Zigzag machining surface roughness modelling using evolutionary approach

Göloğlu, Cevdet; Arslan, Yenal

doi:10.1007/s10845-008-0222-1

Cited by 35 publications

(9 citation statements)

References 9 publications

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“…Genetic programming was used to analyze and predict the surface roughness developed in an end‐milling operation . Genetic programming was employed for surface roughness prediction in zigzag machining and was found more efficient than ANN and ANFIS techniques …”

Section: Discussionmentioning

confidence: 99%

Application of Experimental Design and Analysis of Mathematical Models for Turning Inconel 718 Using Coated Carbide Tools

et al. 2012

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Machining processes have emerged as an important requirement in product design concepts, manufacturing applications, and the overall functional aspects of the product. For machining a component, it is important to understand the characteristics of work material in order to choose the appropriate cutting tool and to fix a set of machining parameters to achieve optimum output. This article presents the details of experiments conducted for machining Inconel 718, by turning process, with two different coated carbide tool inserts (KC5525 and HK150), with an objective of optimizing the process. Furthermore, four different analytical models were developed, validated, and compared to exhibit their performance in establishing the input–output relationship. A set of input machining parameters were chosen to yield a higher material removal rate (MRR), coupled with a moderate surface finish. Experimental data were generated for the chosen set of input parameters and the resultant output parameter and the machining performance of the two tool inserts was compared. With the above experimental data, Analytical models were developed, using genetic programming (GP), artificial neural networks (ANN), adaptive neuro‐fuzzy inference system (ANFIS) and the mathematical regression models with an objective of minimizing the surface roughness while turning Inconel‐718. The effect of machining parameters on the surface roughness was evaluated and the optimum machining condition for minimizing the surface roughness was determined; further the order of influencing input parameters was brought out. Prediction accuracy of the four models was established and the above models were validated, using the different set of experimental data. Comparison of performance of the four models is discussed, extent of prediction accuracy of each model is brought out and the advantages, disadvantages, and limitations of the four models are outlined in this article. This shall be a reference to the machinists to choose appropriate cutting parameters to meet the surface finish requirements demanded by the product designers.

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Section: Discussionmentioning

confidence: 99%

Application of Experimental Design and Analysis of Mathematical Models for Turning Inconel 718 Using Coated Carbide Tools

et al. 2012

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“…It is developed by Hecht Nielsen [22,23]. Research has been carried out to apply CPNs for statistical analysis, function approximation, online measurements, monitoring, and diagnosis [24][25][26][27][28][29][30]. The CPN architecture, illustrated in Fig.…”

Section: Counterpropagation Neural Networkmentioning

confidence: 99%

Intelligent measurements for monitoring and control of glass production furnace for green and efficient manufacturing

Liu

Lyons

Sukanya

et al. 2014

Int J Adv Manuf Technol

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Liquefied petroleum (LP) gas is used as one of the fuel systems for glass production furnaces. This research was conducted to develop an intelligent online measurement system for monitoring and control of LP gas so as to achieve green and efficient manufacturing. LP gas is mixed with air at a desired ratio in order to get a proper specific gravity for glass production. Counterpropagation neural networks (CPNs), which are based on competitive learning, were used in this work. Three inputs, air inlet pressure, air/mixed gas differential pressure, and propane/mixed gas differential pressure, were selected for online measurements of specific gravity for monitoring and control of a glass production furnace. Using a 3×12×1 CPN yields exceedingly successful results for online measurements of specific gravity. An average error of 1.68 %, a minimum error of 0.08 %, and a maximum error of 4.43 % were achieved for online measurements. Control actions can then be taken to achieve much higher energy efficiency which is very important for glass production for green and efficient manufacturing.

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“…Last year many models of cutting process were worked out, but most of them are made for milling, drilling or grinding process [Chang & Lu 2006, Göloğlu & Arslan 2009, Öktem 2009, Prakasvudhisarn 2009, Routara 2009, Suresh & Venkateswara 2005. The analysis of turning process is close to the state from the turn of a century.…”

Section: Conclusion For Cutting Process Modellingmentioning

confidence: 99%

Simulation of Cutting Process – Modeling and Applications

Jabłoński¹

2010

Engineering the Future

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Zigzag machining surface roughness modelling using evolutionary approach

Cited by 35 publications

References 9 publications

Application of Experimental Design and Analysis of Mathematical Models for Turning Inconel 718 Using Coated Carbide Tools

Application of Experimental Design and Analysis of Mathematical Models for Turning Inconel 718 Using Coated Carbide Tools

Intelligent measurements for monitoring and control of glass production furnace for green and efficient manufacturing

Simulation of Cutting Process – Modeling and Applications

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