scholarly journals Optimization of Surface Roughness When Turning Polyamide using ANN-IHSA Approach

2012 ◽  
Vol 1 (4) ◽  
pp. 432 ◽  
Author(s):  
Milos Madic ◽  
D. Markovi? ◽  
M. Radovanovi?
Keyword(s):  
Surface Roughness ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cutting Speed ◽  
Harmony Search Algorithm ◽  
Depth Of Cut ◽  
Optimization Approach ◽  
Ann Model ◽  
Nose Radius ◽  
Cutting Parameter

This study presents an approach by coupling artificial neural network (ANN) and improved harmony search algorithm (IHSA) to determine the optimum cutting parameter settings for minimizing surface roughness when turning of polyamide material. An ANN model surface roughness was developed in terms of cutting speed, feed rate, depth of cut, and tool nose radius using the data from the turning experiment conducted according to Taguchis L27 orthogonal array. The optimal cutting parameter settings were determined by applying the IHSA to the developed ANN surface roughness model. The results show that the proposed optimization approach can be efficiently used for optimization of cutting parameter settings when turning polyamides. Although determining ANN and IHSA parameters is quite complex and problem dependent, it can be simplified by using Taguchis experimental design as in this study.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

OPTIMIZATION OF PROCESS PARAMETERS AFFECTING CUTTING FORCE, POWER CONSUMPTION AND SURFACE ROUGHNESS USING TAGUCHI-BASED GRAY RELATIONAL ANALYSIS IN TURNING AISI 1040 STEEL

2021 ◽  
Author(s):  
MAHIR AKGÜN
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Cutting Speed ◽  
System Optimization ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Significance Level ◽  
Relational Analysis

This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

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. 

Machining Parameters Optimization for Form Milling the Gear Using Improved Harmony Search Algorithm

2012 ◽  
Vol 472-475 ◽  
pp. 1809-1812
Author(s):  
Xin Jie Jia ◽  
Xiao Zhong Deng ◽  
Jian Xin Su
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Cutting Speed ◽  
Harmony Search Algorithm ◽  
Parameters Optimization ◽  
Machining Parameters ◽  
Machining Parameters Optimization ◽  
Improved Harmony Search ◽  
Similar Accuracy ◽  
Improved Harmony Search Algorithm

Machining parameters optimization in face-milling the hypoid gear was often needed in order to obtain lowest cost or highest productivity. In this study, the optimum value of machining parameters including feed rate, rotation speed are obtained using improved harmony search algorithm(IHSA), to yield minimum total time while considering constrains such as allowable cutting speed, tool life and machine tool capabilities. Results indicate that the IHSA converged to optimum solution with similar accuracy in comparison with the genetic algorithm (GA).

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

Constrained Optimization of Surface Roughness in Longitudinal Turning via Novel Modified Harmony Search

2011 ◽  
Author(s):  
Reza Farshbaf Zinati ◽  
Mohammad Reza Razfar
Keyword(s):  
Surface Roughness ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Cutting Condition ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
Artificial Neural ◽  
Longitudinal Turning ◽  
1045 Steel

The present research deals with a modified optimization algorithm of harmony search coupled with artificial neural networks (ANNs) to predict the optimal cutting condition. To this end, several experiments were carried out on AISI 1045 steel to attain required data for training of ANNs. Feed forward artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting experimental data, and Modified Harmony Search algorithm (MHS) was used to find the constrained optimum of the surface roughness. Furthermore, Simple Harmony Search algorithm (SHS) and Genetic Algorithm (GA) were used for solving the same optimization problem to illustrate the capabilities of MHS algorithm. The obtained results demonstrate that MHS algorithm is more effective and authoritative in approaching the global solution than the SHS algorithm and GA.

scholarly journals Prediction of cutting tool vibration and surface roughness in hard turning of AISI52100 steel

2018 ◽  
Vol 211 ◽  
pp. 03011
Author(s):  
Nitin Ambhore ◽  
Dinesh Kamble ◽  
Satish Chinchanikar
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Vibration Amplitude ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Vibration Signals ◽  
Cutting Parameter ◽  
Acceleration Amplitude

The changing behavior of vibration signals with varying cutting parameters (cutting speed, feed rate and depth of cut) for turning hardened AISI52100 steel has been studied and reported. The vibration response of cutting tool in all three mutually perpendicular directions, namely, in feed Vx, radial Vy and, tangential Vz directions have been captured by mounting piezoelectric tri-axial accelerometer close to the cutting tool. Experiments are planned and conducted as per Central composite rotatable design of Surface response methodology. The second order multiple regression models are developed to correlate cutting parameters with vibration acceleration and surface roughness. The coefficient of regression R2 for all models is found close to 0.92 which shows that the developed models are reliable and provide an excellent explanation between the cutting parameter and the vibration of cutting tool within limits. The analysis of the results revealed that cutting conditions are having prominent and mixed type effect on vibration signals. The acceleration amplitude Vx, Vy and Vz increases with increase in cutting speed, and depth of cut. The vibration amplitude Vx, Vy and Vz initially increases as feed increases and, with further increase in feed, the vibration amplitude decreases. The surface roughness is highly influenced by the feed rate followed by cutting speed whereas the depth of cut was found less significant. The fluctuation in frequency is observed in all directions. However, the band of frequency remains within a certain range. Within selected cutting parameter range, the maximum acceleration amplitude is observed in frequency band of 4 kHz - 16 kHz.

scholarly journals Assessments of Process Parameters on Cutting Force and Surface Roughness during Drilling of AA7075/TiB2 In Situ Composite

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1726
Author(s):  
S. Parasuraman ◽  
I. Elamvazuthi ◽  
G. Kanagaraj ◽  
Elango Natarajan ◽  
A. Pugazhenthi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Basic Class ◽  
Tib2 Particles ◽  
The Impact ◽  
Loss Of Strength

Reinforced aluminum composites are the basic class of materials for aviation and transport industries. The machinability of these composites is still an issue due to the presence of hard fillers. The current research is aimed to investigate the drilling topographies of AA7075/TiB2 composites. The samples were prepared with 0, 3, 6, 9 and 12 wt.% of fillers and experiments were conducted by varying the cutting speed, feed, depth of cut and tool nose radius. The machining forces and surface topographies, the structure of the cutting tool and chip patterns were examined. The maximum cutting force was recorded upon increase in cutting speed because of thermal softening, loss of strength discontinuity and reduction of the built-up-edge. The increased plastic deformation with higher cutting speed resulted in the excess metal chip. In addition, the increase in cutting speed improved the surface roughness due to decrease in material movement. The cutting force was decreased upon high loading of TiB2 due to the deterioration of chips caused by fillers. Further introduction of TiB2 particles above 12 wt.% weakened the composite; however, due to the impact of the microcutting action of the fillers, the surface roughness was improved.

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.

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