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The purpose of this paper is to find out the optimum machining parameters using Taguchi technique with principal component analysis (PCA) during end milling of GFRP composites.

In multi-objective optimization, weight criteria of each objective are important for producing better and accurate solutions. This method has been employed for simultaneous minimization of surface roughness, cutting force and delamination factor. Experiments were planned using Taguchi’s orthogonal array with the machining parameters, namely, helix angle of the end mill cutter, spindle speed, feed rate and depth of cut were optimized with considerations of multiple response characteristics, including machining force, surface roughness and delamination as the responses. PCA is adopted to find the weight factors involved for all objectives. Finally analysis of variance concept is employed on multi-SN ratio to find out the relative significance of machining parameter in terms of their percentage contribution.

The multi-SN ratio is achieved by the product of weight factor and SN ratio to the performance characteristics in the utility concept. The results show that a combination of machining parameters for the optimized results has helix angle of 35°, machining speed of 4,000 m/min, feed rate of 750 mm/rev and depth of cut of 2.0 mm.

Effect of milling of GFRP composites on delamination factor, surface roughness and machining force with various helix angle solid carbide end mill has not been analysed yet using PCA techniques.

An experimental study has been conducted to model and optimize wire electric discharge machining (WEDM) process parameters such as pulse-on time, pulse-off time, servo voltage and peak current for response characteristics during machining of Nimonic 75 alloy.

The response surface methodology (RSM)-based Box–Behnken's design has been employed for experimental investigation. RSM is used for developing quadratic regression models for selected response variables i.e. material removal efficiency and kerf width. To validate the model, confirmation experiments have been performed. The multi-response optimization has been done using desirability function approach.

Through analysis of variation, the percent contribution of process parameters on the response characteristics has been found. Pulse-off time is the most significant parameter affecting the kerf width and material removal efficiency followed by pulse-on time. The quadratic regression models have been developed for prediction of selected response variables. An attempt has been made to optimize the WEDM parameters for material removal efficiency and kerf width. The recommended process parameter setting for maximum material removal efficiency and minimum kerf width have been found to be pulse-on time = 0.6 µs, pulse-off time = 14 µs, servo voltage = 25 V and peak current = 200 A.

The “kerf width” is an important response variable for maintaining dimensional accuracy of the machined component, but has not been given due attention by the researchers. In the present work, the developed regression model for “kerf width” can be used in estimating wire offset setting and thereby getting a dimensionally accurate product. The optimum process parameters obtained in WEDM of Nimonic 75 alloy will contribute in database of machining. The outcome of this study would be added to scare database of the machining of Nimonic 75 alloy and also would be extremely useful for making the technology charts for WEDM.

The choice of best machining parameters is an extremely basic factor in handling of any machined parts. The purpose of this paper is to exhibit a multi-objective optimization technique; in view of weighted aggregate sum product assessment (WASPAS) technique toward upgrade the machining parameters in modified air abrasive jet machining (MAAJM) process: injecting pressure, stand-off distance (SOD), and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Three conflicting destinations, material removal rate (MRR), surface roughness (SR) and taper angles (Ta), respectively, are considered at the same time. The proposed procedure uses WASPAS, which is the examination of parametric optimization of the abrasive jet machining (AJM) process. The results was used any scopes of reactions in MAAJM process is the ideal setting of parameters are resolved through investigations represented. There is wide utilization of Nickel 233 in aviation enterprises; machining information on producing a hole utilizing MAAJM for the first time is given in this work, which will be helpful different industries.

This paper exhibits a multi-objective optimization technique; in view of WASPAS technique toward upgrade the machining parameters in MAAJM process: injecting pressure, SOD, and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material.

As an outcome of using the tool in any ranges of responses in the AJM process, the optimal setting of parameters is determined through experiments illustrated. The machining data of generating a hole using AJM are studied for the first time in this work, which will be useful for aerospace industries, where Nickel 233 is used broadly.

A new material in unconventional machining process and also a multi-objective optimization technique are adopted.

The purpose of this paper is to investigate the influence of turning parameters such as cutting speed, feed rate and depth of cut on tool flank wear and machined surface quality of AISI 304 stainless steel during environment friendly turning under nanofluid minimum quantity lubrication (NMQL) conditions using PVD-coated carbide cutting inserts.

Turning experiments are conducted as per the central composite rotatable design under the response surface methodology. ANOVA and regression analysis are employed to examine significant cutting parameters and develop mathematical models for VB (tool flank wear) and R_a (surface roughness). Multi-response desirability optimization approach is used to investigate optimum turning parameters for simultaneously minimizing VB and R_a.

Optimal input turning parameters are observed as follows: cutting speed: 168.06 m/min., feed rate: 0.06 mm/rev. and depth of cut: 0.25 mm with predicted optimal output response factors: VB: 106.864 µm and R_a: 0.571 µm at the 0.753 desirability level. ANOVA test reveals depth of cut and cutting speed-feed rate interaction as statistically significant factors influencing tool flank wear, whereas cutting speed is a dominating factor affecting surface roughness. Confirmation tests show 5.70 and 3.71 percent error between predicted and experimental examined values of VB and R_a, respectively.

AISI 304 is a highly consumed grade of stainless steel in aerospace components, chemical equipment, nuclear industry, pressure vessels, food processing equipment, paper industry, etc. However, AISI 304 stainless steel is considered as a difficult-to-cut material because of its high strength, rapid work hardening and low heat conductivity. This leads to lesser tool life and poor surface finish. Consequently, the optimization of machining parameters is necessary to minimize tool wear and surface roughness. The results obtained in this research can be used as turning database for the above-mentioned industries for attaining a better machined surface quality and tool performance under environment friendly machining conditions.

Turning of AISI 304 stainless steel under NMQL conditions results in environment friendly machining process by maintaining a dry, healthy, clean and pollution free working area.

Machining of AISI 304 stainless steel under vegetable oil-based NMQL conditions has not been investigated previously.

Tool steel (AISI D3) is a preferred material for industrial usage. Some of the typical applications of D3 tool steel are blanking and forming dies, forming rolls, press tools and punches bushes. It is used under conditions where high resistance to wear or to abrasion is required and also for resistance to heavy pressure rather than to sudden shock is desirable. It is a high carbon and high chromium steel. Therefore, wire electric discharge machining (WEDM) is used to machine this tool steel. The paper aims to discuss these issues.

The present experimental investigation evaluates the influence of cryogenically treated wires on material removal rate (MRR) and surface roughness (SR) for machining of AISI D3 steel using the WEDM process. Two important process responses MRR and SR have been studied as a function of four different control parameters, namely pulse width, time between two pulses, wire mechanical tension and wire feed rate.

It was found that pulse width was the most significant parameter which affects the MRR and SR. Better surface finish was obtained with cryogenically treated zinc coated wire than brass wire.

The review of the literature indicates that there is limited published work on the effect of machining parameters in WEDM in cryogenic treated wires. Therefore, in this research work, it was decided to evaluate the effect of cryogenically treated wires on WEDM.

The purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making.

Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current.

The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process.

ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

The purpose of this paper is to determine the optimum level of geometrical parameters such as helix angle, nose radius, rake angle and machining parameters such as cutting speed, feed rate and depth of cut to arrive minimum surface roughness and tool wear during end milling of Al 356/SiC metal matrix composites (MMCs) using high speed steel end mill cutter.

L27 Taguchi orthogonal design with six factors and three levels is employed for conducting experiments. Analysis of variance (ANOVA) is carried out using Minitab16 software to find the influence of each input parameter on output performance measure. Grey-fuzzy logic multi optimisation algorithm is used to find the optimum level of the input parameters for minimum surface roughness and tool wear simultaneously.

It is found that optimal combination of helix angle 40°, nose radius 0.8 mm, rake angle 12°, cutting speed 90 m/min, feed rate 0.04 mm/rev and depth of cut 1.5 mm have generated minimum surface roughness of 0.4063 µm and tool wear of 0.0375 mm. From ANOVA analysis, it is found that cutting speed influence is more on output performance followed by helix angle and rake angle compared with other machining and geometrical parameters.

The influence of tool geometry during end milling of MMC using Grey-fuzzy logic algorithm has not been explored previously.

This paper aims that optimization parameters depending on machining processes examine to define for the milling process of AL 6061-T6 aluminum alloy used in the aviation industry.

The Taguchi method was used to study the optimal parameters. Furthermore, the effects of machining parameters on surface roughness were also evaluated by performing variance analysis. Optimum parameter levels were determined by Signal/Noise analysis.

It was determined that the parameter levels that optimize the surface roughness were “4000 rev/min for the rotational speed of the cutting tool, 0.4 mm for the cutting depth and the optimum value for the feedrate 500 mm/min.”

It is limited by the precision of the manufacturing processes, the desired geometry and the exactness of the measurement make the machine productivity valuable in the production of parts.

By improving the optimal production parameters, reducing part production costs and waste amount in aviation has been seen as an important gain.

Improving production methods and optimization parameters in production technologies will ensure the minimization of loss and waste. These developed parameters with optimizing the surface roughness will add value in this context.

It was determined that the parameter levels that optimize the surface roughness of aluminum considering manufacturing processes. Especially as process parameters, optimum feed rate has been developed for effective rotation speed and cutting depth for cutting tools.

Force output is extremely important for electromagnetic linear machines. The purpose of this study is to explore new permanent magnet (PM) array and winding patterns to increase the magnetic flux density and thus to improve the force output of electromagnetic tubular linear machines.

Based on investigations on various PM patterns, a novel dual Halbach PM array is proposed in this paper to increase the radial component of flux density in three-dimensional machine space, which in turn can increase the force output of tubular linear machine significantly. The force outputs and force ripples for different winding patterns are formulated and analyzed, to select optimized structure parameters.

The proposed dual Halbach array can increase the radial component of flux density and force output of tubular linear machines effectively. It also helps to decrease the axial component of flux density and thus to reduce the deformation and vibration of machines. By using analytical force models, the influence of winding patterns and structure parameters on the machine force output and force ripples can be analyzed. As a result, one set of optimized structure parameters are selected for the design of electromagnetic tubular linear machines.

The proposed dual Halbach array and winding patterns are effective ways to improve the linear machine performance. It can also be implemented into rotary machines. The analyzing and design methods could be extended into the development of other electromagnetic machines.

This paper aims to analyze the significance of machining parameters (workpiece’s rotational speed, magnet coil current and working gap) on final Ra (surface roughness) and material removal rate (MRR) of workpiece in reciprocating magnetorheological polishing (RMRP) process.

The research is planned to analyze, model and predict the optimum machining parameters to anticipate final Ra and MRR by applying response surface methodology (RSM) and multiresponse optimization (desirability function approach). The experiments have been planned by design of experiments (DOE). Analysis of variance (ANOVA) is applied to determine the significances of machining parameters on RMRP performance characteristics.

Response surface plots for final Ra and MRR by RSM show that machining parameters are significant for the responses. The optimum machining parameters obtained are optimized by desirability function approach (DFA), and the optimum parametric combination has been validated by confirmatory experiments. The experimental results of the final Ra and MRR are deviated by 5.12% and 2.31% from the response results under the same optimization conditions, respectively.

In this study, the RMRP responses (final Ra and MRR) are improved at predicted input machining parameters condition obtained by RSM and DFA approach. Furthermore, the research results provide a reference for experimental design and optimization of MRP process.