Multi-Response Optimization of process Parameter in Vertical Milling Machine of EN 31 Using Taguchi Method

The purpose of this experimental research is to compare the effectiveness of using Taguchi approaches for multi-response optimization of process parameters in Vertical Milling Machine of EN 31 Material intending to minimize surface roughness and tool wear rate while maximizing material removal rate to improve the productivity of the process with coated carbide insert. Taguchi L9 and Annova have been applied for experimental design and analysis. This experiment shows that feed and depth of cut are factors that are important for tool wear, Depth of cut is a notable factor for Material Removal Rate and feed is the most notable factor for surface roughness. Spindle speed has little effect on tool wear rate, surface roughness, and material removal rate. Mathematical models for three response parameters i.e. tool wear rate, surface roughness, and material removal rate were obtained by regression analysis

Turning Tool Wear Estimation Based on the Calculated Parameter Values of the Thermodynamic Subsystem of the Cutting System

Today, modern metalworking centers are not yet able to reliably assess the degree of wear of the tool used in metal cutting. Despite the fact that a large number of methods for monitoring the service life of the tool have been developed, this issue still remains a difficult task that needs to be solved. Idea: The article proposes a new, previously unused method for estimating the power of a cutting wedge in metalworking. The aim of the study is to develop a method for indirectly estimating the tool wear rate based on a consistent model of intersystem communication that describes the force, thermal and vibration reactions of the cutting process to the shaping movements of the tool. Research methods: The study consists of experiments on a measuring stand and a homemade measuring complex. It also uses the Matlab mathematical software package for processing and graphical interpretation of data obtained during experiments. The results show that the proposed method of estimating the current tool wear is applicable for the interpretation of experimental data. Statistically, the modified Voltaire operator of the second kind models the temperature more accurately; at the peak, this method is three times more accurate than the other.

Multi-response optimization of EDM drilling parameters of the Nitinol SMA

The demand for Nitinol (SMA) is increasing rapidly for various applications. With the aim of optimum control parameters of EDM, 46 experiments completed on six specimens of 6.156 mm thickness using Sparkonix EDM drill machine. Current (I), voltage (V), charging-time (TON), discharging-time (TOFF), and dielectric pressure (DP) were taken as input control parameters. Single-indexed optimization of material removal rate (MRR), tool-wear rate (TWR), and degree of tapperness (DoT) are evaluated using gray relational grade (GRG). Individual control-parameter contributions are evaluated using Taguchi and ANOVA. The obtained optimal input control parameters were used for the confirmation experiment, and the obtained result gives good agreement to it. V and TON are found as the most significant parameters. Maximum and minimum values of MRR, TWR, and DoT have been recorded as 0.0277 & 0.0074 g/min, 0.0177 & 0.0033 g/min, and 0.032 & 0.01 radians respectively. MRR, TWR, and DoT improved by 49.1, 4.5, and 43.3 %, respectively.

Optimization the Machining Parameters of Electro Chemical Discharge Machining of NiTi Shape Memory Alloys

The electrochemical discharge machining (ECDM) is a combination effect of electrochemical machining in which metal is removed through the electrochemical process and electrical discharge machining in which metal is removed by rapid current discharges between two electrodes which are separated by a dielectric liquid and subject to an electric voltage. Difficulty of machining nickel titanium alloys by conventional methods such as; the significant tool wear, the need of highly experienced operators, and an excessive degradation in the material performance due to the high thermal and mechanical effects of these methods. For these, reasons non-conventional methods such as electrical discharge machining and electro chemical machining are often used to fabricate NiTi alloys with better machining results. The experiments were conducted with various conditions of voltage (50,60,70 and 80)V, dielectric solution concentration (30 and 40% of NaOH) and nanoparticles silver, and copper content (0.5% Cu, 0.5% Ag, 0.5% Cu and Ag) in the (55% Ni-45%Ti) alloy samples. The machining experiments were designed according to Taguchi's design of experiments (L32). Grey relational analysis was used to optimize the responses of the ECDM process. Material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra) represent the response parameters for machining of the alloy samples prepared by the powder metallurgy route. To achieve the objectives of this research work MiniTab17 software was employed. The optimal conditions were: voltage of 50V, solution concentration of 40% and the sample (NiTi+0.5%Cu+0.5%Ag) have the highest effect on machining characteristics with MRR value of 0.04991mg/sec., tool wear rate value of 0.00125mg/sec., and surface roughness of 0.0117μm.

Parametric investigation of tool wear rate in EDM of Fe-based shape memory alloy: microstructural analysis and optimization using genetic algorithm

Purpose This study aims to experimentally investigate the influence of considered process parameters, i.e. pulse on time, pulse off time, peak current and gap voltage, on tool wear rate (TWR) in electrical discharge machining (EDM) of iron (Fe)-based shape memory alloy (SMA) through designed experiments. The parametric optimization for TWR has also been attempted using the desirability approach and genetic algorithm (GA). Design/methodology/approach The response surface methodology (RSM) in the form of Box–Behnken design has been used to scheme out the experiments. The influence of considered process inputs has also been observed through variance analysis. The reliability and fitness of the developed mathematical model have been established with test results. Microstructure analysis of machined samples has also been evaluated and analyzed using a scanning electron microscope (SEM). SEM images revealed the surface characteristics such as micro-cracks, craters and voids on the tool electrode surface. SEM images provide information about the surface integrity and type of wear on the surface of the tool electrode. Findings The input parameters, namely, pulse on time and pulse off time, are major influential factors impacting the TWR. High TWR has been reported at large pulse on time and small pulse off time conditions whereas higher TWR is reported at high peak current input settings. The maximum and minimum TWR values obtained are 0.073 g/min and 0.017 g/min, respectively. The optimization with desirability approach and GA reveals the best parametric values for TWR i.e. 0.01581 g/min and 0.00875 g/min at parametric combination as pulse on time = 60.83 µs, pulse off time = 112.16 µs, peak current = 18.64 A and gap voltage = 59.55 V, and pulse on time = 60 µs, pulse off time = 120 µs, peak current = 12 A and gap voltage = 40 V, correspondingly. Research limitations/implications Proposed work has no limitations. Originality/value SMAs have been well known for their superior and excellent properties, which make them an eligible candidate of paramount importance in real-life industrial applications such as orthopedic implants, actuators, micro tools, stents, coupling, sealing elements, aerospace components, defense instruments, manufacturing elements and bio-medical appliances. However, its effective and productive processing is still a challenge. Tool wear study while processing of SMAs in EDM process is an area which has been less investigated and of major concern for exploring the various properties of the tool and wear in it. Also, the developed mathematical model for TWR through the RSM approach will be helpful in industrial revelation.

Multi response optimization of PMEDM of Ti6Al4V using Al2O3 and SiC powder added de-ionized water as dielectric medium using grey relational analysis

In the present work, surface characteristics of powder mixed electro-discharge machining (PMEDM) process are investigated using alumina and carborundum abrasive powder added dielectric fluid for titanium alloy Ti6Al4V. Deionized water is utilized as dielectric to accomplish an environmentally safe machining climate, and limit the emanation of harmful substances. Pulse ON/OFF time (TON/TOFF), discharge current (IP), and powder concentration (PC) are selected as process variables to reconnoiter characteristics of performance like surface finish, rate of material removal, and tool wear. The multi-response optimization has been performed using grey relational analysis (GRA) to establish the optimal parametric combination of process variables that gives the finest surface quality and minutest tool wear. The investigation results divulge that discharge current (IP) and powder concentration (PC) have the most significant effect on material removal rate (MRR), tool wear rate (TWR), and surface finish. The surface characteristics were evaluated by scanning electron microscope for the optimal parameters combination. The minutest value of surface roughness and tool wear rate is achieved at IP: 06 amps, TON: 05 µS, TOFF: 96 µS, and PC: 0.50 g/L. The optimized set of parameters would help process engineers to attain improved machining performance of PMEDM, economically along with desired surface characteristics.

Fuzzy based multi-response optimization: a case study on EDM machining process

The current study challenges the multi-objective optimization of electric discharge machining (EDM) parameters. EDM is used for creating profiles by machining of workpiece that are difficult to machine by conventional method. In the current work four responses such as material removal rate (production rate), tool wear rate, surface roughness (quality) and circularity (profile) are collectively investigated with varying controlling parameters. The human decision for best combination of controlling parameters for highest performance has uncertainties, which results in inferior solution. The multiple responses along with uncertainties and impreciseness can be addressed by combining a neuro-fuzzy system with particle swarm optimization (PSO). To illustrate the superiority of the proposed approach a set of experiment have been conducted in EDM process using AISI D2 tool steel as workpiece and brass tool. The experimental plan was made according to the Box-Behnken response surface methodology design with four process parameters namely discharge current, pulse-on-time, duty factor, and flushing pressure. The four response parameters such as material removal rate, tool wear rate, surface roughness, and circularity of machined components were optimized simultaneously. One unique Multi-response Performance Characteristic Index was obtained by combining the four responses using the proposed neuro-fuzzy technique. A regression model was developed on single response and optimized by PSO to obtain the optimal parameter setting. An experiment was conducted on optimal parameter to test the optimum performance. It is observed that the EDM responses were affected significantly by discharge current and pulse-on-time. The increase in pulse-on-time leads to larger surface cracks and more micro-pores on the machined surface. Article Highlights RSM was proven to be an effective statistical tool for reducing the experimental runs, and also establishes the relation between multiple inputs and single output. The neuro-fuzzy system combined with PSO results a suitable model to convert multiple response into an equivalent single response. The presented approach can be a practical method for situations where multiple conflicting objectives are needed to be optimized at the same time.

EFFECT OF THE TOOL SURFACE AREA AND WORKPIECE VIBRATION ON THE μEDM PERFORMANCE

This work investigates the influence of tool surface area (TSA) on the average surface roughness ([Formula: see text], tool wear rate (TWR) and material removal rate (MRR) in the micro-electrical discharge machining ([Formula: see text]EDM). The effects of three different TSAs were investigated at three different discharge energy settings. It was observed that the TSA had substantial influence on [Formula: see text]EDM performance owing to scaling effect. Therefore, the low-frequency workpiece vibration was applied to improve the [Formula: see text]EDM performance. The surface topography of machined surfaces was examined using scanning electron microscopy to disclose the effect of TSA as well as vibration frequency on [Formula: see text]EDMed surfaces.