Modeling of chip–tool interface temperature using response surface methodology and artificial neural network in HPC-assisted turning and tool life investigation

Kamruzzaman, M.; Rahman, Saadman Sakib; Ashraf, Md. Zurais Ibne; Dhar, Nikhil Ranjan

doi:10.1007/s00170-016-9467-6

Cited by 24 publications

(10 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prediction model was developed using RSM and ANN and found that speed had maximum contribution on chip–tool interface temperature. 15 The simulation of rough and finish milling operations of two materials was carried out. The neural milling process model had 99% correlation between the proposed and experimental system.…”

Section: Introductionmentioning

confidence: 99%

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Shankar

Mohanraj

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece material and turning test is conducted with various levels of speed, feed and depth of cut. Cutting force and surface roughness are measured for both the fresh and dull tool under dry cutting conditions. The tool insert used is CNMG 120408 grade, TiN coated cemented carbide tool. The experiments are conducted based on the response surface methodology face central composite design of experiments. The feed rate (14.52%), depth of cut (27.72%) and the interaction of feed rate and depth of cut (50.39%) influence the cutting force. The feed rate (21.33%) and the interaction of cutting speed and depth of cut (26.67%) influence the flank wear. The feed rate (61.63%) has the significant influence on surface roughness. The feed forward back propagation neural network of 5-n-1 architecture is trained using the algorithms like Levenberg Marquardt, BFGS quasi-Newton, and Gradient Descent with Momentum and Gradient descent with adaptive learning rate. The network performance has been assessed based on their mean square error and computation time. From this analysis, the BFGS quasi-Newton back propagation algorithm produced the least mean squared error value with minimum computation time.

show abstract

Section: Introductionmentioning

confidence: 99%

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Shankar

Mohanraj

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…The study concluded that model obtained using a Box Cox transformation has a better prediction capability than the other model. M. Kamruzzaman et al (2016) investigated the effect of feed rate, cutting speed, tool material, machining environment and work piece material on chip tool interface temperature by applying Artificial Neural Network techniques, Response surface methodology and ANOVA to determine the significant parameters. The workpiece material used were C-60, 17CrNiMo4, and 42CrMo4 steel alloys and standard carbide inserts like SNMG and SNMM were tools used while machining.…”

Section: Application Of Taguchi Methods In Manufacturing Processesmentioning

confidence: 99%

Review on Optimization Techniques used for Determining Machining Conditions to get Effective Tool Life and Surface Finish

Kumar¹

2017

DAAAM International Scientific Book

View full text Add to dashboard Cite

“…ANOVA establishes the relative significance of factors in terms of their percentage contribution to the response. It estimates the variance of error for the effects and the confidence interval of the error prediction [30]. It is based on the test results of Minitab software while each value is the average of two replications for each condition.…”

Section: Taguchi Experimental Design and Optimizationmentioning

confidence: 99%

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Youssef

El-Hofy

Ahmed

2018

JMMP

View full text Add to dashboard Cite

Due to their high ductility, high durability, and excellent corrosion resistance, stainless steels are attractive materials for a variety of applications. However, high work hardening, low thermal conductivity, and high built-up edge (BUE) formation make these materials difficult to machine. Rapid tool wear and high cutting forces are the common problems encountered while machining these materials. In the present work, the application of Taguchi optimization methodology has been used to optimize the cutting parameters of the drilling process for machining two stainless steels: austenitic AISI 304 and duplex AISI 2205 under dry conditions. The machining parameters which were chosen to be evaluated in this study are the tool material, cutting speed, and feed rate, while, the response factors to be measured are the tool life (T), cutting force (F c ), and specific cutting energy (k s ). Additionally, empirical models were created for predicting the T, F c and k s using linear regression analysis. The results of this study show that AISI 2205 stainless steel has a shorter tool life, a higher cutting force, and a higher specific cutting energy than AISI 304 stainless steel. In addition, the Taguchi method determined that A 3 B 1 C 1 and A 3 B 3 C 1 (A 3 = TiN-coated twist drill, B 1 = 13 m/min, B 3 = 34 m/min, C 1 = 0.12 mm/rev) are the optimized combination of levels for the best tool life and the lowest cutting force, respectively. Meanwhile, the optimized combination of levels for all three control factors from the analysis, which provides the lowest specific cutting energy, was found to be A 3 B 1 C 3 (A 3 = TiN-coated twist drill, B 1 = 13 m/min, C 3 = 0.32 mm/rev) for both stainless steels.

show abstract

Modeling of chip–tool interface temperature using response surface methodology and artificial neural network in HPC-assisted turning and tool life investigation

Cited by 24 publications

References 44 publications

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network

Review on Optimization Techniques used for Determining Machining Conditions to get Effective Tool Life and Surface Finish

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Contact Info

Product

Resources

About