Improved wild horse optimizer with deep learning enabled battery management system for internet of things based hybrid electric vehicles

Machinability analysis of Inconel 601, 625, 718 and 825 during electro-discharge machining: On evaluation of optimal parameters setting

Sustainability is a vital issue for present and future generation, and it aims to obtain overall efficiency in terms of economic, environmental and social aspects. Inconel 825 belongs to the family of nickel-based super alloy and is widely used in the chemical and marine industries. This work attempts to investigate machining performance of Inconel 825 using physical vapor deposition-titanium nitrate inserts, with a focus on sustainable machining. The effect of cutting parameters, viz. cutting speed (v), feed (f) and depth of cut (d) on three aspects of sustainability has been explored in two different machining environments, viz. dry and minimum quantity lubrication (MQL). The experimental results show a significant improvement in MQL machining and tool wear, and cutting power is reduced by 16.57 and 8.47%, respectively, and surface roughness is improved by 10.41%. The interacting effects of parameters on responses are studied using 3-D surface plots; it shows cutting speed and feed are found as dominating parameters on all the three responses. The novelty of this work is to optimize the process for the sustainable production of components by optimizing the process parameters with multiple and conflicting objectives. The sustainable optimization using genetic algorithm provides surface roughness (Ra) as 0.49 µm, tool flank wear (VB) as 110.68 µm and cutting power (P) as 5.44 kW with better convergent capability having 4% deviation. For the application of manufacturing industry, an optimization table is generated for selection of optimum process parameters for achieving desired surface roughness with minimum VB or minimum P.

Sustainable machining: an experimental investigation and optimization of machining Inconel 825 with dry and MQL approach

Electrical Discharge Machining (EDM) is a well-established non-conventional machining process for the machining of electrically conductive and difficult-to-machine materials. But its applications are limited because of the slow machining rate and poor surface finish. Powder mixed EDM (PMEDM) is unitary of the recent progresses in the EDM process in which powder particles mixed in the dielectric fluid results in higher machining rate and better surface quality. In the past, limited work has been found on PMEDM of Inconel-800 material.  Researchers have reported about machining with different powder particles like aluminum powder, silicon carbide, graphite etc. in the dielectric fluid of EDM, but the effect of powder particles, i.e. Tungsten carbide, cobalt and boron carbide along with tool material i.e. copper, copper-chromium and graphite on Inconel-800 material has not been explored. The purpose of the present work is to look into the issue of tool material (Cu, copper-chromium, and graphite) along with powder particles (tungsten carbide, cobalt and boron carbide) suspended in EDM oil on Inconel-800 material. The present work includes optimization of Material Removal Rate (MRR) and Tool Wear Rate (TWR) for the machining of Inconel-800 material using Powder Mixed Elctric Discharge Machining (PMEDM). Different input parameters such as peak current, pulse on-time, pulse off-time, tool and powder materials along with effect of three micro powder particles, i.e. tungsten carbide, cobalt and boron carbide and three electrodes i.e. copper, copper-chromium, and graphite have been considered for the experimentation. The box-Behnken method of Response Surface Methodology (RSM) has been used for designing the experiments along with the Desirability Approach for multiple response parameters optimization. The adequacy of the proposed mathematical models have also been tested using analysis of variance (ANOVA). Microstructure analysis and transfer of different factors on the machined surface has also been investigated using Scanning Electron Microscope (SEM), Energy Dispersive Spectrometer (EDS) and X - Ray Diffraction (XRD). The results showed that peak current, pulse on-time, and tool material significantly affects the Material Removal Rate (MRR) while peak current, pulse on-time, tool material and powder materials affected the Tool Wear Rate (TWR). Pulse off-time has a trifling effect on both MRR and TWR, while powder particles on MRR. From desirability approach, the optimal combination of parameters found to be current 1 amp, pulse on-time 0.98 μs, pulse off-time 0.03 μs, tool material 0.31 and the powder (suspended particles) 0.64. The analysis of the experimental observations highlights that the current, pulse on-time and tool material have found to be the most decisive factors for MRR, while current, pulse on-time, tool material and powder particles for TWR.

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Parametric optimization of powder mixed electrical discharge machining for nickel-based superalloy inconel-800 using response surface methodology

This book chapter pertains to the use of statistical methods and soft computing techniques that can be used in the modelling and optimization of machining processes. More specifically, the factorial design method, Taguchi method, response surface methodology (RSM), analysis of variance, grey relational analysis (GRA), statistical regression methods, artificial neural networks (ANN), fuzzy logic and genetic algorithms are thoroughly examined. As part of the design of experiments (DOE) the aforementioned methods and techniques have proven to be very powerful and reliable tools. Especially in machining, a plethora of works have already been published indicating the importance of these methods.

Modelling and Optimization of Machining with the Use of Statistical Methods and Soft Computing

Mathematical Modeling for Radial Overcut on Electrical Discharge Machining of Incoloy 800 by Response Surface Methodology

This study employed Accelerated Particle Swarm Optimization (APSO) algorithm to optimize the machining parameters that lead to a maximum Material Removal Rate (MRR), minimum surface roughness and minimum kerf width values for Wire Electrical Discharge Machining (WEDM) of AISI D3 die-steel. Four machining parameters that are optimized using APSO algorithm include Pulse on-time, Pulse off-time, Gap voltage, Wire feed. The machining parameters are evaluated by Taguchi’s L9 Orthogonal Array (OA). Experiments are conducted on a CNC WEDM and output responses such as material removal rate, surface roughness and kerf width are determined. The empirical relationship between control factors and output responses are established by using linear regression models using Minitab software. Finally, APSO algorithm, a nature inspired metaheuristic technique, is used to optimize the WEDM machining parameters for higher material removal rate and lower kerf width with surface roughness as constraint. The confirmation experiments carried out with the optimum conditions show that the proposed algorithm was found to be potential in finding numerous optimal input machining parameters which can fulfill wide requirements of a process engineer working in WEDM industry.

An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel

The aim of present experimental investigation is to compare the mechanical properties of Sisal, jute and kenaf fiber reinforced with glass fiber in polyester matrix hybrid composites. Hybrid composites were fabricated by hand lay-up technique. The tensile, flexural and impact tests were carried out on different composite samples as per the ASTM standards. It was observed that the tensile strength of jute/glass fiber composite is 1.94 and 1.59 times more than that of sisal/glass and kenaf/glass composites, respectively. The flexural load carrying capacity of sisal/glass composite is 3.4 and 2.83 times greater than those of jute/glass and kenaf/glass composites, respectively. Also, it can be seen that impact strength of jute/glass composite is almost equal to that of kenaf/glass composite and 1.13 times more than that of sisal/glass composite.

R. Venkatasamy

Papers

Mathematical Modeling for Radial Overcut on Electrical Discharge Machining of Incoloy 800 by Response Surface Methodology

An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel

Comparative Investigation on Mechanical Properties of Natural Fiber Reinforced Polyester Composites