Parameter Space Decomposition for Selection of the Axial and Radial Depth of Cut in Endmilling

2000 ◽  
Vol 123 (4) ◽  
pp. 654-664 ◽  
Author(s):  
J. A. Stori ◽  
P. K. Wright

Feeds and speeds for conventional endmilling operations have been empirically investigated and extensively tabulated 1. However, the selection of the geometric cutting parameters, the axial and radial depths of cut, remains an inexact science. Observation of mechanistic process simulation predictions reveal a relatively complex topology resulting from the multiple cutting flutes of conventional endmilling cutters as the axial and radial depths of cut are varied. A partitioning approach is presented that explicitly enumerates the transition events due to the entrance and exit of the individual cutting flutes. The resulting simplified optimization formulation permits selection of the axial and radial depth of cut that most efficiently satisfy critical simulation predictions such as maximum cutting force or form error. Case studies are presented illustrating the application of the method to select the cutting parameters in climb milling. The optimization objective in the case studies is to maximize the material removal rate, subject to the process induced constraints. Results suggest that operating at the extremes of either axial or radial engagement may in various instances be preferable to more conventional combinations of depth and width of cut. Certain regions of the parameter space are observed to be necessarily sub-optimal relative to particular planning constraints, while other regions are found to contain particularly attractive operating points.

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.


2016 ◽  
Vol 78 (6-9) ◽  
Author(s):  
Mohd Shahfizal Ruslan ◽  
Kamal Othman ◽  
Jaharah A.Ghani ◽  
Mohd Shahir Kassim ◽  
Che Hassan Che Haron

Magnesium alloy is a material with a high strength to weight ratio and is suitable for various applications such as in automotive, aerospace, electronics, industrial, biomedical and sports. Most end products require a mirror-like finish, therefore, this paper will present how a mirror-like finishing can be achieved using a high speed face milling that is equivalent to the manual polishing process. The high speed cutting regime for magnesium alloy was studied at the range of 900-1400 m/min, and the feed rate for finishing at 0.03-0.09 mm/tooth. The surface roughness found for this range of cutting parameters were between 0.061-0.133 µm, which is less than the 0.5µm that can be obtained by manual polishing. Furthermore, from the S/N ratio plots, the optimum cutting condition for the surface roughness can be achieved at a cutting speed of 1100 m/min, feed rate 0.03 mm/tooth, axial depth of cut of 0.20 mm and radial depth of cut of 10 mm. From the experimental result the lowest surface roughness of 0.061µm was obtained at 900 m/min with the same conditions for other cutting parameters. This study revealed that by milling AZ91D at a high speed cutting, it is possible to eliminate the polishing process to achieve a mirror-like finishing.


Author(s):  
Atul Tiwari ◽  
Mohan Kumar Pradhan

To assure desire quality of machined products at minimum machining costs and maximum material removal rate, it is very important to select optimum parameters when metal cutting machine tool are used. Minimum Surface Roughness (Ra) is commonly desirable for the component; however Material Removal Rate (MRR) should be maximized. This chapter presents an approach for determination of the best cutting parameters precede to minimum Ra and maximum MRR simultaneously by integrating Response Surface Methodology with Multi-Objective Technique for Order Preference by Similarity to Ideal Solution and Teaching and learning based optimization algorithm in face milling of Al-6061 alloy. 30 experiments have been conducted based on RSM with 4 parameters, namely Speed, Feed, Depth of Cut and Coolant Speed and three levels each. ANOVA is performed to find the most influential input parameters for both MRR and Ra. Later the multi-objective attribution selection method TOPSIS and multi objective optimization method TLBO is used to optimize the responses.


Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2998 ◽  
Author(s):  
Kubilay Aslantas ◽  
Mohd Danish ◽  
Ahmet Hasçelik ◽  
Mozammel Mia ◽  
Munish Gupta ◽  
...  

Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.


Author(s):  
B.S. Raju ◽  
U. Chandrasekhar ◽  
L.H. Manjunath

Accompanying the development of mechanical industry, the demand for alloy materials having high hardness, toughness and impact resistance are increasing. Nevertheless, such materials are difficult to be machined by traditional machining method. Hence CNC machines are used to machine such materials, which are capable of producing fine, precise, corrosion and wear resistance surfaces. The problem of arriving at optimal levels of operating parameters has attracted the attention of the researchers and practicing engineers, for a very long time. Thus, this paper demonstrates the optimization of the process parameter of machining Inconel 718 super alloy material via., the Taguchi methodbased grey analysis. The modified algorithm adopted here was successfully used for both detraining the optimum settings of machine parameters and for combining multiple quality characteristics into one integrated numerical value called grey relational grade. An attempt has been made to identify the influence of various cutting parameters i.e., speed, feed rate and depth of cut on physical part characteristics i.e., metal removal rate and surface roughness. The predictions of optimal process parameter with respect to the response are the end results of the paper.


2009 ◽  
Vol 69-70 ◽  
pp. 490-494 ◽  
Author(s):  
H.Z. Zhang ◽  
Qing Long An ◽  
Yun Shan Zhang ◽  
Gang Liu ◽  
Ming Chen

This paper presents the optimization process of a surface roughness model for the milling 1Cr18Ni9Ti. The model is developed in term of milling speed, feed per tooth and radial depth of cut. Therefore, the regression model predicting formula for surface roughness has been established by means of uniform design of experiment, and then the response surface methodology was applied to generate response contours of surface roughness. The experimental results indicate that the material removal rate can be improved by selecting optimal milling parameters without increasing the surface roughness. Moreover, it is seen that the feed rate is the most significant factor on the surface roughness.


2009 ◽  
Vol 76-78 ◽  
pp. 15-20 ◽  
Author(s):  
Lan Yan ◽  
Xue Kun Li ◽  
Feng Jiang ◽  
Zhi Xiong Zhou ◽  
Yi Ming Rong

The grinding process can be considered as micro-cutting processes with irregular abrasive grains on the surface of grinding wheel. Single grain cutting simulation of AISI D2 steel with a wide range of cutting parameters is carried out with AdvantEdgeTM. The effect of cutting parameters on cutting force, chip formation, material removal rate, and derived parameters such as the specific cutting force, critical depth of cut and shear angle is analyzed. The formation of chip, side burr and side flow is observed in the cutting zone. Material removal rate increases with the increase of depth of cut and cutting speed. Specific cutting force decreases with the increase of depth of cut resulting in size effect. The shear angle increases as the depth of cut and cutting speed increase. This factorial analysis of single grain cutting is adopted to facilitate the calculation of force consumption for each single abrasive grain in the grinding zone.


2009 ◽  
Vol 69-70 ◽  
pp. 418-422
Author(s):  
L.D. Wu ◽  
Cheng Yong Wang ◽  
D.H. Yu ◽  
Yue Xian Song

Hardened steel P20 at 50 HRC is milled at high speed by TiN coated and TiAlN coated solid carbide straight end mills, and the cutting forces and tool wear are measured. The result shows that TiAlN coated tool is more suitable for cutting hardened steel at high speed. Then the hardened steel is milled under different cutting parameters. It is indicated that the effect of cutting speed on cutting forces is small, but the effect of cutting speed on machine vibration should be considered. Increase feed per tooth or radial depth of cut will increase the cutting forces.


2021 ◽  
Vol 8 ◽  
pp. 17
Author(s):  
Gururaj Bolar ◽  
Shrikrishna Nandkishor Joshi

The selection of optimal process parameters is essential while machining thin-wall parts since it influences the quality of the product and affects productivity. Dimensional accuracy affects the product quality, whereas the material removal rate alters the process productivity. Therefore, the study investigated the effect of tool diameter, feed per tooth, axial and radial depth of cut on wall deflection, and material removal rate. The selected process parameters were found to significantly influence the in-process deflection and thickness deviation due to the generation of unfavorable cutting forces. Further, an increase in the material removal rate resulted in chatter, thus adversely affecting the surface quality during the final stages of machining. Considering the conflicting nature of the two performance measures, Non-dominated Sorting Genetic Algorithm-II was adopted to solve the multi-objective optimization problem. The developed model could predict the optimal combination of process variables needed to lower the in-process wall deflection and maintain a superior surface finish while maintaining a steady material removal rate.


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