scholarly journals Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2986 ◽  
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Surface Roughness ◽  
Prediction Method ◽  
304 Stainless Steel ◽  
Turning Process ◽  
Aisi 304 ◽  
High Agreement ◽  
Machine Learning Approach ◽  
Global Optima ◽  
High Prediction ◽  
Cryogenic Turning

This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

EFFECT OF ABRASIVE TYPE ON THE SURFACE ROUGHNESS AND MRR IN MAF OF AISI 304 STEEL

2021 ◽  
Author(s):  
MAHMUT ÇELIK ◽  
HAKAN GÜRÜN ◽  
ULAŞ ÇAYDAŞ
Keyword(s):  
Surface Roughness ◽  
Removal Rate ◽  
Three Dimensional ◽  
Steel Plates ◽  
304 Stainless Steel ◽  
Average Surface Roughness ◽  
Machining Time ◽  
Aisi 304 ◽  
Average Surface ◽  
Different Levels

In this study, the effects of experimental parameters on average surface roughness and material removal rate (MRR) were experimentally investigated by machining of AISI 304 stainless steel plates by magnetic abrasive finishing (MAF) method. In the study in which three different abrasive types were used (Al2O3, B4C, SiC), the abrasive grain size was changed in two different levels (50 and 80[Formula: see text][Formula: see text]m), while the machining time was changed in three different levels (30, 45, 60[Formula: see text]min). Surface roughness values of finished surfaces were measured by using three-dimensional (3D) optical surface profilometer and surface topographies were created. MRRs were measured with the help of precision scales. The abrasive particles’ condition before and after the MAF process was examined and compared using a scanning electron microscope. As a result of the study, the surface roughness values of plates were reduced from 0.106[Formula: see text][Formula: see text]m to 0.028[Formula: see text][Formula: see text]m. It was determined that the best parameters in terms of average surface roughness were 60[Formula: see text]min machining time with 50[Formula: see text][Formula: see text]m B4C abrasives, while the best result in terms of MRR was taken in 30[Formula: see text]min with 50[Formula: see text][Formula: see text]m SiC abrasives.

Optimization of flank wear and surface roughness during turning of AISI 304 stainless steel using the Taguchi method

2020 ◽  
Vol 62 (9) ◽  
pp. 957-961
Author(s):  
Nursel Altan Özbek ◽  
Metin İbrahim Karadag ◽  
Onur Özbek
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Taguchi Method ◽  
Feed Rate ◽  
Flank Wear ◽  
Signal To Noise Ratio ◽  
Cutting Speed ◽  
304 Stainless Steel ◽  
Aisi 304 Stainless Steel ◽  
Aisi 304

Abstract This paper presents the effect of cutting tool, cutting speed and feed rate on the flank wear and surface roughness of austenitic stainless steel (AISI 304) during wet turning. Turning tests were designed based on the Taguchi method (L18). An orthogonal array, the signal-to-noise ratio (S/N) and the ANOVA were used to investigate the machinability of AISI 304 stainless steel with PVD and CVD coated tungsten carbide inserts. As a result of ANOVA, it was found that the feed rate was the most effective parameter on both flank wear and surface roughness.

scholarly journals Effect of Micro-Shot Peening on the Fatigue Performance of AISI 304 Stainless Steel

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1408
Author(s):  
Yu-Hsuan Chung ◽  
Tai-Cheng Chen ◽  
Hung-Bin Lee ◽  
Leu-Wen Tsay
Keyword(s):  
Residual Stress ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Fatigue Strength ◽  
Strain Hardening ◽  
Fatigue Resistance ◽  
Shot Peening ◽  
304 Stainless Steel ◽  
Aisi 304 Stainless Steel ◽  
Aisi 304

The effects of micro-shot peening on the rotating bending fatigue resistance of AISI 304 stainless steel (SS) were investigated in this study. The strain-hardening, surface roughness and induced residual stress were inspected and correlated with fatigue strength. Micro-shot peening caused intense strain-hardening, phase transformation and residual stress but was also accompanied by a minor increase in surface roughness. A nanograined structure, which was advantageous to fatigue resistance, was observed in the severe shot-peened layer. The absence of microcracks, minor increase in surface roughness, nanograined structure and induced high compressive residual stress in the shot-peened layer were responsible for the improved fatigue strength of AISI 304 SS.

Optimization and Prediction of Cutting Force and Surface Roughness in End Milling Process of AISI 304 Stainless Steel

2015 ◽  
Vol 813-814 ◽  
pp. 362-367 ◽  
Author(s):  
Darshan A. Patel ◽  
Jitendra M. Mistry ◽  
Vrushit P. Kapatel ◽  
Dhaval R. Joshi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Milling Process ◽  
304 Stainless Steel ◽  
Depth Of Cut ◽  
Aisi 304 Stainless Steel ◽  
Aisi 304 ◽  
End Milling Process

The end milling process is most commonly used where the large amount material can be removed to produce almost final shape of component. The present work deals with the experimental study and optimization the machining parameter of AISI 304 stainless steel. The effects of spindle speed, feed rate and depth of cut have been studied on the cutting force and surface roughness using Taguchi’s 27 orthogonal arrays. Regression analyses were used to develop the model of response parameters. The analysis of the result shows, the surface roughness and the cutting force is increased with feed rate and depth of cut but decreased with increased the cutting speed. The ANOVA indicate the feed rate was the most dominate parameter on surface roughness and cutting force than speed and depth of cut.

scholarly journals An experimental study of surface roughness in electrical discharge machining of AISI 304 stainless steel

2018 ◽  
Vol 38 (2) ◽  
pp. 90-96 ◽  
Author(s):  
IGNACIO HERNÁNDEZ CASTILLO ◽  
ORQUÍDEA SÁNCHEZ LÓPEZ ◽  
GUILLERMO ARTURO LANCHO ROMERO ◽  
CUAUHTÉMOC HÉCTOR CASTAÑEDA ROLDÁN
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Current Pulse ◽  
Pulse Current ◽  
304 Stainless Steel ◽  
Aisi 304 Stainless Steel ◽  
Aisi 304 ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time

The effect of the pulse current, pulse on time and pulse off time on the surface roughness of AISI 304 stainless steel workpieces produced by electric discharge machining (EDM) using grade GSP-70 graphite electrodes was studied. A factorial design was performed, considering two levels for each of the three established parameters. From the statistical analysis, it was obtained that the pulse current and pulse on time are the most significant machining parameters on the obtained surface roughness values of the stainless steel AISI 304 workpieces machined by EDM. On the other hand, the regression analysis of a second order model was done to estimate the average roughness (Ra) in terms of the pulse current, pulse on time and pulse off time. Finally, the mean absolute percentage error (MAPE) of the roughness values estimated by the second order regression model and the roughness obtained experimentally is also presented.

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