Determination of the surface topography of ball end micro milled material measures

Groß, Katja; Eifler, Matthias; Klauer, Katja; Huttenlochner, Katharina; Kirsch, Benjamin; Ziegler, Christiane; Aurich, Jan C.; Seewig, Jörg

doi:10.1016/j.jestch.2020.08.005

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…In the floor surface topography is effected by the minimum chip thickness, the elastic recovery, and the transverse vibrations. Groß et al [40] Experimental Brass (CuZn39Pb3)…”

Section: Methodsmentioning

confidence: 99%

“…While, microgeometry is the deviation of the cutting edge geometry from the ideal sharp tool. It is characterized by the geometric features Recently, Groß et al [40] presented the surface generation model that uses several different techniques such as integral approach, correlation analysis and Fourier analysis in order to investigate surface topography details in a smaller order of magnitude. Evaluation results showed the correlation between surface texture parameters and machining parameters.…”

Section: Tool Deflection and Surface Roughness Modelingmentioning

confidence: 99%

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Micro milling process modeling: a review

Mamedov

2021

Manufacturing Rev.

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The trend towards miniature manufacturing in high technological fields like bioengineering, electronics and aerospace has increased dramatically over the last decade. Many methods of micro manufacturing have been researched and applied to manufacture small scale components. Among these manufacturing methods micro-mechanical machining methods have shown themselves to be attractive alternatives. Micro milling is one of the most frequently used micro-mechanical machining method with high potential for the precise manufacturing of complex parts. The aim of this work is to present the principal aspects related to micro milling technology, with emphasis on process modeling and quality of the finished product. A general view on process modeling starting from chip thickness models up to tool and workpiece machining induced distortion models is depicted. Specifically, different modeling techniques related to modeling of micro milling process are evaluated and important aspects that authors revealed during their research are presented. Finally, implications are discussed and suggestions for future research are presented.

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“…In the floor surface topography is effected by the minimum chip thickness, the elastic recovery, and the transverse vibrations. Groß et al [40] Experimental Brass (CuZn39Pb3)…”

Section: Methodsmentioning

confidence: 99%

Section: Tool Deflection and Surface Roughness Modelingmentioning

confidence: 99%

Micro milling process modeling: a review

Mamedov

2021

Manufacturing Rev.

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“…Torres et al [27] proposed a structured numerical simulation using several friction coefficient approximations, and adapted it to a pre-textured martensitic precipitation hardening stainless-steel. Cross et al [28] investigated the surface topography of material measures using confocal microscope and atomic force microscope measurements, and it was found that the surface topography depends primarily on the spindle speed and much less on the feed per tooth, whereas differences between the milling tools are observed in the tendency to form substructures. Klauer et al [29] found that the ploughing of the material instead of a cutting occurs when too-small tilt angles are applied, and investigated the tilt angle's influence on the resulting roughness and machined geometry using micro-milling sinusoidal freeform surfaces at different tilt angles.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Optimization of Machined Surface Topography in Ball-End Milling Process

Wang,

Zhao,

Tan

et al. 2024

Materials

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In order to optimize machined surface topography, this paper presents a novel algorithm for simulating the surface topography and predicting the surface roughness of a ball-end milling process. First, a discrete workpiece model was developed using the Z-map method, and the swept surface of a cutter edge was represented using triangular approximation. The workpiece surface was updated (i.e., material removal process) using the intersection between the vertical reference line and the triangular facet under a cutting judgement. Second, the proposed algorithm was verified by comparing the simulated 3D surface topography as well as 2D surface profile and average roughness (Sa) with experimental measurements. Then, numerical simulation examples planed by the Box–Behnken design methods were carried out to investigate the Sa in the ball-end milling operation. The correlations of Sa and cutting parameters were represented by a response surface reduced quadratic model based on the ANOVA results. Finally, the feed per tooth, radial depth of cut, and tilt and lead angles were optimized for improving the machining efficiency under the Sa constraints. This study presents an effective method for simulating surface topography and predicting the Sa to optimize the cutting parameters during ball-end milling process.

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