle Swarm Optimization-Based Algorithms for Grid Computing Scheduling Systems

Ambursa

Latip

2013

Journal of Computer Science

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Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Metatask-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category.

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Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time

Cited by 98 publications

References 17 publications

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Assessment of Tribological performance of Coconut Shell Ash Particle Reinforced Al-Si-Fe Composites using Grey-Fuzzy Approach

A Survey: Particle Swarm Optimization-Based Algorithms for Grid Computing Scheduling Systems

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