scholarly journals A study on the machining parameters optimization of micro-end milling process

2012 ◽  
Vol 3 (6) ◽  
Author(s):  
PR Periyanan ◽  
U Natarajan ◽  
SH Yang
Keyword(s):  
End Milling ◽  
Milling Process ◽  
Parameters Optimization ◽  
Machining Parameters ◽  
Machining Parameters Optimization ◽  
Micro End Milling ◽  
End Milling Process

Research on the Influence Factors for the Deflection of Micro-Ball-End Cutter in Micro-End-Milling Process

2011 ◽  
Vol 697-698 ◽  
pp. 84-87 ◽  
Author(s):  
Ming Jun Chen ◽  
Zhi Jun Wang ◽  
Chun Ya Wu ◽  
Hai Bo Ni
Keyword(s):  
End Milling ◽  
Three Dimensional ◽  
Great Influence ◽  
Milling Process ◽  
Micro Milling ◽  
Machining Parameters ◽  
Cutting Depth ◽  
Cutter Deflection ◽  
Micro End Milling ◽  
End Milling Process

Machining parameters and spindle radial runout have great influence on the micro-ball-end cutter deflection in the micro-end-milling process. In this study, a 3D (three-dimensional) thermal-mechanical FEM (finite element method) model of micro-milling with non-rigid cutter is built to study how radial runout, cutting depth, feed and spindle speed influence the cutter deflection when feed has the same direction with the spindle radial runout. Cutter deflection under different groove lengths, cutting depths, feeds and spindle speeds is investigated, which shows that cutter deflection increases with spindle radial runout significantly. The largest deflections with runout of 2μm are 3.26μm, 3.26μm, 4.71μm and 4.52μm respectively under the adopted machining conditions, while the largest deflections without runout are 1.85μm, 1.85μm, 2.26μm and 3.79μm respectively. It is also shown that the runout effect increases with groove length, cutting depth, while it decreases with feed.

Dynamic cutting force prediction for micro end milling considering tool vibrations and run-out

2018 ◽  
Vol 233 (7) ◽  
pp. 2248-2261 ◽  
Author(s):  
Xuewei Zhang ◽  
Tianbiao Yu ◽  
Wanshan Wang
Keyword(s):  
Cutting Force ◽  
Cutting Forces ◽  
End Milling ◽  
Chip Thickness ◽  
Milling Process ◽  
Force Model ◽  
Dynamic Cutting Force ◽  
Micro End Milling ◽  
Tool Vibrations ◽  
End Milling Process

An accurate prediction of cutting forces in the micro end milling, which is affected by many factors, is the basis for increasing the machining productivity and selecting optimal cutting parameters. This paper develops a dynamic cutting force model in the micro end milling taking into account tool vibrations and run-out. The influence of tool run-out is integrated with the trochoidal trajectory of tooth and the size effect of cutting edge radius into the static undeformed chip thickness. Meanwhile, the real-time tool vibrations are obtained from differential motion equations with the measured modal parameters, in which the process damping effect is superposed as feedback on the undeformed chip thickness. The proposed dynamic cutting force model has been experimentally validated in the micro end milling process of the Al6061 workpiece. The tool run-out parameters and cutting forces coefficients can be identified on the basis of the measured cutting forces. Compared with the traditional model without tool vibrations and run-out, the predicted and measured cutting forces in the micro end milling process show closer agreement when considering tool vibrations and run-out.

Application of Teaching

2015 ◽  
Vol 2 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Nandkumar N. Bhopale ◽  
Nilesh Nikam ◽  
Raju S. Pawade
Keyword(s):  
Tool Path ◽  
End Milling ◽  
Optimization Technique ◽  
Milling Process ◽  
Parameters Optimization ◽  
Machining Parameters ◽  
Machining Processes ◽  
Conflicting Objectives ◽  
Advanced Machining ◽  
Teaching Learning

Recently advanced machining processes are widely used by manufacturing industries in order to produce high quality precise and very complex products. These advanced machining processes involve large number of input parameters which may affect the cost and quality of the products. Selection of optimum machining parameters in such advanced machining processes is very important to satisfy all the conflicting objectives of the process. This algorithm is inspired by the teaching-learning process and it works on the effect of influence of a teacher on the output of learners in a class. This paper presents the application of Response Surface Methodology coupled with newly developed advanced algorithm Teaching Learning Based Optimization Technique (TLBO) is applied for the process parameters optimization for ball end milling process on Inconel 718 cantilevers. The machining and tool related parameters like spindle speed, milling feed, workpiece thickness and workpiece inclination with tool path orientation are optimized with considerations of multiple response like deflection, surface roughness, and micro hardness of plate.

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