Grinding Performance of Titanium Alloy Ti-6Al-4V with Monolayer Brazed CBN Slotted Wheels

2012 ◽  
Vol 522 ◽  
pp. 162-166
Author(s):  
Wen Feng Ding ◽  
Zhi Wu Liu ◽  
Jian He ◽  
Hai Yan Zhao ◽  
Yan Wang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Titanium Alloy ◽  
Significant Influence ◽  
Grinding Forces ◽  
Grinding Performance ◽  
Grinding Parameters ◽  
Grain Distribution ◽  
Joining Strength

An experimental investigation was conducted on grinding Ti-6Al-4V alloy with monolayer brazed CBN slotted wheels for detecting the performance of the newly developed tools. The results showed that the effects of grinding parameters on the grinding forces and surface roughness with the brazed wheels were similar to that obtained with conventional electroplated ones to a certain extent. However, due to some distinguished features of the brazed CBN tools, i.e., high grain protrusion, optimum grain distribution and especially high joining strength, grain premature pullout behavior that had often occurred for electroplated tools was not observed during grinding, which had significant influence on the CBN wheels with merely singer-layer grains.

Effects of Rotary Dressing on Grinding Wheel Performance

1978 ◽  
Vol 100 (3) ◽  
pp. 297-302 ◽  
Author(s):  
T. Murray ◽  
S. Malkin
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Direct Relationship ◽  
Grinding Wheel ◽  
Grinding Forces ◽  
Trade Off ◽  
Flat Area ◽  
Grinding Performance

An investigation is described of the effects of rotary dressing on grinding wheel performance. Grinding performance is evaluated mainly in terms of the grinding forces and surface finish. It is demonstrated that the magnitudes of the grinding forces can be attributed to differences in the size of the wear flat area obtained by the various rotary dressing conditions. For finer dresser infeeds and greater differences between the peripheral velocities of the dresser and the grinding wheel, bigger grinding forces and smoother surfaces are obtained. A direct relationship is obtained between the grinding performance and the dressing interference angle, a larger angle resulting in smaller grinding forces and rougher surfaces. This leads to a trade-off relationship between grinding forces and surface roughness which characterizes the rotary dressing process.

Experimental Investigation and Estimation of Surface Roughness using ANN, GMDH & MRA models in High Speed Micro End Milling of Titanium Alloy (Grade-5)

2017 ◽  
Vol 4 (2) ◽  
pp. 1019-1028 ◽  
Author(s):  
Chakradhar Bandapalli ◽  
Bharatkumar Mohanbhai Sutaria ◽  
Dhananjay Vishnuprasad Bhatt ◽  
Kundan Kumar Singh
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Titanium Alloy ◽  
High Speed ◽  
End Milling ◽  
Grade 5 ◽  
Micro End Milling

Experimental Study on Micro-Grinding Process of Titanium Alloy Using Electro-Hydro-Dynamic Spray With Nanofluid and Compressed Air

2015 ◽  
Author(s):  
Pil-Ho Lee ◽  
Jung Soo Nam ◽  
Jung Sub Kim ◽  
Sang Won Lee
Keyword(s):  
Experimental Study ◽  
Surface Roughness ◽  
Titanium Alloy ◽  
Surface Quality ◽  
Ground Surface ◽  
Experimental Results ◽  
Compressed Air ◽  
Grinding Process ◽  
Grinding Forces ◽  
Micro Scale

In this paper, the micro-scale grinding processes of titanium alloy (Ti-6Al-4V) using electro-hydro-dynamic (EHD) spray with nanofluid and compressed air are experimentally investigated. In the experiments, specific micro-grinding forces and surface roughness of the ground workpiece are quantitatively analyzed as a function of nanofluid’s concentration and size of nanoparticles. In addition, the ground surface quality is qualitatively investigated by comparing the optical microscopic images. The experimental results show the effectiveness of EHD spraying with nanofluid and compressed air for reducing the specific micro-grinding forces and enhancing ground surface quality.

Experimental Research on the Predictive Model for Surface Roughness of Titanium Alloy in Abrasive Belt Grinding

2013 ◽  
Vol 716 ◽  
pp. 443-448 ◽  
Author(s):  
Rong Kai Cheng ◽  
Yun Huang ◽  
Yao Huang
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Prediction Model ◽  
Significant Influence ◽  
Influence Coefficient ◽  
Linear Regression Method ◽  
Belt Grinding ◽  
Abrasive Belt ◽  
Tc4 Titanium Alloy ◽  
Roughness Prediction

Titanium alloys have been applied to aerospacemedical and other fields. The surface roughness of titanium alloy about these areas is very high. Based on the results of orthogonal test, belt grinding surface roughness prediction model of TC4 Titanium alloy is established using linear regression method. The significant tests of regression equation are conducted and proved that the prediction model has a significant. The results indicate that the model has reliability on the prediction of surface roughness, abrasive belt grinding pressure has certain influence on the surface roughness, and grain size of belt and the belt linear speed have high significant influence on surface roughness and the influence coefficient are-0.9378 and-0.2317. While the contact wheel hardness and workpiece axial feeding speed have no significant influence on surface roughness.

scholarly journals Experimental Investigation on Machinability of Titanium Alloy by Laser-Assisted End Milling

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1552
Author(s):  
Dong-Hyeon Kim ◽  
Choon-Man Lee
Keyword(s):  
Surface Roughness ◽  
Experimental Investigation ◽  
Titanium Alloy ◽  
Titanium Alloys ◽  
Cutting Force ◽  
Cutting Tool ◽  
End Milling ◽  
Chemical Reactivity ◽  
Depth Of Cut ◽  
Laser Assisted Machining

The Machining of titanium alloys is challenging because of their high strength, low thermal conductivity, high chemical reactivity, and high stresses at the cutting tool edges. Laser-assisted machining is an effective way to improve the machinability of titanium alloys. This paper presents an experimental investigation of the machinability of cutting force and surface roughness in laser-assisted end milling of titanium alloy Ti-6Al-4V. The absorptivity of Ti-6Al-4V was determined by conducting preheating experiments using a high-power diode laser with a wavelength of 940–980 nm. A thermal analysis was performed using the finite element method to predict temperature distribution. The depth of cut was determined where tensile strength decreased sharply, and the predicted surface temperature is presented in the analysis results. The experiments were performed with conventional machining and laser-assisted machining. Surface roughness, tool wear, and cutting force were evaluated. In contrast to the results of conventional end milling, laser-assisted end milling improved surface roughness. Moreover, laser-assisted end milling proved more effective than conventional end milling in terms of cutting tool damage. Our results proved that heat assistance significantly influenced the magnitude of the cutting forces—while the actual reduction in forces varied slightly depending on the force component, cutting tool, and cutting conditions, force components showed a reduction of roughly 13–46%.

Simulation of the Surface Roughness by End Face Grinding Wheel with Ordered Abrasive Pattern

2015 ◽  
Vol 1095 ◽  
pp. 898-901
Author(s):  
Xing Shan Li ◽  
Mei Li Shao ◽  
Jun Wang ◽  
Yu Shan Lu
Keyword(s):  
Surface Roughness ◽  
Theoretical Basis ◽  
Grinding Wheel ◽  
Phyllotactic Pattern ◽  
Grinding Performance ◽  
Grinding Parameters ◽  
Simulation Results ◽  
Face Grinding

In order to improve the grinding performance of end face grinding wheel, the ordered theory is applied to the design of grinding wheel. Based on the track equation of the end grinding, the effects of grinding parameters on the surface roughness are studied and compared with the workpiece appearance by grinding wheel with different abrasive patterns. The simulation results show that the surface roughness values are lower by the grinding wheel with phyllotactic pattern than other patterns. It will provide theoretical basis for designing abrasive ordered pattern of grinding wheel.

scholarly journals First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors

2020 ◽  
Vol 4 (2) ◽  
pp. 35 ◽  
Author(s):  
Siamak Mirifar ◽  
Mohammadali Kadivar ◽  
Bahman Azarhoushang
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Acoustic Emission ◽  
Input Parameter ◽  
Ground Surface ◽  
Grinding Forces ◽  
Machining Processes ◽  
Network Parameters ◽  
Grinding Parameters ◽  
Ground Surface Roughness

The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and grinding forces, through an individual integrated acoustic emission (AE) sensor in the machine tool. Two models were trained and tested. Once using only the grinding parameters, and another with both acoustic emission signals and grinding parameters as input data. The recorded AE-signal was pre-processed, amplified and denoised. The feedforward neural network was chosen for the modeling with Bayesian backpropagation, and the model was tested by various experiments with different grinding and neural network parameters. It was found that the predictions presented by the achieved network parameters model agreed well with the experimental results with a superb accuracy of 99 percent. The results also showed that the AE signals act as an additional input parameter in addition to the grinding parameters, and could significantly increase the efficiency of the neural network in predicting the grinding forces and the surface roughness.

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