Investigation of the effects of face-milling parameters of ultra-large-scale plane on milling quality

2007 ◽  
Vol 37 (3-4) ◽  
pp. 241-249 ◽  
Author(s):  
Wei-Piao Wu
Keyword(s):  
Large Scale ◽  
Face Milling ◽  
Milling Quality ◽  
Milling Parameters

scholarly journals Nanofluid-based Minimum Quantity Lubrication (MQL) Face Milling of Inconel 625

2019 ◽  
Vol 16 (3) ◽  
pp. 6874-6888
Author(s):  
P. Singh ◽  
J. S. Dureja ◽  
H. Singh ◽  
M. S. Bhatti
Keyword(s):  
Negative Impact ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
Face Milling ◽  
Cutting Fluid ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Base Oil ◽  
Minimum Quantity ◽  
Milling Parameters

Machining with minimum quantity lubrication (MQL) has gained widespread attention to boost machining performance of difficult to machine materials such as Ni-Cr alloys, especially to reduce the negative impact of conventional flooded machining on environment and machine operator health. The present study is aimed to evaluate MQL face milling performance of Inconel 625 using nano cutting fluid based on vegetable oil mixed with multi-walled carbon nanotubes (MWCNT). Experiments were designed with 2-level factorial design methodology. ANOVA test and desirability optimisation method were employed to arrive at optimised milling parameters to achieve minimum tool wear and machined surface quality. Experiments were performed under nanoparticles based minimum quantity lubrication (NMQL) conditions using different weight concentrations of MWCNT in base oil: 0.50, 0.75, 1, 1.25 and 1.5 wt. %; and pure MQL environment (without nanoparticles). The optimal MQL milling parameters found are cutting speed: 47 m/min, table feed rate: 0.05 mm/tooth and depth of cut: 0.20 mm. The results revealed improvement in the surface finish (Ra) by 17.33% and reduction in tool flank wear (VB) by 11.48 % under NMQL face milling of Inconel 625 with 1% weight concentration of MWCNT in base oil compared to pure MQL machining conditions.

A Coupled Model for the Prediction of Surface Variation in Face Milling Large-Scale Workpiece With Complex Geometry

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Shun Liu ◽  
Sun Jin ◽  
Xue-Ping Zhang ◽  
Kun Chen ◽  
Ang Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Coupled Model ◽  
Prediction Method ◽  
Face Milling ◽  
Milling Force ◽  
Important Indicator ◽  
Surface Variation ◽  
Local Stiffness ◽  
Machining Parameter ◽  
Complex Components

Face milling commonly generates surface quality of variation, is especially severe for milling of large-scale components with complex surface geometry such as cylinder block, engine head, and valve body. Thus surface variation serves as an important indicator both for machining parameter selection and components' service performance such as sealing, energy consumption, and emission. An efficient and comprehensive numerical model is highly desired for the prediction of surface variation of entire surface. This study proposes a coupled numerical simulation method, updating finite element (FE) model iteratively based on integration of data from abaqus and matlab, to predict surface variation induced by face milling of large-scale components with complex surfaces. Using the coupled model, three-dimensional (3D) variation of large-scale surface can be successfully simulated by considering face milling process including dynamic milling force, spiral curve of milling trajectory, and intermittently rotating contact characteristics. Surface variation is finally represented with point cloud from iterative FE analysis and verified by face milling experiment. Comparison between measured and predicted results shows that the new prediction method can simulate surface variation of complex components well. Based on the verified model, a set of analyses are conducted to evaluate the effects of local stiffness nonhomogenization and milling force variation on machined surface variation. It demonstrates that surface variation with surface peaks and concaves is strongly correlated with local stiffness nonhomogenization especially in feed direction. And thus the coupled prediction method provides a theoretical and efficient way to study surface variation induced by face milling of large-scale complex components.

scholarly journals Analysis and Modeling of Burr Formation During the Plane Milling of Cast Aluminum Alloy Using Polycrystalline Diamond Tools

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Clément Bourlet ◽  
Guillaume Fromentin ◽  
Elias Harika ◽  
Arnaud Crolet
Keyword(s):  
Large Scale ◽  
Tool Path ◽  
Polycrystalline Diamond ◽  
Face Milling ◽  
Burr Formation ◽  
Geometrical Parameters ◽  
Cast Aluminum Alloy ◽  
Cast Aluminum ◽  
Geometrical Quality ◽  
Local Parameters

Burr formation is a significant problem during manufacturing and leads to a lack of geometrical quality through the appearance of undesired and undefined shapes on the workpiece. Thus, understanding the burr formation and elaborating of predictive models are helpful for process design in order to avoid or to reduce burrs and to optimize the strategies for eventual deburring. This study presents both an experimental approach and a model for the plane milling of openwork parts, where burrs are a significant factor. A large-scale analysis of relevant geometrical parameters and their interactions are performed. A phenomenological burr size model is established considering local parameters and the specificities of 3D cutting in milling. Based on local parameters, this article proposes a new methodology to simulate burr height along any part edge and for most face-milling trajectories. Simulations and validations during tool path exits, with changing local parameters, are presented. In addition to the quantitative approach, new 3D aspects of face milling in relation with exit order sequence (EOS) are developed.

scholarly journals Study Regarding the Optimal Milling Parameters for Finishing 3D Printed Parts from ABS and PLA Materials

2018 ◽  
Vol 70 (1) ◽  
pp. 66-72 ◽  
Author(s):  
Răzvan Gabriel Pămărac ◽  
Radu Emanuil Petruse
Keyword(s):  
Face Milling ◽  
Spindle Speed ◽  
Milling Parameters ◽  
3D Printed ◽  
Finishing Processes

Abstract In this paper we proposed to identify the optimum milling parameter required for finishing processes performed on 3Dprinted parts from ABS and PLA materials. We have identified the optimum milling parameters for a constant spindle speed of 3500 rot/min for face milling and profile contouring operations with different tools diameters. The study was performed on 3D printed specimens from ABS and PLA materials.

Coupled Predicting Surface Variation by Face Milling of Engine Block/Head

2018 ◽  
Author(s):  
Shun Liu ◽  
Sun Jin ◽  
Xueping Zhang ◽  
Changhui Liu ◽  
Fuyong Yang ◽  
...  
Keyword(s):  
Surface Quality ◽  
Large Scale ◽  
Prediction Method ◽  
Experimental Methods ◽  
Face Milling ◽  
Milling Force ◽  
Machined Surface ◽  
Surface Variation ◽  
Local Stiffness ◽  
Complex Components

Face milling commonly generates surface quality of roughness or variation, especially severe for the milling of large-scale components with complex surface geometry such as cylinder block, engine head, and valve body. Thus surface variation serves as an important indicator both for machining parameter selection and components’ service performance. Conversely the optimization of machining process is a vital objective to improve the surface quality and its service life of machined components. Many researchers have dedicated to the prediction of machined surface variation generated by face milling using numerical or experimental methods. However, the numerical methods based on finite element analysis (FEA) are good at predicting local deformation of workpiece under instantaneous milling force, particularly applied for online compensation in face milling. Whereas experimental methods can only be used to estimate whole surface variation through reverse correlation analysis of measured data and processing variables. Therefore, an efficient and comprehensive numerical model is highly desired for the prediction of surface variation of entire surface. This study proposes a coupled numerical simulation method, updating FE model literarily based on the integration of data from ABAQUS and MATLAB, to predict surface variation induced by the face milling of large-scale components with complex surfaces. Using the coupled model, the 3D variation of large-scale surface can be successfully simulated by considering face milling process including dynamic milling force, spiral curve of milling trajectory, and intermittently rotating contact characteristics. Surface variation is finally represented with point cloud from totally iterative FE analysis and verified by face milling experiment. Result shows that the new prediction method can simulate surface variation of complex components. Based on the verified model, a set of numerical analyses are conducted to evaluate the effects of local stiffness non-homogenization and milling force variation on machined surface variation. It demonstrates that surface variation with surface peaks and concaves is strongly correlated with local stiffness non-homogenization especially in feed direction. Thus the coupled prediction method provides a theoretical and efficient way to study surface variation induced by face milling of large-scale complex components.

Study on Computer-Aided Optimizing Milling Parameters of Numerical Control Machine

2009 ◽  
Vol 419-420 ◽  
pp. 833-836
Author(s):  
Yu Xia Zhao ◽  
Hong Hai Xu ◽  
De Wen Gao
Keyword(s):  
Optimization Technique ◽  
Complex Form ◽  
Mathematic Model ◽  
Face Milling ◽  
Numerical Control ◽  
Machining Process ◽  
Modern Theory ◽  
Milling Parameters ◽  
Nc Milling ◽  
The Mathematical Model

In this paper, the modern theory of milling and the optimization technique are applied to the establishment of the mathematical model of face milling to obtain the optimum face milling parameters, in which the highest productivity is the optimizing objective，and many constraints are taking into consideration．The optimization is programmed based on the complex form method and castigatory function method．And a optimization system is established, the system which is composed of optimizing calculation module, special milling parameters database and man-machine interface．And it is programmed by adopting Visual Basic．The NC milling example is optimized，the validity and practicability of optimizing mathematic model is proved，and the base of the optimization of the NC machining process is established．

Multi-objective optimization in face milling process with cryogenic cooling using grey fuzzy analysis and BPNN-GA methods

2019 ◽  
Vol 36 (5) ◽  
pp. 1542-1565 ◽  
Author(s):  
Bobby Oedy Pramoedyo Soepangkat ◽  
Rachmadi Norcahyo ◽  
Bambang Pramujati ◽  
M. Abdul Wahid
Keyword(s):  
Cutting Speed ◽  
Milling Process ◽  
Face Milling ◽  
Cryogenic Cooling ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Content Type ◽  
Fuzzy Analysis ◽  
Milling Parameters ◽  
The Face

Purpose The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42. Design/methodology/approach The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (Fc) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L18 mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method. Findings The optimum SR, cutting force (Fc) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics. Originality/value To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

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