scholarly journals Prediction of Surface Roughness considering Cutting Parameters and Humidity Condition in End Milling of Polyamide Materials

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Mustafa Bozdemir
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Processing Parameters ◽  
Average Surface Roughness ◽  
Ann Model ◽  
Average Surface ◽  
Humidity Condition ◽  
The Impact

To know the impact of processing parameters of PA6G under different humidity conditions is important as it is vulnerable to humidity up to 7 %. This study investigated the effect of cutting parameters to surface roughness quality in wet and dry conditions. Artificial Neural Network (ANN) modeling is also developed with the obtained results from the experiments. Humidity condition, tool type, cutting speed, cutting rate, and depth of cutting parameters were used as input and average surface roughness value were used as output of the ANN model. Testing results showed that ANN can be used for prediction of average surface roughness.

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 Biomechanical evaluation between orthodontic attachment and three different materials after various surface treatments: A three-dimensional optical profilometry analysis

2019 ◽  
Vol 89 (5) ◽  
pp. 742-750
Author(s):  
İrem Kurt ◽  
Zafer Cavit Çehreli ◽  
Ayça Arman Özçırpıcı ◽  
Çağla Şar
Keyword(s):  
Surface Roughness ◽  
Three Dimensional ◽  
Material Surface ◽  
Original Material ◽  
Average Surface Roughness ◽  
Average Surface ◽  
Diamond Bur ◽  
Monolithic Zirconia ◽  
Feldspathic Porcelain ◽  
The Impact

ABSTRACT Objectives: To determine the best bonding method of orthodontic attachment among monolithic zirconia, feldspathic porcelain, hybrid porcelain, and the impact of surface-conditioning methods using a three-dimensional optical profilometer after debonding. Materials and Methods: 56 feldspathic porcelain, 56 monolithic zirconia, and 56 hybrid porcelain samples were divided into four surface treatment subgroups: (1) hydrofluoric (HF) acid etch + silane, (2) Al2O3 sandblasting + silane, (3) silicoating (SiO2), and (4) diamond bur + silane. The specimens were tested to evaluate shear bond strength (SBS). Residual composite was removed after debonding. Three-dimensional white-light interferometry was used to obtain quantitative measurements on surface roughness. Results: The highest SBS value was found for the HF acid–etched feldspathic porcelain group. The average surface roughness values were significantly higher in all material groups in which diamond bur was applied, while roughening with Cojet provided average surface roughness values closer to the original material surface. Conclusions: Variations in structures of the materials and roughening techniques affected the SBS and surface roughness findings.

scholarly journals The examination of the effect of variable cutting speeds on the surface and edge qualities of milled granite materials

2019 ◽  
Vol 11 (7) ◽  
pp. 168781401983631 ◽  
Author(s):  
István Gábor Gyurika ◽  
Tibor Szalay
Keyword(s):  
Surface Roughness ◽  
Cutting Speed ◽  
Sample Surface ◽  
Average Surface Roughness ◽  
Edge Chipping ◽  
Average Surface ◽  
Grain Sizes ◽  
Natural Stones ◽  
Cutting Speeds

Automated stone manufacturing has undergone considerable development in recent years. Thanks to international research dealing with the cutting, sawing and grinding of different natural stones, processing time shortens and tool-life lengthens. However, the process of stone milling has not been extensively examined yet, primarily because of the novelty of this technology. The aim of the research described in this article is to examine how variable cutting speed affects the quality of workpiece edges while milling granite materials. For the research, sample surfaces were formed on five granite slabs with different average grain sizes using five cutting speed values. Afterwards, changes in the average surface roughness and average edge chipping rate were examined. From the research results, it can be concluded that, due to an increase in cutting speed, the average edge chipping rate will decrease until reaching a borderline speed. In the case of a higher cutting speed, the referent tendency cannot be ascertained. A statistical analysis conducted in the scope of this research showed that if a variable cutting speed is applied, then changes in the quality of the sample surface edge can be inferred from the development trends of average surface roughness.

A Prediction Model of Surface Roughness in Micro End Milling

2015 ◽  
Vol 727-728 ◽  
pp. 354-357
Author(s):  
Mei Xia Yuan ◽  
Xi Bin Wang ◽  
Li Jiao ◽  
Yan Li
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Feed Rate ◽  
End Milling ◽  
Orthogonal Experiment ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Cutting Depth ◽  
Micro End Milling

Micro-milling orthogonal experiment of micro plane was done in mesoscale. Probability statistics and multiple regression principle were used to establish the surface roughness prediction model about cutting speed, feed rate and cutting depth, and the significant test of regression equation was done. On the basis of successfully building the prediction model of surface roughness, the diagram of surface roughness and cutting parameters was intuitively built, and then the effect of the cutting speed, feed rate and cutting depth on the small structure surface roughness was obtained.

Optimization of Machining Condition in WEDM for Titanium Grade 6 Using MOORA Coupled with PCA — A Multivariate Hybrid Approach

2017 ◽  
Vol 16 (02) ◽  
pp. 81-99 ◽  
Author(s):  
Himadri Majumder ◽  
Kalipada Maity
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Hybrid Approach ◽  
Cutting Speed ◽  
Kerf Width ◽  
Average Surface Roughness ◽  
Average Surface ◽  
Titanium Grade ◽  
Pulse On Time ◽  
Pulse Off Time

This paper represents a multivariate hybrid approach, combining Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and Principal Component Analysis (PCA) to optimize different correlated responses during Wire Electrical Discharge Machining (WEDM) process of titanium grade 6. The response parameters selected are the average cutting speed, average Kerf width and average surface roughness (Ra). All of them have been studied in terms of pulse-ON time, pulse-OFF time, wire feed and wire tension. As indicated by Taguchi’s signal-to-noise ratio, the optimum process parameters were achieved for the desired average cutting speed, average Kerf width and average surface roughness, respectively. At last, the optimum combination of process parameters was validated by affirmation test which gave considerably improved various quality characteristics. Confirmation test outcome revealed that multivariate hybrid approach MOORA coupled with PCA was a competent strategy to decide available cutting parameters for a desired response quality for WEDM of titanium grade 6.

Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy

2011 ◽  
Vol 325 ◽  
pp. 418-423 ◽  
Author(s):  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Back Propagation Neural Network ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Bpnn Model

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

The Investigation of Prediction Surface Roughness from Machining Forces in End Milling Processes

2011 ◽  
Vol 486 ◽  
pp. 91-94 ◽  
Author(s):  
Jabbar Abbas ◽  
Amin Al-Habaibeh ◽  
Dai Zhong Su
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Irregular Waves ◽  
Depth Of Cut ◽  
End Mill ◽  
Machined Surface ◽  
Machining Forces ◽  
Mill Cutter

Surface roughness is one of the most significant parameters to determine quality of machined parts. Surface roughness is defined as a group of irregular waves in the surface, measured in micrometers (μm). Many investigations have been performed to verify the relationship between surface roughness and cutting parameters such as cutting speed, feed rate and depth of cut. To predict the surface produced by end milling, surface roughness models have been developed in this paper using the machining forces by assuming the end mill cutter as a cantilever beam rigidly or semi- rigidly supported by tool holder. An Aluminium workpiece and solid carbide end mill tools are used in this work. Model to predict surface roughness has been developed. Close relationship between machined surface roughness and roughness predicted using the measured forces signals.

Investigating the Effect of Cutting Speed Variation on Surface Roughness of 7136 Aluminum Alloy in End Milling

2015 ◽  
Vol 809-810 ◽  
pp. 129-134 ◽  
Author(s):  
Alina Bianca Bonţiu Pop ◽  
Mircea Lobonţiu
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Milling Process ◽  
Future Research ◽  
Cutting Depth ◽  
Speed Variation

Surface quality is affected by various processing parameters and inherent uncertainties of the metal cutting process. Therefore, the surface roughness anticipation becomes a real challenge for engineers and researchers. In previous researches [1] I have investigated the feed rate influence on surface roughness and manufacturing time reduction. The 7136 aluminum alloy was machined by end milling operation using standard tools for aluminum machining. The purpose of this paper is to identify by experiments the influence of cutting speed variation on surface roughness. The scientific contribution brought by this research is the improvement of the end milling process of 7136 aluminum alloy. This material is an aluminum alloy developed by Universal Alloy Corporation and is used in the aircraft industry to manufacture parts from extruded profiles. The research method used to solve the problem is experiment. A range of cutting speeds was used while the cutting depth and the feed per tooth were constrained per minimum and maximum requirements defined for the given cutting tool. The experiment was performed by using a 16 mm End milling cutter, holding two indexable cutting inserts. The machine used for the milling tests was a HAAS VF2 CNC. The surface roughness (response) was measured by using a portable surface roughness tester (TESA RUGOSURF 20 Portable Surface Finish Instrument). Following the experimental research, results were obtained which highlight the cutting speed influence on surface roughness. Based on these results we created roughness variation diagrams according to the cutting speed for each value of feed per tooth and cutting depth. The final results will be used as data for future research.

Prediction of Surface Roughness in High Speed Machining of Inconel 718

2011 ◽  
Vol 264-265 ◽  
pp. 1193-1198
Author(s):  
Mokhtar Suhaily ◽  
A.K.M. Nurul Amin ◽  
Anayet Ullah Patwari
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Inconel 718 ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
High Speed Machining

Surface finish and dimensional accuracy is one of the most important requirements in machining process. Inconel 718 has been widely used in the aerospace industries. High speed machining (HSM) is capable of producing parts that require little or no grinding/lapping operations within the required machining tolerances. In this study small diameter tools are used to achieve high rpm to facilitate the application of low values of feed and depths of cut to investigate better surface finish in high speed machining of Inconel 718. This paper describes mathematically the effect of cutting parameters on Surface roughness in high speed end milling of Inconel 718. 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. Machining were performed using CNC Vertical Machining Center (VMC) with a HES510 high speed machining attachment in which using a 4mm solid carbide fluted flat end mill tool. Wyko NT1100 optical profiler was used to measure the definite machined surface for obtaining the surface roughness data. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to predict the surface roughness value with in the specified cutting conditions limit.

Optimization of Surface Roughness in End Milling of Titanium Alloy Ti-6Al-4V under the Influence of Magnetic Field from Permanents Magnets

2012 ◽  
Vol 576 ◽  
pp. 51-55 ◽  
Author(s):  
Syidatul Akma Sulaiman ◽  
A.K.M. Nurul Amin ◽  
M.D. Arif
Keyword(s):  
Magnetic Field ◽  
Surface Roughness ◽  
Titanium Alloy ◽  
Permanent Magnets ◽  
End Milling ◽  
Desirability Function ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Central Composite

This paper presents the effect of cutting parameters on surface roughness in end milling of Titanium alloy Ti-6Al-4V under the influence of magnetic field from permanent magnets. Response Surface Methodology (RSM) with a small central composite design was used in developing the relationship between cutting speed, feed, and depth of cut, with surface roughness. In this experiment, three factors and five levels of central composite with 0.16817 alpha value was used as an approach to predict the surface roughness, in end milling of titanium alloy, with reasonable accuracy. The Design-Expert 6.0 software was applied to develop the surface roughness equation for the predictive model. The adequacy of the surface roughness model was validated to 95% by using ANOVA analysis. Finally, desirability function approach was used to determine the optimum possible surface roughness given the capabilities of the end machine.

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