Ground surface roughness prediction based upon experimental design and neural network models

Fredj, Nabil Ben; Amamou, Ridha

doi:10.1007/s00170-005-0169-8

Cited by 47 publications

(17 citation statements)

References 25 publications

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“…A second-order response model for the geometric error is developed and the utilization of the response surface model is evaluated with constraints of the surface roughness and the material removal rate. Fredj and Amamou (2006) have tried to establish a model combining the application of design of experiments (DOE) and neural network method for ground surface roughness prediction. Kwak et al (2006) have developed a model for grinding power spent during the process and the surface roughness in the external cylindrical grinding of hardened SCM440 steel using the response surface method.…”

Section: Review Of Roughness Study In Machiningmentioning

confidence: 99%

Fundamental Consideration

Sahoo¹,

Barman²,

Davim³

2011

Fractal Analysis in Machining

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The importance and usefulness of fractal dimension in describing surface roughness over the conventional roughness parameters are presented in this chapter. The fundamental of fractal dimension and the methodology for evaluation of fractal dimension are also discussed. Literature survey is carried out for four different types of machining processes and shows that there is scarcity of literatures which deal with fractal description of surface roughness. Fundamentals of design of experiments and response surface methodology are also discussed. IntroductionSurfaces are irregular though they may look like very smooth. When the surfaces are magnified, the irregularities become prominent. This is true for the machining surfaces as well. In a material removal process such as machining, unwanted material is removed and altered surface topography is obtained. The surface generated consists of inherent irregularities left by the cutting tool, which are commonly defined as surface roughness. Such a surface is composed of a large number of length scales of superimposed roughness that are generally characterized by the standard deviation of surface peaks. Three statistical characteristics are generally used to describe the structure of machined surface topography: texture, waviness and roughness. The texture determines the anisotropic property of the surface. The waviness reflects the reference profile (or surface). The surface roughness is formed by the micro deformation during the machining process.Surface roughness plays an important role. It has large impact on the mechanical properties like fatigue behavior, corrosion resistance, creep life, etc.It also affects other functional attributes of machine components like friction, wear, light reflection, heat transmission, lubrication, electrical conductivity, etc. Surface roughness may depend on various factors like machining parameters, work-piece materials, cutting tool properties, cutting phenomenon, etc. In a review P. Sahoo et al.,

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Section: Review Of Roughness Study In Machiningmentioning

confidence: 99%

Fundamental Consideration

Sahoo¹,

Barman²,

Davim³

2011

Fractal Analysis in Machining

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show abstract

“…Those models, however, do not take into account any imperfections in the process, such as tool vibration or chip adhesion, according to Sharma et al (2008). In some cases, practical results diverge from theoretical predictions (Zhong, Khoo and Han, 2006;Fredj and Amamou, 2006).…”

Section: Surface Roughnessmentioning

confidence: 99%

“…In other cases, a 'one-factor-at-a-time' technique is used in the search for a suitable configuration (Fredj and Amamou 2006;Kohli and Dixit, 2005).…”

Section: Network Topology Definitionmentioning

confidence: 99%

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

Pontes¹,

Silva²,

Ferreira³

et al. 2010

J. Braz. Soc. Mech. Sci. & Eng.

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“…The application of signals from the cutting process became a more interesting tool when neural networks are used for their processing and interpretation. This tool has attracted interest of several researchers in the surface roughness prediction (Wang, 2005;Aguiar et al, 2006;Kwak et al, 2006;Fredj et al, 2002). Artificial neural networks have been studied for many years in the hope of achieving the human-like performance in the field of the speech, image recognition and the pattern classification.…”

Section: Literature Reviewmentioning

confidence: 99%