scholarly journals Path Interval and Its Relevance to Cutting Force in Ball and Filleted End Milling

2022 ◽  
Vol 16 (1) ◽  
pp. 85-94
Author(s):  
Tsutomu Sekine
Keyword(s):  
Cutting Force ◽  
End Milling

FRACTAL-BASED ANALYSIS OF THE VARIATIONS OF CUTTING FORCES ALONG DIFFERENT AXES IN END MILLING OPERATION

Fractals ◽  
2018 ◽  
Vol 26 (06) ◽  
pp. 1850089 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
ALI AKHAVAN FARID ◽  
TECK SENG CHANG
Keyword(s):  
Fractal Dimension ◽  
Cutting Force ◽  
Cutting Forces ◽  
End Milling ◽  
Fractal Theory ◽  
Radial Force ◽  
Machining Conditions ◽  
Feed Force ◽  
Milling Operation ◽  
Force Signal

Analysis of cutting forces in machining operation is an important issue. The cutting force changes randomly in milling operation where it makes a signal by plotting over time span. An important type of analysis belongs to the study of how cutting forces change along different axes. Since cutting force has fractal characteristics, in this paper for the first time we analyze the variations of complexity of cutting force signal along different axes using fractal theory. For this purpose, we consider two cutting depths and do milling operation in dry and wet machining conditions. The obtained cutting force time series was analyzed by computing the fractal dimension. The result showed that in both wet and dry machining conditions, the feed force (along [Formula: see text]-axis) has greater fractal dimension than radial force (along [Formula: see text]-axis). In addition, the radial force (along [Formula: see text]-axis) has greater fractal dimension than thrust force (along [Formula: see text]-axis). The method of analysis that was used in this research can be applied to other machining operations to study the variations of fractal structure of cutting force signal along different axes.

scholarly journals Influence of Ultrasonic Vibration-Assisted Ball-End Milling on the Cutting Performance of AZ31B Magnesium Alloy

2020 ◽  
Vol 9 (4) ◽  
pp. 271-276
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Ultrasonic Vibration ◽  
Biomedical Applications ◽  
End Milling ◽  
Cutting Temperature ◽  
Cutting Performance ◽  
Machining Process ◽  
Dry Conditions ◽  
Good Biocompatibility

Magnesium alloys have a tremendous possibility for biomedical applications due to their good biocompatibility, integrity and degradability, but their low ignition temperature and easy corrosive property restrict the machining process for potential biomedical applications. In this research, ultrasonic vibration-assisted ball milling (UVABM) for AZ31B is investigated to improve the cutting performance and get specific surface morphology in dry conditions. Cutting force and cutting temperatures are measured during UVABM. Surface roughness is measured with a white light interferometer after UVABM. The experimental results show cutting force and cutting temperature reduce due to ultrasonic vibration, and surface roughness decreases by 34.92%, compared with that got from traditional milling, which indicates UVABM is suitable to process AZ31B for potential biomedical applications.

Influence of cutting speed on cutting force, flank temperature, and tool wear in end milling of Ti-6Al-4V alloy

2013 ◽  
Vol 70 (9-12) ◽  
pp. 1835-1845 ◽  
Author(s):  
Junzhan Hou ◽  
Wei Zhou ◽  
Hongjian Duan ◽  
Guang Yang ◽  
Hongwei Xu ◽  
...  
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
End Milling ◽  
Cutting Speed

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

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