Hybrid approach to surface roughness evaluation in multistage machining processes

Grzesik, Wit; Brol, S.

doi:10.1016/s0924-0136(02)01105-6

Cited by 50 publications

(20 citation statements)

References 14 publications

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“…The term 'fractal' was first introduced by Benoît Mandelbrot in 1960 (Mandelbrot, 1989) and over the past few decades it was adapted as an important parameter for surface texture analysis (Terry and Brown, 1997). (Grzesik and Brol, 2003) showed that fractal dimensions are tightly correlated with Ra, Rz and can be used as tools for controlling surface finish in a complex, multistage machining process.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and systematic selection of significant multi-scale surface roughness parameters (SRPs) as process monitoring index

Das

Linke

2017

Journal of Materials Processing Technology

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The evaluation of multi-scale surface roughness parameters (SRPs) is important to solve many engineering problems (e.g. contact stress, sealing, friction) and is closely related to further fundamental problems (e.g. microbial contamination). Traditionally, surface roughness has been used as a standard for indicating process performance, such as tool wear, tool vibration etc. This paper also aims to find appropriate surface roughness parameters (SRPs) that can be used as process monitoring indices. Grade 304 stainless steel surfaces, generated by extrusion and grinding processes, were used in this study. The evaluation of different SRPs and their topography properties (such as fractal dimension) is discussed for extruded and ground surfaces.One problem with existing surface metrology is the availability of a multitude of disconnected roughness parameters. A statistical approach is presented in this paper that allows the most appropriate roughness parameters to monitor whether the intended surface quality converges to be found.

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Section: Introductionmentioning

confidence: 99%

Evaluation and systematic selection of significant multi-scale surface roughness parameters (SRPs) as process monitoring index

Das

Linke

2017

Journal of Materials Processing Technology

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“…For successful application of these composites in different fields, machining of the components is necessary. The surface roughness is a quality indicator of surface characteristics of machined parts and it influences many properties of material [4].…”

Section: Introductionmentioning

confidence: 99%

Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool

Rajasekaran

Palanikumar²,

Vinayagam

2011

Prod. Eng. Res. Devel.

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In recent days, carbon fiber reinforced polymer (CFRP) composites play a vital role in various engineering and technological applications. They are replacing conventional materials due to their excellent properties. Tubes made of these materials are made up of either hand layup process or filament winding processes and are widely used in aircraft, automobile, sports industries, etc., The objective of this study is to examine the influence of machining parameters combination so as to obtain a good surface finish in turning of CFRP composite by cubic boron nitride (CBN) cutting tool and to predict the surface roughness values using fuzzy modeling. The results indicate that the fuzzy logic modeling technique can be effectively used for the prediction of surface roughness in machining of CFRP composites.

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“…Back propagation neural network, proposed by Rumelhart, Hilton, and Williams (1986), have been successfully applied by Sathyanarayanan, Lin, and Chen (1992) and Jain, Jain, and Kalra (1999), and Feng, Wang, and Yu (2002) for modelling a typical creep feed super alloy-grinding, prediction of material removal rate and surface finish parameter of a typical abrasive flow machining, and a honing operation of engine cylinder liners, respectively. Grzesik & Brol (2003) show the usefulness of ANN modelling for controlling surface finish characteristics in multistage machining processes.…”

Section: Artificial Neural Network (Ann)-based Modellingmentioning

confidence: 96%

A review of optimization techniques in metal cutting processes

Mukherjee

Ray

2006

Computers & Industrial Engineering

459

197

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Hybrid approach to surface roughness evaluation in multistage machining processes

Cited by 50 publications

References 14 publications

Evaluation and systematic selection of significant multi-scale surface roughness parameters (SRPs) as process monitoring index

Evaluation and systematic selection of significant multi-scale surface roughness parameters (SRPs) as process monitoring index

Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool

A review of optimization techniques in metal cutting processes

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