scholarly journals Optimization of Cutting Parameters to Minimize the Surface Roughness in the End Milling Process Using the Taguchi Method

2017 ◽  
Vol 61 (1) ◽  
pp. 30-35 ◽  
Author(s):  
João Ribeiro ◽  
◽  
Hernâni Lopes ◽  
Luis Queijo ◽  
Daniel Figueiredo ◽  
...  
2012 ◽  
Vol 723 ◽  
pp. 196-201 ◽  
Author(s):  
Peng Nan Li ◽  
Ming Chen ◽  
Xiao Jian Kang ◽  
Li Na Zhang ◽  
Ming Zhou

In this study AISI 1045 steel of different hardness are used in high speed milling. According to Taguchi method, cutting parameters (milling speed, milling depth, feed per tooth) and workpiece hardness for the influence of high speed milling of the surface roughness are optimized. Through this study, not only the optimal cutting parameters of the minimum surface roughness is obtained, but also the main cutting parameters that effect performance in high speed milling is analysed. Researching results can be provided to guide establishment of the high speed milling process.


2016 ◽  
Vol 16 (4) ◽  
pp. 255-261
Author(s):  
Tamiloli N ◽  
Venkatesan J

AbstractMachining of alloy materials at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality. Thus, developing a model for estimating the cutting parameters and optimizing this model to minimize the surface roughness and cutting temperatures becomes utmost important to avoid any damage to the quality surface. This paper presents the development of new models and optimizing these models of machining parameters to minimize the surface roughness and cutting temperature in end milling process by Taguchi method with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and cutting temperature. Due to the complexity of this machining optimization problem, a single objective Taguchi method has been applied to resolve the problem, and the results have been analyzed.


2011 ◽  
Vol 291-294 ◽  
pp. 3013-3023 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Channarong Rungruang

In order to realize the environmental hazard, this paper presents the investigation of the machinability of ball-end milling process with the dry cutting, the wet cutting, and the mist cutting for aluminum. The relations of the surface roughness, the cutting force, and the cutting parameters are examined based on the experimental results by using the Response Surface Analysis with the Box-Behnken design. The in-process cutting force is monitored to analyze the relations of the surface roughness and the cutting parameters. The proper cutting condition can be determined easily referring to the minimum use of cutting fluid, and the minimum surface roughness and cutting force of the surface plot. The effectiveness of the obtained surface roughness and cutting force models have been proved by utilizing the analysis of variance at 95% confident level.


2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.


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