Glowworm swarm optimization (GSO) for optimization of machining parameters

Zainal, Nurezayana; Zain, Azlan Mohd; Radzi, Nor Haizan Mohd; Othman, Muhamad Razib

doi:10.1007/s10845-014-0914-7

Cited by 57 publications

(30 citation statements)

References 34 publications

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“…Artificial Neural Networks (ANN) are the most widely used AI technique Grzenda et al 2012;Díez-Pastor et al 2012;Benardos and Vosniakos 2002;Samanta et al 2008;Correa et al 2008), although other techniques like neuro-fuzzy inference systems (Samanta et al 2008), Bayesian networks (Correa et al 2008), genetic algorithms (Brezocnik et al 2004), swarm optimization techniques (Zainal et al 2016), and support vector machines (Prakasvudhisarn et al 2008) have also been tested for the same industrial task. Unfortunately, ANN models are highly dependent on the parameters of the neural networks (Bustillo et al 2011) and the process of fine-tuning these parameters is a highly time-consuming task that frequently requires expertise for good results.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised roughness prediction with partly unlabeled vibration data streams

Grzenda

Bustillo

2018

J Intell Manuf

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Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.

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Section: Introductionmentioning

confidence: 99%

Semi-supervised roughness prediction with partly unlabeled vibration data streams

Grzenda

Bustillo

2018

J Intell Manuf

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“…The experimental validation had been conducted and the result obtained is in very close which shows the effectiveness of the proposed approach. Zainal et al [18] employed an intelligence optimization techniques, viz., glowworm swarm optimization (GSO) and particle swarm optimization (PSO) for optimizing R a in milling aluminium with carbide tool. They found that GSO performs better.…”

Section: Introductionmentioning

confidence: 99%

Integrated optimization methodology for intelligent machining of inconel 825 and its shop-floor application

Tamang

Chandrasekaran

2016

J Braz. Soc. Mech. Sci. Eng.

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“…In the early 20th century, the machining process is recognized as non conventional machining process which includes milling, bending, grinding, drilling and drilling. Since the coming of novel technologies, there has been modern machining process such as electrochemical machining (ECM), electrical discharge machining (EDM), abrasive water jet (AWJ) and ultrasonic machining (USM) [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

A study of electrode wear ratio on EDM of Ti-6AL-4V with copper-tungsten electrode

2016

Self Cite

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Abstract. In this work, a study of electrode wear ratio (EWR) on the diesinking electrical discharge machining (EDM) ofTi-6AL-4V titanium alloy with copper-tungsten (Cu-W) electrode has been carried out. Pulse on time (ON), pulse of time (OFF), peak current (V) and servo voltage (SV) were seen as the machining parameters. The experiments were run according to the design of experiments (DOE), which is two levels of full factorial with added centre points. The experimental results reveal that pulse on time and peak current are statistically significant parameters for affecting EWR with the p-value of 0.0013 and 0.0012 respectively. Moreover, based on ANOVA, we recognized peak current as the most significant parameters which contribute 31.75%, followed by pulse on time, servo voltage and pulse on time which contribute 30.99%, 8.68% and 0.72%, respectively.

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Glowworm swarm optimization (GSO) for optimization of machining parameters

Cited by 57 publications

References 34 publications

Semi-supervised roughness prediction with partly unlabeled vibration data streams

Semi-supervised roughness prediction with partly unlabeled vibration data streams

Integrated optimization methodology for intelligent machining of inconel 825 and its shop-floor application

A study of electrode wear ratio on EDM of Ti-6AL-4V with copper-tungsten electrode

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