Optimization of machining parameters for turning process by using grey relational analysis

The present paper investigates the cutting parameters pertaining to the turning of X2CrNi18-09 austenitic stainless steel that are studied and optimized using both RSM and desirability approaches. The cutting tool inserts used are the CVD coated carbide. The cutting speed, the feed rate and the depth of cut represent the main machining parameters considered. Their influence on the surface roughness and the cutting force are further investigated using the ANOVA method. The results obtained lead to conclude that the feed rate is the surface roughness highest influencing parameter with a contribution of 89.69%.The depth of cut and the feed rate are further identified as the most important parameters affecting the cutting force with contributions of 46.46% and 39.04% respectively. The quadratic mathematical models presenting the progression of the surface roughness and the cutting force and based on the machining parameters considered (cutting speed, feed rate and depth of cut) were obtained through the application of the RSM method. They are presented and compared to the experimental results. Good agreement is found between the two sections of the investigation. Furthermore, the flank wear of the CVD-coated carbide tool (GC2015) is found to increase with both cutting speed and cutting time. A higher tool life represented by t=44min is observed at cutting speed, feed rate and depth of cut of 280m/min,0.08mm/rev and 0.2mm respectively. Moreover and at low cutting speeds, the formation of micro weld is noticed and leads to an alteration of the surface roughness of the work piece. Finally, optimizing the machining parameters with the objective of achieving an improved surface roughness was accomplished through the application of the Desirability Function approach. This enabled to finding out the optimal parameters for maximal material removal rate and best surface quality for a cutting speed of 350m/min, a feed rate of 0.088 mm/rev and a depth of cut of 0.9mm.

When it comes to machining, manufacturers are always striving for the best possible outcomes. The improvement of the precision and productivity of material processing is the primary goal to enhance Wire Electrical Discharge Machining (EDM) parameters. Whether it's a fragile substance or one that conducts electricity, traditional approaches generally fall short. Maximizing the benefits of machining includes higher product quality, greater production efficiency, and lower production costs. Therefore, industries work hard to fine-tune the EDM settings. Within the scope of this investigation, HSS (8X200) wire EDM parameters are optimized. Finding the ideal machining settings that enhance surface roughness (SR), material removal rate (MRR), and cutting speed (CS) while maintaining economic feasibility is the goal. The Taguchi design parameter and Grey Relational Analysis (GRA) are both utilized in this research. Machining parameters such as pulse on time, pulse off time, servo feed, and servo voltage can be designed, tested, and determined with the use of the Taguchi technique. GRA analyzes experimental data and determines optimal parameter values. In order to evaluate the quality of the machining, wire-cut machines measure SR, MRR, and CS. The settings are validated using the ANOVA. In the fifth experiment, the ideal HSS (8X200) wire EDM parameters were determined. According to the findings of the study, the factors that have the largest influence on SR, MRR, and CS are pulse on time (55.43%), servo feed (17.62%), pulse off time (14.13%), and servo voltage (12.82%).

In presentt-day scenario, energy-efficient product or process is highly in demand. In the present work, we have made an attempt to study the effects of machining parameters in turning process of AISI 4340 steel by using grey relational analysis. Experiments were performed using Taguchi experimental design to reduce the number of experiments in the study. Taguchi’s L9 orthogonal array was chosen for design of experiments. Experiments were done by carrying out turning operations on dry cutting conditions by using high-quality multilayer-coated titanium carbide tool insert. Cutting speed, feed and depth of cut were chosen as the machinability parameters. The aim of grey analysis is to get the best set of parameters for getting optimized value of material removal rate (MRR) and surface roughness. The levels of cutting speed chosen were 160, 200 and 240 m/min, feed levels were 0.14, 0.18 and 0.22 mm/rev, and depths of cut were 0.5, 0.75 and 1 mm, respectively. The results of grey analysis showed that cutting speed 240 m/min, feed 0.5 mm/rev and depth of cut 0.75 gave the optimized value for MRR and surface roughness. Hence, using both grey analysis and Taguchi technique, we can considerably reduce the energy consumption by improving the machining process making the process easier and smoother to machine and energy efficient by eliminating the unnecessary effort.

Development of new materials is a never-ending process in material science. Novel materials need to have good machinability for it to be industrially applicable. In the current work, multi-walled carbon nanotubes (MW-CNTs) and silicon carbide (SiC) reinforced hybrid aluminium composite (HAC) is developed using stir casting route. Scanning electron microscopy (SEM) images reveal proper mixing with very little agglomeration of reinforcement particles. Hardness and corrosion resistance were also improved in comparison with the base alloy. The developed composite was machined using conventional milling machine following L27 Taguchi orthogonal array experimental design. Material removal rate (MRR) and surface roughness (SR) were optimised using Taguchi signal-to-noise (S/N) ratio, grey relation analysis (GRA) and dragonfly algorithm (DA). Single objective optimisation revealed that low spindle speed and high feed rate and depth of cut would result in maximum MRR while high spindle speed, feed rate and depth of cut would result in minimum SR. DA could successfully predict the optimum MRR with less than ± 5 % error suggesting it to be a reliable tool for optimisation problems. Pareto front for multi-objective optimisation revealed that a proportionate compromise between MRR and SR can be made to identify optimum processing parameters in the experimental space.

Effect of process parameters on centre lathe of EN8 steel in turning process

Incoloy 800H is an austenitic Fe-Ni-Cr based superalloy and used in many applications due to their high corrosion resistance and creep-strength. However, this alloy is difficult to machine or cut material because of their eminent characteristics such as rapid-work hardening, lesser thermal conductivity and easy to tool-material attraction. Hence, the necessary experimental investigation is required to study and optimize the turning parameters of this alloy. This work presents the investigation of turning parameter effects on the Incoloy 800H superalloy with cryogenically treated cutting tool. The dry turning experiments were conducted based on Taguchi L9 Orthogonal array (OA) with the input parameters of cutting speed, feed rate and depth of cut. The outputs such as material removal rate, surface roughness, cutting force and tool-tip temperature were considered as the responses. The measured output responses were optimized and modeled using Taguchi-based Grey relational analysis (GRA) and response surface methodology (RSM), respectively. The tool flank wear and tool life were examined on coated insert with cryogenically treated, coated insert (without cryogenic treatment) and uncoated insert for comparison. The results revealed that greater amount of tool-wear reduction was observed in the case of coated tool with-cryogenically treated about 47.88%, coated tool without-cryogenically treated about 27.51% when compared with an uncoated tool. Besides, analysis of variance (ANOVA) was performed to find the most significant parameter over the obtained responses. The obtained mathematical model through RSM was agreed with the experimental result. Further, the machined surface topography was examined using White Light Interferometer (WLI).

The aim of this research is to study the machinability aspects of hardened AISI 4340 High Strength Low Alloy (HSLA) steel (50 ± 2 HRC (Hardness Rockwell C)). The experimental investigation using coated carbide inserts is carried out during the dry hard milling process in a sustainable environment. The input parameters in the study are speed, feed rate and depth of cut and the responses are Average surface Roughness (Ra) and Material Removal Rate (MRR) that are selected through screening. Central Composite Design (CCD) in response surface methodology has been utilized as the experimental design technique with twenty experiments. Analysis of variance has been employed to examine the momentous machining parameters and responses. A mathematical model has been developed to optimize the surface roughness and material removal rate. It has been observed that the most significant factor for Ra is feed rate while for MRR depth of cut is the most significant factor. The results show that the minimum value of Ra ~ 0.098 μm is achieved at speed ~ 1000 RPM, feed rate ~ 300 mm/min and depth of cut ~ 0.2 mm while the maximum value of MRR ~ 6.35 cm3/min is attained at feed rate ~ 500mm/min and depth of cut ~ 0.4 mm regarding less or no effect of speed ~ 500-1000 RPM. The average forecast error for the validation information has been observed to be 3.35%. for Ra and 3.2% for MRR. Further, it is investigated that good surface finish like grinding and dimensional accuracy can be achieved with coated carbide tools.

Input parameter like the cutting fluid is one of the requirements for minimal surface roughness, cutting temperature, tool wear and optimal material removal rate coupled with improved machinability and productivity. The evaluation of the optimal factors of surface roughness, material removal rate, cutting temperature and tool wear in the turning of AISI Mild Steel with the use of eco-friendly fluids. Concerns has been raise globally about the non - biodegradability and non-recyclability of the conventional fluids in the research space. This prompted the research interest in replacing the mineral oil based fluids with eco-friendly cutting fluid such as castor seed oil based cutting fluid (CBCF). The locally sourced castor seed oil was investigated for its physiochemical properties as well as its fatty acid composition (FAC). The cutting fluid was formulated using ratio 1:9 of oil with additives to distilled water and then characterized. In turning of AISI 1020 Mild Steel, the evaluation of surface roughness, material removal rate, cutting temperature and tool wear under the CBCF compared to the mineral oil based cutting fluid (MBCF) were carried out using Taguchi experimental design and Grey Relational Analysis (GRA) for multi-response optimization. The formulated cutting fluid showed pH value of 8.47, viscosity of 0.830 mm2/s, good resistance to corrosion, good stability and milkfish in colour. From the GRA, the multi-response optimal factor combination under the CBCF is (1250 rev/min) spindle speed, (0.6 mm/rev) feed rate and (1.0 mm) depth of cut, all at level 3 while under the MBCF, it also shows (1250 rev/min) spindle speed, (0.6 mm/rev) feed rate and (1.0 mm) depth of cut all at level 3. The parameters from Taguchi and GRA results are in agreement with results from other vegetable oil based fluids and this study also contributes and improves the science of machining.

Surface finish and increased productivity (increased material removal rate (MRR)) are two premier performance parameters in machining and determination of optimal cutting parameters influencing the machining performance is paramount for different cutting conditions, cutting tools and workpiece materials. This study focuses on optimising a set of cutting parameters (cutting speed, feed rate and depth of cut) during dry turning operation of AISI H13 tool steel using a brazed uncoated tungsten carbide tip as the cutting tool material. Taguchi's L9 orthogonal array was employed for minimising surface roughness and maximising MRR based on signal-to-noise ratio results. Analysis of variance (ANOVA) conducted showed that the feed rate affected the performance responses (surface roughness and MRR) the most. Additionally, artificial neural network (ANN) and regression models were developed for both surface roughness and MRR, which showed promising prediction capability for calculating the surface roughness and MRR at any combination of cutting speed, feed and depth of cut.

The present work focuses to analyse the importance of turning parameters on the responses: machining time, surface roughness and material removal rate in CNC turning while machining of aluminium alloy Al-6082 T6 using tungsten carbide tool. Cutting speed, feed rate and depth of cut with three levels each have been considered in the current work as the machining parameters. The present study uses the Taguchi's DOE methodology, grey relational approach and principal component analysis (PCA) to optimise the response parameters simultaneously. Experiments have been conducted as per Taguchi's L9 orthogonal array. The experimental results were then analysed using the grey relational analysis (GRA) along with the principal component analysis (PCA). The speed and feed are observed to be statistically significant on the responses whereas the depth of cut is insignificant. Optimal levels for the parameters are determined using the grey Taguchi method and PCA. The confirmation test is carried out and the results are validated.

Nickel-based Inconel X-750 superalloy is widely applied in aerospace industry and manufacturing of gas turbine blades, power generators and heat exchangers due to its exclusive properties. As a consequence of low heat transfer coefficient and work-hardening properties, this alloy is known as a poorly machinable alloy. In this work, effect of machining parameters (cutting speed, feed rate and depth of cut) on cutting forces and surface roughness was investigated during turning of Inconel alloy X-750 with coated carbide tool. In order to meet the demands of the environment-friendly cutting processes and human health, biodegradable vegetable oil (BioCut 4600) was selected as the cutting fluid. The results were analyzed using response surface methodology and statistical analysis of variance, and mathematical models for cutting forces and surface roughness were proposed. Results indicated that feed rate and cutting speed were the most effective parameters on the surface roughness. However, depth of cut was the most effective parameter on cutting forces in comparison with cutting speed and feed rate. Eventually, in order to achieve the main aims of industrial production in large amounts and green manufacturing, the ranges for the best cutting conditions were presented.

The study investigates the efficacy of eco-friendly cooling fluids, specifically vegetable oil and water mixtures, in the machining of EN19 steel, with a focus on enhancing performance metrics while promoting environmental sustainability.Machining parameters, including cutting speed, feed rate, and depth of cut (DOC), were analyzed for their effects on surface roughness, tool temperature, cutting forces, and material removal rate (MRR).The study employed a hybrid optimization approach, integrating Taguchi's orthogonal array (OA) method with grey relational analysis (GRA), to evaluate the effectiveness of these eco-friendly cutting fluids.The analysis revealed that spindle speed significantly influenced the MRR, while the DOC notably affected cutting force and tool temperature.The choice of coolant was found to have a considerable impact on surface roughness.Although the Taguchi method effectively optimized individual machining parameters, GRA provided a more comprehensive evaluation by synthesizing multiple performance metrics into a single index, achieving an accuracy of 80.17%, which surpassed the 72.44% accuracy of the Taguchi method.These findings underscore the potential of GRA to optimize the machining process of EN19 steel, offering substantial improvements in manufacturing efficiency and sustainability.The study highlights the importance of adopting eco-friendly practices in industrial machining, demonstrating that the integration of GRA and Taguchi methods can lead to more sustainable and efficient manufacturing processes.

This paper describes the important significance of cutting-edge technology in the machining of polycrystalline diamond (PCD) cutting inserts by comparing the evaluation criteria. The LASER technology of cutting-edge machining is compared with grinding and electrical discharge machining (EDM) technologies. To evaluate the data from the experiments, the Grey Relational Analysis (GRA) method was used to optimize the input factors of turning to achieve the required output parameters, namely the deviation of roundness and chip cross-section. The input factors of cutting speed, feed rate, depth of cut and corner radius were applied in the experiment for three different levels (minimum, medium and maximum). The optimal input factors for turning of aluminum alloy (AW 5083) were determined for the factorial plan according to Grey Relational Grade based on the GRA method for the multi-criteria of the output parameters. The results were confirmed by a verification test according to the GRA method and optimal values of input factors were recommended for the machining of Al-alloy (AW 5083) products. This material is currently being developed by engineers for forming selected components for the automotive and railway industries, mainly to reduce weight and energy costs. The best values of the output parameters were obtained at a cutting speed of 870 m/min, feed rate of 0.1 mm/min, depth of cut of 0.5 mm and a corner radius of 1.2 mm.

Lack of knowledge and comprehension of critical input parameters and material machinability has limited the industry's use of machining, making it challenging to meet requirements for machining responses and numerous other problems. This study uses response surface methods to evaluate Aluminum Alloy 6351 Eggshell Reinforced Composite as a turning machining material. The material removal rate (MRR), cutting force (Fc), and surface roughness (Ra) of the samples were examined. The mass percentage of the composite is 15% egg shell reinforcement and 85% aluminum alloy. Cutting force (Fc), surface roughness (Ra), and material removal rate (MRR) ANOVA tables show that several models have significant probability values (P-values) less than 0.05. Numerical optimization was used to identify combinations of process parameters that will give the best response of cutting force (Fc), surface roughness (Ra), and material removal rate (MRR). The Cutting force (Fc), surface roughness (Ra), and material removal rate (MRR) can all be predicted using the regression equation model that was created. The only input variable that significantly affects the cutting force is cutting speed. The three-input variable studied has a significant effect on surface roughness (Ra) and material removal rate (MRR). The optimization result obtained indicates that the optimal response for turning an aluminum alloy 6351 eggshell reinforced composite is 1.39676µm, 101.333N, and 2016.77mm3/min for surface roughness (Ra), cutting force (Fc), and material removal rate (MRR), respectively. This is achieved when the input variables of cutting speed (Vc), feed rate (Fr), and depth of cut (Dc), which are 589.479 rpm, 0.205976 mm/min, and 0.315524 mm, respectively, are used.

PurposeMetal matrix composites (MMC) has been a section which gives an overview of composite materials and owing to those exceptional physical and mechanical properties, particulate-reinforced aluminum MMCs have gained increasing interest in particular engineering applications. Owing to the toughness and abrasive quality of reinforcement components such as silicon carbide (SiC) and titanium carbide (TiC), such materials are categorized as difficult materials for machining. The work aims to develop the model for evaluating the machinability of the materials via the response surface technique by machining three distinct types of hybrid MMCs.Design/methodology/approachThe combined effects of three machining parameters, namely “cutting speed” (s), “feed rate” (f) and “depth of cut” (d), together with three separate composite materials, were evaluated with the help of three performance characteristics, i.e. material removal rate (MRR), cutting force (CF) and surface roughness (SR). Response surface methodology and analysis of variance (ANOVA) both were initially used for analyzing the machining parameters results.FindingsThe contours were developed to observe the combined process parameters along with their correlations. The process variables were concurrently configured using grey relational analysis (GRA) and the composite desirability methodology. Both the GRA and composite desirability approach obtained similar results.Practical implicationsThe results obtained in the present paper will be helpful for decision-makers in manufacturing industries, who work on metal cutting area especially composites, to select the suitable solution by implementing the Grey Taguchi and modeling techniques.Originality/valueThe originality of this research is to identify the suitability of process parameters combination based on the obtained research results. The optimization of machining parameters in turning of hybrid metal matrix composites is carried out with two different methods such as Grey Taguchi and composite desirability approach.