Surface integrity of AISI D2 when turned using coated and uncoated carbide tools

Haron, Che Hasan Che; Ghani, Jaharah A.; Ibrahim, Gusri Akhyar

doi:10.1504/ijptech.2007.015349

Cited by 2 publications

(2 citation statements)

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“…Che Haron et al (2007) reported that the surface roughness for uncoated carbide tools was in the range of 0.36-4.05 μm and 0.30-1.51μm for coated carbide insert (CVD TiN/ Al2O3/TiCN) respectively during turning AISI D2 (22 HRC) steel. The lowest surface roughness value for both types of carbide tools were observed at cutting speed of 250 m/min and feed rate of 0.05 mm/rev.…”

Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

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The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R 2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

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Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…If calculated F-value is greater than F-table value, the parameter is said to be statistically significant. Also if probability of significance (P-value) is less than 0.05 at 95% confidence level, then the factors are said to be statistical significance on responses (Haron et al, 2007). Table 6 and Table 7 show the results of ANOVA for cutting force and cutting temperature respectively.…”

Section: Effect Of Process Parametersmentioning

confidence: 99%

Some studies on cutting force and temperature in machining Ti-6Al-4V alloy using regression analysis and ANOVA

Kumar¹,

Sahoo²,

Satyanarayana³

et al. 2013

10.5267/j.ijiec

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The present work deals with the cutting forces and cutting temperature produced during turning of titanium alloy Ti-6Al-4V with PVD TiN coated tungsten carbide inserts under dry environment. The 1st order mathematical models are developed using multiple regression analysis and optimized the process parameters using contour plots. The model presented high determination coefficient (R2 = 0.964 and 0.989 explaining 96.4 % and 98.9 % of the variability in the cutting force and cutting temperature, which indicates the goodness of fit for the model and high significance of the model. The developed mathematical model correlates the relationship of the cutting force and temperature with the process parameters with good degree of approximation. From the contour plots, the optimal parametric combination for lowest cutting force is v 3 (75 m/min) – f 1 (0.25 mm/rev). Similarly, the optimal parametric combination for minimum temperature is v 1 (45 m/min) – f 1 (0.25 mm/rev). Cutting speed is found to be the most significance parameter on cutting forces followed by feed. Similarly, for cutting temperature, feed is found to be the most influencing parameter followed by cutting speed

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Surface integrity of AISI D2 when turned using coated and uncoated carbide tools

Cited by 2 publications

References 0 publications

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Some studies on cutting force and temperature in machining Ti-6Al-4V alloy using regression analysis and ANOVA

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