104 Modelling and Optimization of Cutting Parameter during Wire-EDM of Inconel 718 using Response Surface Methodology

2015 ◽  
Vol 2015 (0) ◽  
pp. 51-52
Author(s):  
Mohd Shahir Kasim ◽  
Nor Fakhriah Zakaria ◽  
Che Hasan Che Haron ◽  
Jaharah Abd Ghani ◽  
Raja Izamshah ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Inconel 718 ◽  
Wire Edm ◽  
Cutting Parameter ◽  
Modelling And Optimization

Parameter Optimization of Ball End Milling Process on Inconel 718 Using RSM and TLBO Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Nandkumar N. Bhopale ◽  
Nilesh Nikam ◽  
Raju S. Pawade
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Integrity ◽  
Inconel 718 ◽  
Tool Path ◽  
End Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Ball End Milling

AbstractThis paper presents the application of Response Surface Methodology (RSM) coupled with Teaching Learning Based Optimization Technique (TLBO) for optimizing surface integrity of thin cantilever type Inconel 718 workpiece in ball end milling. The machining and tool related parameters like spindle speed, milling feed, axial depth of cut and tool path orientation are optimized with considerations of multiple response like deflection, surface roughness, and micro hardness of plate. Mathematical relationship between process parameters and deflection, surface roughness and microhardness are found out by using response surface methodology. It is observed that after optimizing the process that at the spindle speed of 2,000 rpm, feed 0.05 mm/tooth/rev, plate thickness of 5.5 mm and 15° workpiece inclination with horizontal tool path gives favorable surface integrity.

Multi-Objective Optimization of Wire-Electric Discharge Machined Ultra-Thin Silicon Wafers Using Response Surface Methodology for Solar Cell Applications

2020 ◽  
Author(s):  
Divyanshu Bhartiya ◽  
Pinal Rana ◽  
Meinam Annebushan Singh ◽  
Deepak Marla
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Electric Discharge ◽  
Thermal Damage ◽  
Wire Edm ◽  
High Productivity ◽  
Wire Electric Discharge Machining ◽  
Thin Silicon ◽  
Pulse On Time

Abstract Recent investigations on the fabrication of ultra-thin silicon (Si) wafers using wire-electric discharge machining (wire-EDM) were observed to possess some inherent limitations. These include thermal damage (TD), kerf-loss (KL), and low slicing rate (SR), which constraints its industrial use. The extent of TD, KL, and SR largely depends on the process parameters such as open voltage (OV), servo voltage (SV), and pulse on-time (Ton). Therefore, optimizing the parameters that pertain to minimum TD and KL while maintaining a higher SR is the key to improvement in the fabrication of Si wafers using wire-EDM. Thus, the present study is an effort to analyze and identify the optimal parameters that relate to the most effective Si slicing in wire-EDM. A central composite design-based response surface methodology was used for optimizing the process parameters. The capability to slice Si wafers in wire-EDM was observed to be influenced by the discharge energy, which significantly impacted the overall responses. The severities of thermal damages were observed to be mainly dominated by the variation in OV and Ton due to the diffusion of thermal energy into the workpiece, leading to melting and subsequent re-solidification. For high productivity, the optimized parameters resulted in a slicing rate of 0.72 mm/min, thermal damage of 17.44 µm, and a kerf loss of about 280 µm.

scholarly journals Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

2020 ◽  
Vol 4 (2) ◽  
pp. 44
Author(s):  
Vishal Lalwani ◽  
Priyaranjan Sharma ◽  
Catalin Iulian Pruncu ◽  
Deepak Rajendra Unune
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Inconel 718 ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Wire Electrical Discharge Machining ◽  
Ann Model ◽  
Control Factors

This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives.

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