Surface Roughness Modelling in Face Milling

Felhő, Csaba; Karpuschewski, Bernhard; Kundrák, János

doi:10.1016/j.procir.2015.03.075

Cited by 62 publications

(19 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many researchers have studied surface roughness in face milling (Baek et al 1997;Adamczak et al 2009;Miko and Nowakowski 2012;Arizmendi et al 2009;Rosales et al 2010;Muñoz-Escalona and Maropoulos 2015;Felho et al 2015;Zhenyu et al 2015;Moghaddam and Kolahan 2016;Popov and Schindelarz 2017;Jersák and Simon 2017). Baek et al (1997) presented a mathematical model for surface roughness prediction taking into account the dynamic characteristics of the face-milling operation.…”

Section: Introductionmentioning

confidence: 99%

“…Muñoz-Escalona and Maropoulos (2015) designed a geometrical model for the prediction of surface roughness in face milling with square insert tools. Felho et al (2015) presented the estimated relations between the calculated theoretical and measured real roughness values, allowing researchers to predict the machined surface roughness. Zhenyu et al (2015) suggested an algorithm for predicting surface roughness that takes into account the influence of both static and dynamic factors on the roughness of the face milled surface.…”

Section: Introductionmentioning

confidence: 99%

“…Jersák and Simon (2017) studied the influence of cooling lubricants on surface roughness and the energy efficiency of cutting machine tools for the three following techniques: lubricantfree cutting, cutting with the use of a lubricant with the MQL technique, and only utilizing finish-turning and finish-face milling. However, no account of the impact of face mill wear on surface roughness as it forms has been found in these references (Baek et al 1997;Adamczak et al 2009;Miko and Nowakowski 2012;Arizmendi et al 2009;Rosales et al 2010;Muñoz-Escalona and Maropoulos 2015;Felho et al 2015;Zhenyu et al 2015;Moghaddam and Kolahan 2016;Popov and Schindelarz 2017;Jersák and Simon 2017).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

View full text Add to dashboard Cite

Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N , in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power-that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements. Keywords

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

View full text Add to dashboard Cite

show abstract

“…A maximális érdességet a mart felület különböző pontjaiban mértem, ez alapján javaslom a mart felületet lefedő, párhuzamos síkokban is az érdességmérést. A párhuzamos síkokban -elméletileg azonos értékektől [10] eltérően -azok különbséget mutatnak. A belépő oldalon, ahol ellenirányú a marás, nagyobbak lesznek az érdességi értékek.…”

Section: öSszefoglalásunclassified

Homlokmart felület érdessége az előtolás és a forgácsarány változtatásának hatására rombusz alakú lapka alkalmazásával

Nagy

2020

MDT

View full text Add to dashboard Cite

A felületi minőség hatással van az alkatrészek illeszkedésére, élettartamára és a szerkezeti egységek működési követelményeinek megfelelésére, emiatt kell terveznünk a várható felületi érdességet is. A homlokmarásra jellemző kinematika révén a felület érdessége inhomogén lesz, vagyis a felületet különböző irányokban mérve változó értékeket mutat. A cikkben az előtolás növelésének hatását vizsgáltuk a felületi érdességre, mivel az előtolás növelése a termelékenység fokozásának egy hatékony módja.

show abstract

“…The analyses were carried out using a method for determining the theoretical values of surface roughness measurements [21]. Its essence is the CAD modelling of the machined surface.…”

Section: Determination Of Roughness Values By Cad Modellingmentioning

confidence: 99%

Effects of Setting Errors (Insert Run-Outs) on Surface Roughness in Face Milling When Using Circular Inserts

Felhő

Kundrák

2018

Machines

Self Cite

View full text Add to dashboard Cite

In face milling, the roughness of the machined surface varies due to the movement of the cutting edge. Changes in roughness parameter values in the axis of rotation (symmetry plane) have been examined at a constant depth of cut for symmetrical milling. In this paper, the effect of increasing feed per tooth on the topography of the surface is studied in fly-cutting and in multi-point face milling. The study takes into account the axial run-out of the inserts. Theoretical roughness values were modelled, the real values were tested in experiments and in both cases the impact of the run-out of the cutting edges and the change of the chip cross-section were also taken into account. Based on the performed experiments it can be stated that the accuracy of the introduced roughness prediction method increases with the increase in feed and therefore the application of the method in the case of high-feed milling is particularly effective. The results have also shown that the run-out of the insert significantly effects the roughness of the milled surfaces and therefore the measurement and minimization of these setting errors is essential.

show abstract

Surface Roughness Modelling in Face Milling

Cited by 62 publications

References 10 publications

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Homlokmart felület érdessége az előtolás és a forgácsarány változtatásának hatására rombusz alakú lapka alkalmazásával

Effects of Setting Errors (Insert Run-Outs) on Surface Roughness in Face Milling When Using Circular Inserts

Contact Info

Product

Resources

About