Integration of fuzzy logic with response surface methodology for thrust force and surface roughness modeling of drilling on titanium alloy

Abstract In the current study, the research explored the effect of the process parameters on the Titanium Alloy (Ti–6Al–4V) to improve the machining, surface and geometric characteristics of the circular cut-off profile by determining the optimum parameters for the Abrasive Water Jet Machining (AWJM). The input parameters considered are the Abrasive Flow Rate (AFR), Stand-off Distance (SoD), and Traverse Rate (TR). There are various input parameters to evaluate output parameters like Circularity, Cylindricity, and Surface Roughness (SR) of the circular cut profile. The experiments are conducted using Central Composite Design (CCD) in the Response Surface Methodology (RSM). Analysis of variance (ANOVA) is carried out to define most influenced process parameters and percentage of contribution. The RSM is used to predict the mathematical models for formulating the objective function using experimental results. RSM desirability approach is included in the method for determining optimum levels and discerning impacts on response variables of machining parameters. Confirmation tests with an optimum level of machining parameters have been completed to determine the adequacy of the RSM. In addition to that, the cutting profiles are also analysed using Scanning Electron Microscope (SEM). The Atomic Force Microscope(AFM) is often used to verify the minimum Surface Roughness of the AWJM machined surface.

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Multi-Response Optimization of CNC WEDM Process Parameters for Machining Titanium Alloy Ti 6AI-4V Using Response Surface Methodology (RSM)

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.541-542.354 ◽

2014 ◽

Vol 541-542 ◽

pp. 354-358 ◽

Cited By ~ 2

Author(s):

C. Nandakumar ◽

B. Mohan

Keyword(s):

Surface Roughness ◽

Response Surface Methodology ◽

Titanium Alloy ◽

Response Surface ◽

Process Parameters ◽

Material Removal Rate ◽

Material Removal ◽

Removal Rate ◽

Response Optimization ◽

Wedm Process

This research deals with the multi-response optimization of CNC WEDM process parameters for machining titanium alloy Ti 6AI-4V using Response Surface Methodology (RSM) to achieve higher Material Removal Rate (MRR) and lower surface roughness (Ra). The process parameters of CNC WEDM namely pulse-on time (TON), pulse-off time (TOFF) and wire feed rate (WF) were optimized to study the responses in terms of material removal rate and surface roughness. The surface plot and the contour plots were generated between the process parameters and the responses using MINITAB software. The results show that the Response surface methodology (RSM) is a powerful tool for providing experimental diagrams and statistical-mathematical models to perform the experiments appropriately and economically.

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DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)

Acta Polytechnica ◽

10.14311/ap.2020.60.0369 ◽

2020 ◽

Vol 60 (5) ◽

pp. 369-390

Author(s):

Ilesanmi Daniyan ◽

Isaac Tlhabadira ◽

Khumbulani Mpofu ◽

Adefemi Adeodu

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Titanium Alloy ◽

Response Surface ◽

Process Parameters ◽

Numerical Models ◽

Physical Experiments ◽

Artificial Neural Network Ann

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.

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