Prediction and modeling of roughness in ball end milling with tool-surface inclination

Bílek, Ondřej; Milde, Radoslav; Strnad, Jan; Žaludek, Milan; Bednařík, Martin

doi:10.1088/1757-899x/726/1/012003

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They have analysed the impact of the tool path on the surface roughness measured in the workpiece areas inclined at different angles. Based on a ANNs and orthogonal arrays, Zhou et al [13] and Bilek et al [14] have developed a method for multicriterial optimization of the cutting tool inclination angle measured in a single plane. Yao et al [15] and De Souza et al [16] have studied the impact of the cutting tool direction and angle during machining of a curvilinear surface using a carbide ball-nosed milling cutter.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Prediction in the Hardened Steel Ball-End Milling by Using the Artificial Neural Networks and Taguchi Method

Matras

2024

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

The paper provides an analysis of the impact of the values of cutting tool inclination strategies and angles measured in the parallel and perpendicular to feed direction, radial depth of cut and feedrate on the surface roughness. The workpiece was made of the AISI H13 steel, hardness 50 HRC, and was machined using a ball-nosed end mill with CBN edges. The research methodology involved experiments conducted based on the Taguchi orthogonal array, optimization of parameters with the use of Taguchi method and process modelling using neural networks. Thanks to the use of neural networks, the analyses were performed for various levels of machining efficiency, obtained as a result of different radial depths of cut and feedrates. In order to obtain mathematical models welldescribing strongly nonlinear impact of the cutting tool inclination strategies and angles, a separate neural network learned for each tool inclination strategy. The prediction of results was made using a set of neural networks. The analyses and experiments resulted in surfaces with very low Ra parameter of 0.16 mm and mathematical models with a good fit to the experimental data. Values of the cutting tool inclination angle that allow obtaining the surface of specific surface roughness were specified for various levels of machining performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Surface Roughness Prediction in the Hardened Steel Ball-End Milling by Using the Artificial Neural Networks and Taguchi Method

Matras

2024

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

show abstract

“…case of the face milling of titanium alloy. Bílek et al [4] show the effect of the inclination of the surface in case of ball-end milling. Matras and Zębala [5] investigate ball-end milling by different strategies in case of hardened tool steel.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Surface Roughness and its Geometric Model in Case of Z-level Milling

Mikó¹

2023

AMS

View full text Add to dashboard Cite

The Z-level milling technology is one of the most often used finishing milling strategy in case of the CNC milling of the steep free form surfaces. A main spindle parallel end-milling cutter can be used with or without corner radius, and the depth of cut means the axis parallel distance between the tool paths. During the planning of machining process two main aspects should be considered: the productivity and the accuracy. The accuracy means macro and micro accuracy. In this article the surface roughness, as a part of micro accuracy, is investigated, and a geometric approach of its estimation is presented. The tool corner geometry, the depth of cut and the inclination of the surface are considered, and the critical inclination is introduced. The theoretical description of the surface roughness is compared with the results of milling tests. As concluded, the cusp height, as the geometric model of the surface roughness can be a good base of the estimation, but a detailed and case depend model is required. The results ensure a better selection of tool path parameters in CAM systems considering the properties of cutting tool and the surface.

show abstract

Prediction and modeling of roughness in ball end milling with tool-surface inclination

Cited by 2 publications

References 14 publications

Surface Roughness Prediction in the Hardened Steel Ball-End Milling by Using the Artificial Neural Networks and Taguchi Method

Surface Roughness Prediction in the Hardened Steel Ball-End Milling by Using the Artificial Neural Networks and Taguchi Method

Comparison of the Surface Roughness and its Geometric Model in Case of Z-level Milling

Contact Info

Product

Resources

About