Selection of optimal MQL and cutting conditions for enhancing machinability in turning of brass

2008 ◽  
Vol 204 (1-3) ◽  
pp. 459-464 ◽  
Author(s):  
V.N. Gaitonde ◽  
S.R. Karnik ◽  
J. Paulo Davim
Keyword(s):  
Cutting Conditions ◽  
Selection Of

Optimization of Cutting Conditions for Turning of Ductile Materials to Achieve Favourable Chip Form

2018 ◽  
Vol 780 ◽  
pp. 98-104
Author(s):  
Alexander Belyakov ◽  
Stanislav Mikhailov ◽  
Nikolai Kovelenov ◽  
Sergey Danilov
Keyword(s):  
Cutting Parameters ◽  
Cutting Conditions ◽  
Optimal Conditions ◽  
Materials Selection ◽  
Ductile Materials ◽  
Optimal Cutting Parameters ◽  
Selection Of

A technique and supporting software were designed to select optimal conditions for turning of ductile materials. Selection of optimal cutting parameters is based on a number of process requirements, including achieving the favourable chip form.

scholarly journals Cutting Force in Stone Machining by Diamond Disk

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
S. Turchetta
Keyword(s):  
Cutting Force ◽  
Process Parameters ◽  
Chip Thickness ◽  
Empirical Models ◽  
Cutting Conditions ◽  
Cutting Process ◽  
Diamond Disk ◽  
Cutting Energy ◽  
Removal Process ◽  
Selection Of

Stone machining by diamond disk is a widespread process to manufacture standard products, such as tiles, slabs, and kerbs. Cutting force and energy may be used to monitor stone machining. Empirical models are required to guide the selection of cutting conditions. In this paper, the effects of cutting conditions on cutting force and cutting energy are related to the shape of the idealized chip thickness. The empirical models developed in this paper can be used to predict the variation of the cutting energy. Therefore these models can be used to guide the selection of cutting conditions. The chip generation and removal process has been quantified with the intention of assisting both the toolmaker and the stonemason in optimising the tool composition and cutting process parameters, respectively.

Selection of Optimal Tool Geometry and Cutting Conditions Using a Surface Roughness Prediction Model for Hard Turning

2008 ◽  
Author(s):  
Dilbag Singh ◽  
P. Venkateswara Rao
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Hard Turning ◽  
Second Order ◽  
Cutting Conditions ◽  
Tool Geometry ◽  
Surface Roughness Prediction ◽  
Machining Conditions ◽  
Roughness Prediction ◽  
Selection Of

Due to technical and economical factors, hard turning is competing successfully with the grinding process in the industries. However, due to the large number of variables and their interactions affecting the hard turning process, the process control becomes complex. So, the selection of optimal machining conditions for good surface quality, in hard turning, is of great concern in the manufacturing industries these days. In the present work, experimental investigation has been conducted to study the effect of the tool geometry (effective rake angle and nose radius) and cutting conditions (cutting speed and feed) on the surface roughness during the hard turning of the bearing steel with mixed ceramic inserts. Central composite design was employed for experimentation. The first and the second order mathematical models were developed in terms of machining parameters by using the Response Surface Methodology (RSM) on the basis of the experimental results. Results show that all the factors and their interactions were significantly influencing the surface roughness. Analysis of Variance (ANOVA) indicated that the second order surface roughness model was significant. Further, the surface roughness prediction model has been optimized by using genetic algorithms (GA). The genetic algorithm program gives minimum values of surface roughness and their respective optimal machining conditions (cutting conditions and tool geometry).

scholarly journals Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

2009 ◽  
Vol 22 (4) ◽  
pp. 491-504 ◽  
Author(s):  
Sukhomay Pal ◽  
P. Stephan Heyns ◽  
Burkhard H. Freyer ◽  
Nico J. Theron ◽  
Surjya K. Pal
Keyword(s):  
Tool Wear ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Optimum Cutting ◽  
Wear Monitoring ◽  
Input Uncertainties ◽  
Selection Of

Multi-Objective Selection of Cutting Conditions in Advanced Machining Processes via an Efficient Global Optimization Approach

2014 ◽  
Author(s):  
Mohamed Aly ◽  
Karim Hamza ◽  
Mohammed Tauhiduzzaman ◽  
Mouhab Meshreki ◽  
Ashraf O. Nassef ◽  
...  
Keyword(s):  
Global Optimization ◽  
High Speed ◽  
Diamond Turning ◽  
Cutting Conditions ◽  
Efficient Global Optimization ◽  
Experimental Sample ◽  
Machining Processes ◽  
Multi Objective ◽  
Ultra Precision ◽  
Selection Of

Optimum selection of cutting conditions in high-speed and ultra-precision machining processes often poses a challenging task due to several reasons; such as the need for costly experimental setup and the limitation on the number of experiments that can be performed before tool degradation starts becoming a source of noise in the readings. Moreover, oftentimes there are several objectives to consider, some of which may be conflicting, while others may be somewhat correlated. Pareto-optimality analysis is needed for conflicting objectives; however the existence of several objectives (high-dimension Pareto space) makes the generation and interpretation of Pareto solutions difficult. The approach adopted in this paper is a modified multi-objective efficient global optimization (m-EGO). In m-EGO, sample data points from experiments are used to construct Kriging meta-models, which act as predictors for the performance objectives. Evolutionary multi-objective optimization is then conducted to spread a population of new candidate experiments towards the zones of search space that are predicted by the Kriging models to have favorable performance, as well as zones that are under-explored. New experiments are then used to update the Kriging models, and the process is repeated until termination criteria are met. Handling a large number of objectives is improved via a special selection operator based on principle component analysis (PCA) within the evolutionary optimization. PCA is used to automatically detect correlations among objectives and perform the selection within a reduced space in order to achieve a better distribution of experimental sample points on the Pareto frontier. Case studies show favorable results in ultra-precision diamond turning of Aluminum alloy as well as high-speed drilling of woven composites.

Sign in / Sign up

Export Citation Format

Share Document