An experimental investigation as to the effect of cutting parameters on roundness error and surface roughness in cylindrical grinding

2005 ◽  
Vol 43 (11) ◽  
pp. 2309-2322 ◽  
Author(s):  
H. Saglam * ◽  
F. Unsacar ◽  
S. Yaldiz
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Cutting Parameters ◽  
Roundness Error ◽  
Cylindrical Grinding

The Super-Thin Rod Cylindrical Honing Technology Research

2012 ◽  
Vol 503-504 ◽  
pp. 764-767 ◽  
Author(s):  
Lin Zhu ◽  
Lin Pan
Keyword(s):  
Surface Roughness ◽  
High Precision ◽  
High Efficiency ◽  
Slenderness Ratio ◽  
Flexible Production ◽  
Technology Research ◽  
Processing Efficiency ◽  
Stroke Length ◽  
Roundness Error ◽  
Cylindrical Grinding

The super-thin rod cylindrical grinding is a problem in the machining, super-thin rod with large slenderness ratio, poor rigidity, large roundness error after grinding, and with low processing efficiency. This study uses cylindrical honing processing super-thin rod parts, and designing the super-thin rod cylindrical honing head, carrying on a honing test. The results show that the super-thin rod cylindrical coarse honing capacity reach up to 0.002mm/double stroke(length 1698mm), surface roughness reach up to Ra 0.8 ~ 0.025μm after honing, roundness error reach up to 2μm. It fully shows that super-thin rod cylindrical honing technology has high precision, low surface roughness, flexible production processing and high efficiency.

Influence of Cutting Parameters on the Surface Quality of Round Parts Made from AZ61 Magnesium Alloy and Machined by Turning

2013 ◽  
Vol 837 ◽  
pp. 128-134 ◽  
Author(s):  
Gheorghe Mustea ◽  
Gheorghe Brabie
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Magnesium Alloy ◽  
Surface Quality ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Az61 Magnesium Alloy ◽  
Machined Surfaces

The use of magnesium alloys in construction of different components of the mechanical systems (such: cars, aerospace vehicles, medical equipment etc.) is very efficient not only because it leads to reduction of the systems weight but also because it leads to reduction or elimination of the environment polluting and to reduction of the energy consumption. Generally, the main factors that influence the quality of the machined surfaces are as follows: cutting parameters, material properties, geometry of the tools, cooling liquids and lubricants, physical and mechanical properties of the subsurface layers etc. Among the above mentioned factors, cutting parameters are the factors that strongly influence the quality of the machined surfaces. The present paper analysis the results of the experimental investigation performed to determine the influence of cutting parameters (cutting speed, feed rate and cutting depth) on the surface quality machined by turning the AZ61 magnesium alloy. The main characteristics of the machined surface quality analyzed in experimental investigation were the surface roughness and hardness. The main conclusions resulted from the results analysis were as follows: the decrease of the feed rate led to surface roughness decrease and hardness increase; the increase of the cutting speed also led to an improved surface quality.

Experimental Investigation of Surface Roughness in High Speed Turning of Hardened AISI P20 Steel

2011 ◽  
Vol 418-420 ◽  
pp. 1228-1231 ◽  
Author(s):  
Bo Di Cui
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
High Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Turning ◽  
Aisi P20 ◽  
Optimal Cutting Parameters ◽  
Aisi P20 Steel

Surface roughness is one of the most important product quality characteristics. In this paper, experimental investigation of surface roughness was performed in high speed turning of hardened AISI P20 steel with CBN tool based on design of experiment. The influence of cutting speed, feed rate, depth of cut and nose radius on surface roughness were assessed using analysis of variance (ANOVA). Optimal cutting parameters were found to improve the machining performance. Due to the complexity of machining process, artificial neural network (ANN) was employed to develop the predictive model of surface roughness. Simulations were done to study the relationship between surface roughness and cutting parameters based on the proposed model.

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