Prediction of Surface Topomorphy and Roughness in Ball-End Milling

Antoniadis, Aristomenis; Savakis, C.; Bilalis, Nikolaos; Balouktsis, A.

doi:10.1007/s00170-002-1418-8

Cited by 68 publications

(50 citation statements)

References 13 publications

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“…From equation (1) it can be clearly seen that an increase in feed per tooth f z causes surface roughness growth, whereas an increase in tool diameter D, causes a decline in surface roughness. It is worth to indicate that during calculation of surface roughness height in directions different from the feed motion v f direction, one should include also the pick feed value [20]. Nevertheless, theoretic models describing alterations of surface roughness parameters in the function of kinematic-geometric parameters often vary from the real surface roughness value, especially for low feed values.…”

Section: Surface Roughness In the Ball End Milling Processmentioning

confidence: 99%

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Surface Texture Analysis After Ball End Milling With Various Surface Inclination of Hardened Steel

Wojciechowski¹,

Twardowski²,

Wieczorowski³

2014

Metrology and Measurement Systems

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In this paper, an analysis of various factors affecting machined surface texture is presented. The investigation was focused on ball end mill inclination against the work piece (defined by surface inclination angle α. Surface roughness was investigated in a 3D array, and measurements were conducted parallel to the feed motion direction. The analysis of machined surface irregularities as a function of frequency (wavelength λ), on the basis of the Power Density Spectrum -PDS was also carried out. This kind of analysis is aimed at valuation of primary factors influencing surface roughness generation as well as its randomness. Subsequently, a surface roughness model including cutter displacements was developed. It was found that plain cutting with ball end mill (surface inclination angle α = 0°) is unfavorable from the point of view of surface roughness, because in cutter's axis the cutting speed v c ≈ 0 m/min. This means that a cutting process does not occur, whereas on the machined surface some characteristics marks can be found. These marks do not appear in case of α ≠ 0°, because the cutting speed v c ≠ 0 on the full length of the active cutting edge and as a result, the machined surface texture is more homogenous. Surface roughness parameters determined on the basis of the model including cutter displacements are closer to experimental data for cases with inclination angles α ≠ 0°, in comparison with those determined for plain cutting (α = 0°). It is probably caused by higher contribution in surface irregularities generation of plastic and elastic deformations cumulated near the cutter's free end than kinematic and geometric parameters, as well as cutter displacements.

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Section: Surface Roughness In the Ball End Milling Processmentioning

confidence: 99%

“…Nevertheless, majority of research related to surface roughness generated in ball end milling focuses only on a kinematic-geometric basic model [19,20]. For that reason this paper comprises the analysis of surface roughness generated in ball end milling including surface inclination angle, cutter's run out and minimum uncut chip thickness.…”

Section: Introductionmentioning

confidence: 99%

Surface Texture Analysis After Ball End Milling With Various Surface Inclination of Hardened Steel

Wojciechowski¹,

Twardowski²,

Wieczorowski³

2014

Metrology and Measurement Systems

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“…Their methodology is integrated to the simulation module of a manufacturing simulation package and case studies are presented to show validity. Antoniadis et al [32] presented a novel methodology known as milling software needle programme (MSN) to determine resulting surface roughness for ball end milling. It is claimed in their study that accurate machining simulations are performed whilst great precision is also achieved regarding tool kinematics.…”

Section: Introductionmentioning

confidence: 99%

Development of a software-automated intelligent sculptured surface machining optimization environment

Fountas

Vaxevanidis

Stergiou

et al. 2014

Int J Adv Manuf Technol

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To ensure the quality of machined products at minimum cost and maximum effectiveness, it is crucial that selection of optimum machining parameters should be done when computer numerically controlled (CNC) machine tools technology is employed. Traditionally, experience of the operator plays a major role in the selection of efficient parameter values; however, attaining optimum ones each time by even skilled end users, is extremely difficult. This paper takes advantage of the possibilities of current computer-aided design (CAD)/computer-aided manufacturing (CAM) technology and implements a genetic algorithm for optimising CNC machining operations mainly for sculptured surfaces. The algorithm has been developed as a hosted application to a cutting-edge CAD/CAM system. Collaboration among applications has been achieved through programming for software automation by utilising the application programme interface of the system. The approach was implemented to a group of test sculptured models with different properties whilst one of them has been actually machined using typical resources. Results obtained after the implementation indicated that the methodology is capable of providing optimum values for process parameters on its way to maintain both productivity and high quality.

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“…Antoniadis et al, (2003) proposed a Milling Software Needle(MSN) program to predict the surface roughness of ball-end milling. Gao et al, (2006) simulated the surface topography of end and ball-end milling by considering the trajectory equations of the cutting edge relative to the workpiece.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction of end milling process based on IPSO-LSSVM

Duan

Hao

2014

JAMDSM

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Surface roughness is a significant index in evaluating workpiece quality. So research about predicting surface roughness precisely prior to machining is necessary in order to save cost and attain high productivity levels. In this paper, a method called improved particle swarm optimization-least square support vector machine (IPSO-LSSVM) is proposed to predict the surface roughness of end milling. Firstly, an improved particle swarm optimization(IPSO) algorithm is used to optimize the parameters of LSSVM method which have significant influence on the accuracy of LSSVM model. Secondly, a surface roughness prediction model is established through LSSVM method with the optimized parameters. Then prediction accuracy of the established model can be attained through test data. Finally, the prediction accuracy of IPSO-LSSVM method is compared with the accuracy of other methods, and the results show that IPSO-LSSVM method is competent in fields of surface roughness prediction.

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Prediction of Surface Topomorphy and Roughness in Ball-End Milling

Cited by 68 publications

References 13 publications

Surface Texture Analysis After Ball End Milling With Various Surface Inclination of Hardened Steel

Surface Texture Analysis After Ball End Milling With Various Surface Inclination of Hardened Steel

Development of a software-automated intelligent sculptured surface machining optimization environment

Surface roughness prediction of end milling process based on IPSO-LSSVM

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