scholarly journals Surface Roughness Analysis and Prediction with an Artificial Neural Network Model for Dry Milling of Co–Cr Biomedical Alloys

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6361
Author(s):  
Manuela-Roxana Dijmărescu ◽  
Bogdan Felician Abaza ◽  
Ionelia Voiculescu ◽  
Maria-Cristina Dijmărescu ◽  
Ion Ciocan
Keyword(s):  
Surface Roughness ◽  
Prediction Models ◽  
End Milling ◽  
Design Method ◽  
Cutting Speed ◽  
Process Time ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Analysis ◽  
Input Variables

The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface quality and shortening the manufacturing process time. An experimental plan based on the central composite design method was adopted to determine the influence of the axial depth of cut, feed per tooth and cutting speed process parameters (input variables) on the Ra surface roughness (response variable) which was recorded after machining for both alloys. To develop the prediction models, statistical techniques were used first and three prediction equations were obtained for each alloy, the best results being achieved using response surface methodology. However, for obtaining a higher accuracy of prediction, ANN models were developed with the help of an application made in LabView for roughness (Ra) prediction. The primary results of this research consist of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni prediction models and the developed application. The modeling results show that the ANN model can predict the surface roughness with high accuracy for the considered Co–Cr alloys.

scholarly journals Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Aykut Eser ◽  
Elmas Aşkar Ayyıldız ◽  
Mustafa Ayyıldız ◽  
Fuat Kara
Keyword(s):  
Surface Roughness ◽  
Conjugate Gradient ◽  
Prediction Models ◽  
Experimental Studies ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Ann Model ◽  
Aa6061 Alloy

This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.

Optimization of Milling Parameters of CFRP for Surface Roughness Using Taguchi Design Method

2014 ◽  
Vol 1027 ◽  
pp. 76-79
Author(s):  
Jing Wen Zhou ◽  
Yan Chen ◽  
Yu Can Fu ◽  
Jiu Hua Xu ◽  
An Dong Hu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Design Method ◽  
Statistical Significance ◽  
Cutting Speed ◽  
Taguchi Design ◽  
Depth Of Cut ◽  
Milling Parameters ◽  
Validation Experiment ◽  
Coated Cemented Carbide

End milling is conducted on carbon fiber reinforced plastics (CFRP) by using a diamond coated cemented carbide tool. Taguchi design method is employed to investigate the influence of cutting speed, feed rate and depth of cut on surface roughness. In Taguchi method, a three level orthogonal array has been used to determine the S/N ratio. Analysis of variance (ANOVA) and pareto diagram are used to determine the most significant milling parameters affecting the surface roughness. The results indicate that only the depth of cut has great statistical significance on the surface roughness, while the influences of cutting speed and feed are negligible. SEM micrographs shows that with the increase of depth of cut, a great deal epoxy resin will adhere to the finished surface. The greatest S/N ratio (1.46dB) is obtained during the validation experiment with optimum milling parameters.

A Comprehensive Study on Surface Roughness in Machining of AISI D2 Hardened Steel

2012 ◽  
Vol 576 ◽  
pp. 60-63 ◽  
Author(s):  
N.A.H. Jasni ◽  
Mohd Amri Lajis
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Radial Depth ◽  
Aisi D2 ◽  
Coated Carbide

Hard milling of hardened steel has wide application in mould and die industries. However, milling induced surface finish has received little attention. An experimental investigation is conducted to comprehensively characterize the surface roughness of AISI D2 hardened steel (58-62 HRC) in end milling operation using TiAlN/AlCrN multilayer coated carbide. Surface roughness (Ra) was examined at different cutting speed (v) and radial depth of cut (dr) while the measurement was taken in feed speed, Vf and cutting speed, Vc directions. The experimental results show that the milled surface is anisotropic in nature. Surface roughness values in feed speed direction do not appear to correspond to any definite pattern in relation to cutting speed, while it increases with radial depth-of-cut within the range 0.13-0.24 µm. In cutting speed direction, surface roughness value decreases in the high speed range, while it increases in the high radial depth of cut. Radial depth of cut is the most influencing parameter in surface roughness followed by cutting speed.

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.

Statistical Analysis of Process Parameters in Drilling of Al/Al2O3p Metal Matrix Composites

2014 ◽  
Vol 14 (3) ◽  
pp. 171-175 ◽  
Author(s):  
Yashvir Singh ◽  
Amneesh Singla ◽  
Ajay Kumar
Keyword(s):  
Surface Roughness ◽  
Statistical Analysis ◽  
Process Parameters ◽  
Metal Matrix ◽  
Design Method ◽  
Experimental Studies ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Signal To Noise ◽  
Drilling Parameters

AbstractThis paper presents a statistical analysis of process parameters for surface roughness in drilling of Al/Al2O3p metal matrix composite. The experimental studies were conducted under varying spindle speed, feed rate, point angle of drill. The settings of drilling parameters were determined by using Taguchi experimental design method. The level of importance of the drilling parameters is determined by using analysis of variance. The optimum drilling parameter combination was obtained by using the analysis of signal-to-noise ratio. Through statistical analysis of response variables and signal-to-noise ratios, the determined significant factors are depth of cut and drill point angle with the contributions of 87% and 12% respectively, whereas the cutting speed is insignificant contributing by 1% only. Confirmation tests verified that the selected optimal combination of process parameter through Taguchi design was able to achieve desired surface roughness.

Surface Roughness and Chip Formation of AlSi/AIN Metal Matrix Composite by End Milling Machining using the Taguchi Method

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
M. S. Said ◽  
J. A. Ghani ◽  
R. Othman ◽  
M. A. Selamat ◽  
N. N. Wan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Matrix Composite ◽  
Surface Finish ◽  
Chip Formation ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Taguchi Optimization ◽  
Cutting Speeds

The purpose of this research is to demonstrate surface roughness and chip formation by the machining of Aluminium silicon alloy (AlSic) matrix composite, reinforced with aluminium nitride (AlN), with three types of carbide inserts present. Experiments were conducted at various cutting speeds, feed rates, and depths of cut, according to the Taguchi method, using a standard orthogonal array L9 (34). The effects of cutting speeds, feed rates, depths of cut, and types of tool on surface roughness during the milling operation were evaluated using Taguchi optimization methodology, using the signal-to-noise (S/N) ratio. The surface finish produced is very important in determining whether the quality of the machined part is within specification and permissible tolerance limits. It is understood that chip formation is a fundamental element that influences tool performance. The analysis of chip formation was done using a Sometech SV-35 video microscope. The analysis of results, using the S/N ratio, concluded that a combination of low feed rate, low depth of cut, medium cutting speed, and an uncoated tool, gave a remarkable surface finish. The chips formed from the experiment varied from semi–continuous to discontinuous. 

Cutting Forces and Surface Roughness in High-Speed End Milling of Ti6Al4V

2013 ◽  
Vol 589-590 ◽  
pp. 76-81
Author(s):  
Fu Zeng Wang ◽  
Jun Zhao ◽  
An Hai Li ◽  
Jia Bang Zhao
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Cutting Forces ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Wide Range ◽  
Radial Depth ◽  
Coated Carbide

In this paper, high speed milling experiments on Ti6Al4V were conducted with coated carbide inserts under a wide range of cutting conditions. The effects of cutting speed, feed rate and radial depth of cut on the cutting forces, chip morphologies as well as surface roughness were investigated. The results indicated that the cutting speed 200m/min could be considered as a critical value at which both relatively low cutting forces and good surface quality can be obtained at the same time. When the cutting speed exceeds 200m/min, the cutting forces increase rapidly and the surface quality degrades. There exist obvious correlations between cutting forces and surface roughness.

An AI-Based Model for Predicting Instananeous Cutting Force in End Milling

Manufacturing ◽  
2002 ◽  
Author(s):  
Hazim El-Mounayri ◽  
Vipul Tandon
Keyword(s):  
Cutting Force ◽  
Frequency Domain ◽  
Cutting Forces ◽  
End Milling ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Ann Model ◽  
Force Signal ◽  
Input Variables ◽  
Artificial Neural Network Ann

An Artificial Neural Network (ANN) model is developed to accurately predict the instantaneous cutting forces in flat end milling. A unique frequency domain approach is presented and is seen to simulate instantaneous cutting forces reasonably well. A set of eight input variables is chosen to represent the machining conditions and frequency domain parameters of the cutting force signal are generated. Three input parameters are varied, namely Feed, Speed and Depth of Cut. Four output parameters are suggested as a sufficient set to adequately reproduce the instantaneous cutting forces. Exhaustive experimentation is conducted to collect data (consisting of Fx, Fy, and Fz) to train and validate the model.

scholarly journals OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
Muhammad Yanis ◽  
Amrifan Saladin Mohruni ◽  
Safian Sharif ◽  
Irsyadi Yani
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Ann Model ◽  
Green Machining ◽  
Radial Depth ◽  
Thin Walled

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. 

Investigations on surface roughness and microhardness of turned AM alloy

2020 ◽  
pp. 095440622097616
Author(s):  
Sunil Dutta ◽  
NSK Reddy
Keyword(s):  
Surface Roughness ◽  
Surface Integrity ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Average Roughness ◽  
Study Results ◽  
Superior Surface ◽  
Input Variables ◽  
Response Variables

Manufacturers in different sectors look for materials exhibiting good mechanical properties, high machinability, and superior surface integrity. The machinability of Mg alloys is one of the vital aspects which requires an exhaustive survey during their selection for different applications. The study examines the surface integrity of a fabricated AM alloy (Mg alloy with 7 wt%Al-0.9 wt%Mn) through dry turning. During the experiments, the input variables of turning viz. cutting speed( v), depth of cut (DOC), and feed( f) is altered and applied to the workpiece. The data obtained for the two response variables viz. surface roughness and microhardness accentuate the maximum influence of feed, followed by DOC and speed. For validation a two-stage methodology was adopted; In the first stage, the validation was done with the help of Analysis of variance (ANOVA); the results show the % contribution of feed, speed, and DOC on average roughness is 66.94%, 5.91%, and 27.23% and on microhardness is 47%, 8.3%, and 44.57%, respectively. Subsequently, in the second stage, the surface plots are drawn for both the response variables to ascertain the ANOVA outcomes; the shape of the plots corroborates the experimental and ANOVA results. The study results provide vital insights for parameter selection to get improved results on surface roughness and microhardness during machining of AM alloy.

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