Experimental development of RSM techniques for surface quality prediction in metal cutting applications

Prete, Antonio Del; Vitis, A. A. De; Spagnolo, A.

doi:10.1007/s12289-010-0809-x

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…If simple geometrical equations exist to estimate the mean surface roughness (R a ) and mean surface roughness depth (R z ), being able to predict them over a wide range of cutting parameters requires some other approaches. Response Surface Methodology (RSM) (Del Prete et al 2010a) and ANOVA (Saffioti et al 2021b) were commonly applied to generate empirical models and assess the most influential parameters. In broaching, Makarov et al (2008) and Arrieta et al (2017) showed that surface roughness was also highly dependent on the microstructure of the material especially due to the small uncut chip thickness compared to the grain size.…”

Section: Prediction Of Surface Integritymentioning

confidence: 99%

Mini symposium on cutting and machining: 25 years of ESAFORM activity

et al. 2022

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This paper reports on the state of the art in the experimental and numerical investigations of cutting and machining processes. The contributions on the above-mentioned processes and published on the Proceedings of the European Scientific Association for material FORMing (ESAFORM) Conferences are highlighted. In particular, this literature review is an update of a previous one conducted in the 2007, after ten years of the ESAFORM activities, and it confirms the crucial role played by the minisymposium on Machining and Cutting in this field. In fact, the research has been quite active even in these last fifteen years, as demonstrated by the number of contributions and their relevant scientific contents. As overall, this review shows as the minisymposium on Machining and Cutting, that has been organized since 2001 with no interruptions, has contributed to the scientific progress on the study of the material removal processes.

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Section: Prediction Of Surface Integritymentioning

confidence: 99%

Mini symposium on cutting and machining: 25 years of ESAFORM activity

et al. 2022

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“…[10]. The roughness on worked surfaces have been detected with Mahr's Perthometer Concept 6.5 profilometer.…”

Section: Experimental Equipmentsmentioning

confidence: 99%

Roughness improvement in machining operations through coupled metamodel and genetic algorithms technique

2010

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Due to the widespread use of highly automated machine tools, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining Al7075-T6, using Response Surface Methodology (RSM). Machining operations of work pieces made by Al7075-T6 covering a wide range of machining conditions have been carried out with by flat end mill with four teeth made by High Speed Steel. A RS model, in terms of machining parameters, was developed for surface roughness prediction using the Radial Basis Functions (RBF) technique. This model gives the process response sensitivity to the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA).

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