scholarly journals An analytical force and surface roughness model for cylindrical grinding of brittle materials

2017 ◽  
Vol 8 (1) ◽  
pp. 68 ◽  
Author(s):  
Ali Zahedi ◽  
Bahman Azarhoushang
Keyword(s):  
Surface Roughness ◽  
Brittle Materials ◽  
Cylindrical Grinding ◽  
Roughness Model

Theoretical Surface Roughness Model in High Speed Face Milling

2014 ◽  
Vol 989-994 ◽  
pp. 3331-3334
Author(s):  
Tao Zhang ◽  
Guo He Li ◽  
L. Han
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
High Speed ◽  
Cutting Parameters ◽  
Angular Speed ◽  
Face Milling ◽  
Advanced Manufacturing ◽  
Roughness Model ◽  
Machined Parts ◽  
Important Object

High speed milling is a newly developed advanced manufacturing technology. Surface integrity is an important object of machined parts. Surface roughness is mostly used to evaluate to the surface integrity. A theoretical surface roughness model for high face milling was established. The influence of cutting parameters on the surface roughness is analyzed. The surface roughness decreases when the cutter radius increases, total number of tooth and rotation angular speed, while it increases with the feeding velocity. The high speed face milling can get a smooth surface and it can replace the grinding with higher efficiency.

The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network

2010 ◽  
Vol 102-104 ◽  
pp. 610-614 ◽  
Author(s):  
Jun Chi ◽  
Lian Qing Chen
Keyword(s):  
Surface Roughness ◽  
Real Time ◽  
Back Propagation ◽  
Cutting Parameters ◽  
Wavelet Network ◽  
Back Propagation Algorithm ◽  
The Real ◽  
Roughness Model ◽  
On Line ◽  
Artificial Network

A methodology based on relax-type wavelet network was proposed for predicting surface roughness. After the influencing factors of roughness model were analyzed and the modified wavelet pack algorithm for signal filtering was discussed, the structure of artificial network for prediction was developed. The real-time forecast on line was achieved by the nonlinear mapping and learning mechanism in Elman algorithm based on the vibration acceleration and cutting parameters. The weights in network were optimized using genetic algorithm before back-propagation algorithm to reduce learning time.The validation of this methodology is carried out for turning aluminum and steel in the experiments and its prediction error is measured less than 3%.

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.

Investigation of Loss Impact From Production-Like Features in a Compressor Duct Under Engine Realistic Conditions

2017 ◽  
Author(s):  
Fredrik Wallin ◽  
Mark H. Ross ◽  
Max Rusche ◽  
Scott Morris ◽  
Steven Ray
Keyword(s):  
Surface Roughness ◽  
Total Pressure ◽  
Experimental Testing ◽  
Computational Grid ◽  
Test Facility ◽  
Pressure Probe ◽  
Cfd Analysis ◽  
Roughness Model ◽  
Geometrical Features ◽  
Real Geometry

An experimental and numerical investigation of the flow in a compressor duct with engine-realistic in-production features is presented in this paper. The experimental testing was conducted in the ND-FSCC test facility at University of Notre Dame, Indiana, USA. A baseline duct was also tested for back-to-back comparison. The ducts were heavily instrumented; duct inlet and exit flowfields were scanned using a five-hole pressure probe that provided total pressure, velocities and flow angles. Based on the five-hole probe total pressures, duct losses could be assessed. Furthermore the duct inlet boundary layers were traversed and turbulence intensity levels were assessed. For the CFD analysis of the production-like duct, a highly complex computational grid, resolving all the geometrical features present, was used. A previously validated surface roughness model was used to account for the cast aero-surfaces. Both experimental and numerical results show that there is a significant increase in loss for the production-like duct when compared to the baseline duct loss. The CFD results agree very well with experimental results for the baseline duct, which makes it possible to use the experimental data recorded for the production-like duct to validate CFD tools for real geometry effects, such as interface steps and surface roughness for example.

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

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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