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The purpose of this paper is to examine the performance parameters of WEDM to improve the productivity and material removal rate (MRR) with a high surface finish of high chromium-high carbon dies steel.

The experiments were performed on AGIE CUT 220 CNC WEDM. High chromium-high carbon dies steel (D3) was used in the form of a rectangular plate. The workpiece and the brass wire having diameter ɸ 0.25 mm had linked up with +ve and –ve polarity in the DC power source, respectively. De-ionized water having a conductivity level of 0.6 µs/cm was used as the dielectric medium. The dielectric fluid was flushed from the top and bottom nozzles and material was submerged in the dielectric.

The WEDM process parameters for D3 die steel had optimized by using Grey relational analysis method couples with Taguchi method. The optimum solution has been calculated for MRR, cutting speed (Cs), machining time and surface roughness (SR) (Ra value). A fuzzy logic model using Matlab was developed for the prediction of performance parameters, namely MRR, cutting speed (Cs), machining time (M/c time) and SR with respect to changes in input parameters.

The fuzzy model shows the 96.19 percent accuracy between the experimental values and the predicted values.

The optimized parameters by multi-parametric optimization method showed considerable improvement in the process and will facilitate the WEDM, tool and die industries, defense and aerospace industries to improve the productivity with the higher surface finish.

This manuscript represents valid work and the authors have no conflict of interests. The attained optimum outcomes had also been examined through a real experiment and established to be satisfactory.

This study aims to investigate the influence of process parameters of wire electrical discharge machining (WEDM) of Nimonic75. Nimonic75 is a Nickel-based alloy mostly used in the aerospace industry for its strength at high temperature.

One factor at a time (OFAT) approach has been used to perform the experiments. Pulse on time, pulse off time, peak current and servo voltage were chosen as input process parameters. Cutting speed, material removal rate and surface roughness (Ra) were selected as output performance characteristics.

Through experimental work, the effect of process parameters on the response characteristics has been found. Results identified the most important parameters to maximize the cutting speed and material removal rate and minimize Ra.

Very limited research work has been done on WEDM of Nickel-based alloy Nimonic75. Therefore, the aim of this paper to conduct preliminary experimentation for identifying the parameters, which influence the response characteristics such as material removal rate, cutting speed, Ra, etc. during WEDM of Nickel-based alloy (Nimonic75) using OFAT approach and found the machinability of Nimonic75 for further exhaustive experimentation work.

The purpose of this paper is to analyze the surface integrity, including recast layer thickness, surface crack density, X-ray diffractions study and microhardness for Ni53.49Ti46.51 shape memory alloy (SMA) during wire-spark erosion machining.

Four persuasive process parameters, that is, spark on time (SON), spark off time (SOFF), wire feed (WF) and spark gap voltage (SV), have been chosen for the current investigation. Efforts have been done to explore the effects of above said parameters on the machined surface of Ni-Ti SMA by embracing box Behnken design of response surface methodology (RSM). Cutting speed and ten-point mean roughness (Rz) has been taken into account as response variables. Analysis of variance test was also performed for both response parameters with the coefficient of determination (R²) 0.9610 for cutting speed and 0.9252 for ten-point mean Rz.

The recast layer thickness from 7.83 to 12.13 µm was developed near the machined surface at different parametric settings. The least surface crack density was found at the lowest value of ten-point mean Rz, while most surface crack density was identified at the highest value of cutting speed. The microhardness near the machined surface was increased by approximately 1.8 times bulk-hardness of Ni53.49Ti46.51 SMA.

Some researchers have done a study on average surface roughness, but very few investigators concentrated on ten-point mean Rz. Surface crack density is an essential aspect of machined parts; other researchers have seldom reported it. The novelty of this research work is that the influence of SON, SV, WF and SOFF on cutting speed, Rz, recast layer thickness, micro-hardness and surface crack density proximate the machined surface while machining workpiece material.

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

The knowledge over the performance of cutting fluids when applied under different machining conditions (such as distinct work material and cutting parameters) is critical in order to improve the efficiency of most machining operations. This paper is concerned with the performance of cutting fluids employed under two distinct machining operations involving aluminium alloys: drilling of AA 1050‐O aluminium applying cutting fluid as a mist and turning of AA 6262‐T6 aluminium alloy using cutting fluids (as a flood) with distinct extreme pressure additives (chlorine, sulphur and phosphor).

This work reports on a experimental study of the performance of cutting fluids when machining aluminium alloys.

The results indicated an increase in the flow rate of the mist led to lower feed forces but higher torque, power consumption and specific cutting pressure in the drilling operation (AA 1050‐O aluminium). The surface finish was not drastically affected by the cutting fluid flow rate. When turning AA 6162‐T6 aluminium alloy, in general, best results were observed using 10 per cent fluid concentration applied at the tool‐workpiece interface. The cutting fluid containing chlorine as extreme pressure additive produced lower cutting forces and better surface finish at high cutting speed and low feed rate and depth of cut.

The novel element of this paper is the use of minimal lubrication (drilling) and cutting fluids with distinct extreme pressure (turning).

This study was conducted to analyze the effects of machining parameters on the specific energy consumption in the computerized numerical control lathe turning operation of a hardened alloy steel roll at low cutting speeds. The aim was to minimize its consumption.

The design matrix was based on three variable factors at three levels. Response surface methodology was used for the analysis of experimental results. Optimization was carried out by using the desirability function and genetic algorithm. A multiple regression model was used for relationship build-up.

According to desirability function, genetic algorithm and multiple regression analysis, optimal machining parameters were cutting speed 40 m/min, feed 0.2 mm/rev and depth of cut 0.50 mm, which resulted in minimal specific energy consumption of 0.78, 0.772 and 0.78 kJ/mm³, respectively. Correlation analysis and multiple regression model found a quadratic relationship between specific energy consumption with power consumption and material removal rate.

In the past, many researchers have developed mathematical models for specific energy consumption, but these models were developed at high cutting speed, and a majority of the models were based on the material removal rate as the independent variable. This research work developed a mathematical model based on the machining parameters as an independent variable at low cutting speeds, for a new type of large-sized hardened alloy steel roll. A multiple regression model was developed to build a quadratic relationship of specific energy consumption with power consumption and material removal rate. This work has a practical application in hot rolling industry.

This paper aims to focus on the application of multi-attribute decision-making methods (MADMs) to ascertain the optimal machining parameters while turning Ti-6Al-4V alloy under minimum quantity lubrication (MQL) conditions using Jatropha-curcas oil (JCO) bio-based lubricant.

The experiments were designed and performed using Taguchi L27 design of experiments methodology. A total of 27 experiments were performed under MQL conditions using textured carbide cutting tools on which different MADMs like Analytic hierarchy process (AHP), Technique for order preference by similarity to ideal solution (TOPSIS) and Simple additive weighting (SAW) were implemented in an empirical manner to extract optimize machining parameters for turning of Ti-6Al-4V alloy under set of constrained conditions.

The results evaluated through MADMs exhibit the optimized set of machining parameters (cutting speed V_c = 80 m/min, feed rate f = 0.05 mm/rev. and depth of cut a_p = 0.10 mm) for minimizing the average surface roughness (Ra), maximum flank wear (Vbmax), tangential cutting force (Fc) and cutting temperature (T). Further, analysis of variance (ANOVA) and traditional desirability function approach was applied and results of TOPSIS and SAW methods having optimal setting of parameters were compared as well as confirmation experiments were conducted to verify the results. A SEM analysis at lowest and highest cutting speeds was performed to investigate the tool wear patterns. At the highest speed, large cutting temperature generated, thereby resulted in chipping as well as notching and fracturing of the textured insert.

The research paper attempted in exploring the optimized machining parameters during turning of difficult-to-cut titanium alloy (Ti-6AL-4V) with textured carbide cutting tool under MQL environment through combined approach of MADMs techniques. Ti-6Al-4V alloy has been extensively used in important aerospace components like fuselage, hydraulic tubing, bulk head, wing spar, landing gear, as well as bio-medical applications.

The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra).

The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments.

The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results.

The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity.

No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

This work aims to describe the face milling analysis on Inconel X-750 superalloy using coated carbides. The formed chips and tool wear were further analyzed at different cutting parameters. The various impact of cutting parameters on chip morphology was also analyzed. Superalloys, often referred to as heat-resistant alloys, have exceptional tensile, ductile and creep strength at high operating temperatures and good fatigue strength, and often better corrosion and oxidation resistance at extreme heat. Because of these qualities, these alloys account for more than half of the weight of sophisticated aviation, biomedical and thermal power plants today. Inconel X-750 is a high-temperature nickel-based superalloy that is hard to machine because of its extensive properties. At last, the discussion regarding the tool wear mechanism was analyzed and discussed in this article.

The machining parameters for the study are cutting speed, feed rate and depth of cut. One factor at a time approach was implemented to investigate the effect of cutting parameters on the cutting forces, surface roughness and material removal rate. The scatter plot was plotted between cutting parameters and target functions (cutting forces, surface roughness and material removal rate). The six levels of cutting speed, feed rate and depth of cut were taken as cutting parameters.

The cutting forces are primarily affected by the cutting parameters, tool geometry, work material etc. The maximum forces Fx were encountered at 10 mm/min cutting speed, 0.15 mm/rev feed rate and 0.4 mm depth of cut, further maximum forces Fy were attained at 10 mm/min cutting speed, 0.25 mm/rev feed rate and 0.4 mm depth of cut and maximum forces Fz were attained at 50 mm/min cutting speed, 0.05 mm/rev feed rate and 0.4 mm depth of cut. The maximum surface roughness value was observed at 40 mm/min cutting speed, 0.15 mm/rev feed rate and 0.5 mm depth of cut.

The effect of machining parameters on cutting forces, surface roughness, chip morphology and tool wear for milling of Inconel X-750 high-temperature superalloy is being less researched in the present literature. Therefore, this research paper will give a direction for researchers for further studies to be carried out in the domain of high-temperature superalloys. Furthermore, the different tool wear mechanisms at separate experimental trials have been explored to evaluate and validate the process performance by conducting scanning electron microscopy analysis. Chip morphology has also been evaluated and analyzed under the variation of selected process inputs at different levels.

The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42.

The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (F_c) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L₁₈ mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method.

The optimum SR, cutting force (F_c) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics.

To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.