Optimization of Cutting Conditions Using Regression and Genetic Algorithm in End Milling

2015 ◽  
Vol 20 ◽  
pp. 12-18 ◽  
Author(s):  
Omar Monir Koura ◽  
Ahmed Samy El-Akkad
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
General Equation ◽  
End Milling ◽  
Cutting Conditions ◽  
Good Tool ◽  
Milling Operation ◽  
Optimal Surface ◽  
The Cost ◽  
High Degree

End milling is a key machining operation in industrial world, particularly in manufacturing of dies and similar products. Although, such products require high degree of surface roughness, milling operation is taken to be the enough for the cost wise if considering further finishing operations. Thus optimizing the cutting conditions to achieve the optimal surface roughness is becoming a vital issue. Several authors tackled this problem. In this paper the same case is investigated but with an advanced algorithm using regression and genetic methodology. The results obtained which ended by deducing a general equation combining the effect of various parameters on surface roughness highlighted the factors involved in achieving the surface roughness and proved to be good tool to predict the optimal cutting conditions.

Prediction and Investigation of Surface Response in High Speed End Milling of Ti-6Al-4V and Optimization by Genetic Algorithm

2009 ◽  
Vol 83-86 ◽  
pp. 1009-1015 ◽  
Author(s):  
S. Alam ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari ◽  
Mohamed Konneh
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Flat End Milling ◽  
Five Axis

In this study, statistical models were developed using the capabilities of Response Surface Methodology (RSM) to predict the surface roughness in high-speed flat end milling of Ti-6Al-4V under dry cutting conditions. Machining was performed on a five-axis NC milling machine with a high speed attachment, using spindle speed, feed rate, and depth of cut as machining variables. The adequacy of the model was tested at 95% confidence interval. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting little deviations in surface roughness prediction. A very good performance of the RSM model, in terms of agreement with experimental data, was achieved. It is observed that cutting speed has the most significant influence on surface roughness followed by feed and depth of cut. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the surface roughness in flat end milling of Ti-6Al-4V materials. The developed quadratic prediction model on surface roughness was coupled with the genetic algorithm to optimize the cutting parameters for the minimum surface roughness.

Surface Roughness Identification in End Milling Using Vibration Signals and Fuzzy Clustering

2016 ◽  
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee ◽  
A. H. M. Esfakur Rahman
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Wavelet Transforms ◽  
Fourier Transforms ◽  
End Milling ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machined Surface ◽  
Vibration Signals

The study presented involves the identification of surface roughness in Aluminum work pieces in an end milling process using fuzzy clustering of vibration signals. Vibration signals are experimentally acquired using an accelerometer for varying cutting conditions such as spindle speed, feed rate and depth of cut. Features are then extracted by processing the acquired signals in both the time and frequency domain. Techniques based on statistical parameters, Fast Fourier Transforms (FFT) and the Continuous Wavelet Transforms (CWT) are utilized for feature extraction. The surface roughness of the machined surface is also measured. In this study, fuzzy clustering is used to partition the feature sets, followed by a correlation with the experimentally obtained surface roughness measurements. The fuzzifier and the number of clusters are varied and it is found that the partitions produced by fuzzy clustering in the vibration signal feature space are related to the partitions based on cutting conditions with surface roughness as the output parameter. The results based on limited simulations are encouraging and work is underway to develop a larger framework for online cutting condition monitoring system for end milling.

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

scholarly journals Influence of Chatter of VMC Arising During End Milling Operation and Cutting Conditions on Quality of Machined Surface

1970 ◽  
Vol 2 (1) ◽  
Author(s):  
A.K.M.N. Amin, M.A. Rizal, and M. Razman
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
Metal Removal ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
End Mill ◽  
Machined Surface ◽  
Operating Cycle ◽  
Wide Range

Machine tool chatter is a dynamic instability of the cutting process. Chatter results in poor part surface finish, damaged cutting tool, and an irritating and unacceptable noise. Exten¬sive research has been undertaken to study the mechanisms of chatter formation. Efforts have been also made to prevent the occurrence of chatter vibration. Even though some progress have been made, fundamental studies on the mechanics of metal cutting are necessary to achieve chatter free operation of CNC machine tools to maintain their smooth operating cycle. The same is also true for Vertical Machining Centres (VMC), which operate at high cutting speeds and are capable of offering high metal removal rates. The present work deals with the effect of work materials, cutting conditions and diameter of end mill cutters on the frequency-amplitude characteristics of chatter and on machined surface roughness. Vibration data were recorded using an experimental rig consisting of KISTLER 3-component dynamometer model 9257B, amplifier, scope meters and a PC.  Three different types of vibrations were observed. The first type was a low frequency vibration, associated with the interrupted nature of end mill operation. The second type of vibration was associated with the instability of the chip formation process and the third type was due to chatter. The frequency of the last type remained practically unchanged over a wide range of cutting speed.  It was further observed that chip-tool contact processes had considerable effect on the roughness of the machined surface.Key Words: Chatter, Cutting Conditions, Stable Cutting, Surface Roughness.

Comparative studies of variation in cutting conditions and cutter nomenclature on the surface morphology and the integrity of slot milled nimonic 263 alloy

2021 ◽  
Vol 9 (4) ◽  
pp. 045035
Author(s):  
S Gowthaman
Keyword(s):  
Surface Roughness ◽  
Surface Morphology ◽  
Flow Rate ◽  
Interactive Effect ◽  
Surface Characteristics ◽  
Milling Process ◽  
Behavioral Changes ◽  
Cutting Conditions ◽  
Slot Milling ◽  
Milling Operation

Abstract Cutter nomenclature and machining conditions has invoke critical impact on the machining behavior and surface integrity of machined samples. In this investigation, the slot milling operation has been performed under various cutter terminology or nomenclature (cutter with the RRA of −7°, 0° and 7°) and cutting conditions (spindle speed, table feed and MQL flow rate) to analyze its resulting outcome on the surface morphological features such as surface roughness (Sa), skewness (S sk ) and kurtosis (S ku ), etc Because the examination of these characteristics are important and significant to analyze the behavioral changes of asperities such as decohesion, wear resistance and adhesion, etc during in its relative motion. Additionally, the plasticity index and surface morphology of machined samples are helps to predict the variation in surface morphology under various machining behavior and through this study, it is found that the interactive effect of MQL flow rate and table feed offer higher and significant impact over the surface characteristics followed by the MQL flow rate during slot milling process.

Sign in / Sign up

Export Citation Format

Share Document