Optimization Of Material Removal Rate Research Articles

Material removal rate optimization with bayesian optimized differential evolution based on deep learning in robotic polishing

Large aperture aspheric optical surfaces (LAAOS) have been applied in many industries, but their high requirements for precision and efficiency make manufacturing more challenging. Robotic polishing is a representative computer-controlled optical surfacing technique to manufacture LAAOS with low-cost and high-efficiency. However, how to achieve the highest material removal rate (MRR) involves many process parameters. It is difficult to determine the optimal parameter settings since the complex relationships among them. In this paper, a novel Bayesian optimized differential evolution based on deep learning method is proposed to optimize the MRR, in which the designed deep neural network is responsible for MRR modeling and Bayesian optimized differential evolution is used for MRR optimization. Bayesian optimization is used to find the best hyperparameter of differential evolution method so as to improve optimization performance. To evaluate the proposed method, a series of robotic polishing experiments are conducted to build the MRR model. The optimization performance comparison experiments show the superiority of our proposed method, which increases MRR by an average of 0.16.

Optimization of Material Removal Rate and Wear Rate in EDM Machining of Inconel 625 Using a Novel Nano Al-Ni Composite Electrode: A Response Surface Methodology Approach

The electric discharge machine is a novel machining technique. When the tool and the work item do not communicate. Components composed of difficult-to-machine hard materials. In the current experimental inquiry, the process factors that influence the machining concert process and its effectiveness are explored. The response table for each set of machining limitations is used in conjunction with the response surface methodology in the research to determine the appropriate levels of machining factors. The work's machining input parameters are optimized for maximum material removal rate (MRR) and reduce the electrode wear rate (EWR) when electro-discharge machining Inconel 625 using the tool Nano Al-Ni composite electrode. Another analysis of variance is to identify the factors causing the different answers mentioned above. EDAX was also used to investigate the material composition of the workpiece. Machined workpiece surfaces were evaluated using scanning electron microscopy to assess surface accuracy and microstructural characteristics (SEM). The following conclusions may be drawn from this work. The MRR parameter that is most affected is pulse off time. As pulse-off time lengthens, MRR increases. According to the response graph, the greatest MRR value that can be attained under the ideal parameters of Ip = 8A, T-ON = 50μs, and T-OFF= 100μs is 0.0115 g/min. The magnitudes of MRR range from 0.0091 to 0.044 g/min. Impact of T ON and T OFF: Higher EWR is 0.0022 g/min is seen in runs with T ON = 70 μs and T OFF = 100 μs.

Multiobjective optimization of end milling parameters for enhanced machining performance on 42CrMo4 using machine learning and NSGA-III

The present study analyzes and optimizes machining characteristics, including feed rate (fz), depth of cut (ap), cutting speed (Vc), cutter-coated material (Mtc) and cutting-edge radius (rt), impacting on surface roughness (Ra), material removal rate (MRR) and tool wear (VB) of 42CrMo4 steel during dry end-milling. A total of 108 experimental runs were conducted, focusing on Ra, VB and MRR as response parameters. The nano TiAlN PVD-coated tool yielded better results for Ra and VB than did the TiCN/Al2O3 MT-CVD-coated tool. Then, Ra, VB and MRR optimization was carried out simultaneously using a Non-Dominated Sorting Genetic Algorithm III (NSGA-III) and Machine Learning (ML) models. Pareto solutions were found to offer a range of values for the three performance objectives: Ra (0.315–0.556 µm), VB (12.33–32.48 µm) and MRR (0.44–3.58 cm3/min). A quantitative performance score (Ps) ranking index was calculated to rank Pareto solutions for practical case studies. Validation experiments were subsequently performed to affirm that the optimal solution fell within a reasonable error range, with MAPE of 9.58% for Ra, 9.25% for VB and 13.39% for MRR. The validation results underscore the versatility of this approach, suggesting its applicability to a wide array of machining optimization challenges.

Optimization of Material Removal Rate in Computer Numerical Control Lathe Machine in Turning AISI 1040 Steel

Optimization of Material Removal Rate in Computer Numerical Control Lathe Machine in Turning AISI 1040 Steel

Analysis of Machining Parameters for Turning of AL-6061 and Optimization

The paper shows and includes targeted supervision of optimizing machining parameters. Material Removal Rate and surface roughness being integral to machining efficacy of any workpiece , simulation based modeling helps in failure mitigation.Thepotential of ANN-GA mathematical approach for prediction and optimization of MRR and surface roughness of AL 6061 , a analysis based statistical study has been discussed. The computational model between the desired output and the inputs have been configured using Multiple Regression- Genetic Algorithm and Artificial Neural Network(ANN) methods. The closeness in predicted and optimized data sets were mapped using integrational ANN with GA to interpolate efficacy in optimality. Keywords: Artificial Neural Network(ANN)k, Material Removal Rate(MRR), Genetic algorithm(GA), Surface roughness , depth of cut , feedrate , Regression analysis, ANOVA

Optimization of EDM parameters using Ananya algorithm: A new advanced optimization technique

Titanium alloys are nowadays machined using electric discharge machining (EDM). EDM is an unconventional machining tool which is used to produce complex geometric profiles. The major limitation of EDM machining is the low material removal rate (MRR) hence the MRR need to be maximized. There are so many modeling techniques available nowadays like regression modeling, response surface modeling, artificial neural network etc. Optimization frequently relies on the use of a predictive model for achieving the best possible outcomes. Through the utilization of a computer and a well-suited optimization algorithm, it becomes feasible to attain an optimal result. Nowadays metaheuristic-based optimization techniques are very popular for optimization because traditional optimization is often unable to solve this type of problem. The result is either the optimum one or very near to the optimum solution. Genetic algorithm (GA), particle swarm optimization (PSO), and Ant colony optimization (ACO) is more popular algorithm nowadays. This research paper shows the EDM parameter optimization of MRR and tool wear rate (TWR) for titanium alloy Ti6Al4V. The Ananya algorithm is used to optimize the MRR and TWR. Ananya algorithm is very simple and easier to implement. Furthermore, it always achieved optimum solutions in every run. Ananya algorithm is compared to other algorithms.

Optimization of material removal rate and surface roughness during electric discharge machining of ultra‐fine grained Al6082 using Taguchi technique

AbstractIn the current work, the statistical analysis of various electric discharge machining parameters on Al6082 ultra‐fine grained aluminium alloy using Taguchi method has been presented. Repetitive corrugation and straightening (RCS) method was employed to obtain ultra‐fine grained aluminium alloy. The electric discharge machining studies were carried out for test variables – pulse off time, pulse on time and current (I). The specimens were machined in dielectric medium with current range of 3 A to 9 A in step of 3 A. Machining features of the samples analysed statistically by adopting the Taguchi's ‐ design of experiments (DOE) methodology. Impact of parameters on material removal rate (MRR) and surface roughness (SR) were examined via signal‐to‐noise ratio (S/N ratio, expressed in decibel, dB) as well as analysis‐of‐variance (ANOVA). Outcomes disclose that every selected response explicitly surface roughness (SR) and material removal rate was significantly influenced by parameters. The material removal rate was found to rise with discharge current and decrease with the duration of pulse on time and the duration of pulse off time. On the other hand, the surface roughness increased with increase in peak current and decreased with pulse on time and pulse off time especially. The machining mechanisms were examined by scanning electron microscopy.

Implementation of Passing Vehicle Search Algorithm for Optimization of WEDM Process of Nickel-Based Superalloy Waspaloy.

Nickel-based superalloys find their main use in missile engines, atomic devices, investigational aircraft, aerospace engineering, industrial applications, and automotive gas turbines, spacecraft petrochemical tools, steam power, submarines, and broader heating applications. These superalloys impose certain difficulties during the process fabrication owing to their levels of higher hardness. In the current study, the precise machining of Waspaloy was attempted through the wire electrical discharge machining (WEDM) technique. A multi-objective optimization has been performed, and the influence of multi-walled carbon nanotubes (MWCNTs) has been assessed using the passing vehicle search (PVS) algorithm. The effects of machining variables like current, Toff, and Ton were studied using the output measures of material removal rate (MRR), recast layer thickness (RLT), and surface roughness (SR). The Box-Behnken design was applied to generate the experimental matrix. Empirical models were generated which show the interrelationship among the process variables and output measures. The analysis of variance (ANOVA) method was used to check the adequacy, and suitability of the models and to understand the significance of the parameters. The PVS technique was executed for the optimization of MRR, SR, and RLT. Pareto fronts were derived which gives a choice to the user to select any point on the front as per the requirement. To enhance the machining performance, MWCNTs mixed dielectric fluid was utilized, and the effect of these MWCNTs was also analyzed on the surface defects. The use of MWCNTs at 1 g/L enhanced the performance of MRR, SR, and RLT by 65.70%, 50.68%, and 40.96%, respectively. Also, the addition of MWCNTs has shown that the machined surface largely reduces the surface defects.

Experimental investigation on simultaneous machining of EDM and ECM of Ti6Al4V with different abrasive materials and particle sizes

In view of the difficult machining characteristics of titanium alloys and the limitations of sequential EDM/ECM machining, taking NaNO3 salt solution with low conductivity as the working fluid, the simultaneous machining of EDM and ECM (SEDCM) assisted by abrasive particles was carried out. Firstly, the effects of the electrical conductivity of the working fluid, the abrasive material, and particle size on material removal rate (MRR), tool wear rate (TWR), surface roughness (Ra), and surface morphology of SEDCM were investigated. Then, SiC abrasive with particle size of 50 μm was selected to add in the working fluid of SEDCM, owing to the unique properties of SiC in comparison with Al and Cu abrasive particles. The effect of process parameters, such as peak current, pulse on time, abrasive concentration, and gap voltage, was optimized using Taguchi-based grey relational analysis. The multi-objective optimization of MRR, TWR, and Ra was converted into the optimization of a single grey relational grade. Finally, based on the grey relational grade, the optimal combination of peak current, pulse on time, abrasive concentration, and gap voltage was predicted and verified by experiments. The results show that the comprehensive machining effect is better when the conductivity of working medium is 300 μS/cm, as long as other electrical parameters and experimental conditions are the same. Compared with Cu and Al abrasives, SiC abrasive has the lowest tool wear rate and the best surface quality, even with the worst material removal rate. Moreover, MRR, TWR, and Ra were obviously improved at the optimal parametric combination of peak current 1.5A, pulse on time 15 μs, abrasive concentration 5 g/L, and gap voltage 40 V by implementing Taguchi-based grey relational analysis.

Experimental Investigation and Optimization of Material Removal Rate and Tool Wear in the Machining of Aluminum-Boron Carbide (Al-B4C) Nanocomposite Using EDM Process

Electrical discharge machining (EDM) is a cost-effective unconventional machining method used for machining any composites materials. EDM is based on the thermoelectric energy between the electrode and workpiece. In this work, boron carbide particles of 50 nm (6 wt.%) are reinforced with aluminum 7075 (94 wt.%) prepared using stir casting method. The stir casting process is carried out at speed of 700–800 rev/min. The fabricated aluminum-boron carbide nanometal matrix composites are used as workpiece (anode); copper electrode is used as tool (cathode). This work investigates the influence of EDM process parameters such as current (I), pulse on-time (ton), and tool diameter (d) during machining of Al-B4C composite on metal removal rate (MRR) and tool wear rate (TWR). The design of experimental plan is executed by Taguchi approach, and the responses of each parameter are influenced by analysis of variances (ANOVA). Response table for average value of MRR and TWR shows that the current is the significant parameter affecting MRR and TWR. From this work, it was observed that material removal rate increased with increasing the current, and the tool wear rate decreases.

Parametric Optimization of Material Removal Rate of EDM (AISI-4147) by using Taguchi Approach

Electric discharge machine is a non-conventional machining. In which there is no contact between tool and work piece. By EDM process machining of hard material components that are difficult to machine such as heat treated tool steels, ceramics, composites, carbides, heat resistance steel which are used in die and mould producing industries, aero space and nuclear industries. EDM is a non-contact type technique of material removal and in this spark is used to remove the material from the work piece. Since the EDM is an attractive means of making tool and dies with high accuracy in dimension and surface finish quality of micro scale parts. The present experimental research studies the process parameters that are influencing the machining concert and its productivity. A collective approach is used for the optimization in parameters and performance characteristics which are based on Taguchi method. The experiments are based on Taguchi‟s L9 orthogonal array. The reaction graph and reaction table of each near of machining constraints are took from Taguchi method to select the optimum levels of machining factors. In the work, the machining parameters for thesis are current, pulse on time and pulse off time, which are optimized for minimum tool wear rate (TWR), maximum material removal rate (MRR) and minimum surface roughness during electro discharge machining of AISI 4147. Analysis of Variance is also used to find out variable affecting the various responses mentioned above.

Multi-Objective Optimization of Material Removal Rate and Tool Wear in Rough Honing Processes

This study focuses on obtaining regression models for material removal rate and tool wear in rough honing processes. For this purpose, experimental tests were carried out according to a central composite design of experiments. Five different parameters were varied: grain size or particle size of abrasive, density of abrasive or abrasive concentration, pressure of the stones against the cylinder internal surface, tangential speed (in this case, corresponding to the rotation speed of the cylinder), and linear speed of the honing head. In addition, multi-objective optimization was carried out with the aim of maximizing the material removal rate and minimizing tool wear. The results show that, within the range studied, the material removal rate depends mainly on tangential speed, followed by grain size and pressure. Tool wear is directly influenced by density of abrasive, followed by pressure, tangential speed, and grain size. According to the multi-objective optimization, if the two responses are given the same importance, it is recommended that high grain size, high density, high tangential speed, and low pressure be selected. Linear speed has less influence on both responses studied. If the material removal rate is considered to be more preponderant than tool wear, then the same values should be considered, except for high pressure. If tool wear is preponderant, then lower grain size of 128 (ISO 6106) should be selected, and lower tangential speed of approximately 166 min−1. The other variables, density and pressure, would not change significantly from the first situation.

Optimization of material removal rate of Ti-6Al-4V using Rao-1 algorithm

Titanium alloy Ti6Al4V has attractive properties for engineering applications. Electric discharge machining is nowadays widely adapted for titanium alloy machining to produce proper geometric profiles and surface integrity. EDM has a high machining cost and has a lower material removal rate (MRR). It is always desirable to improve the machining efficiency. This research involves, enhancing the MRR of Titanium alloy during EDM. The influence of four EDM input parameters on MRR is investigated in this research. Current, pulse on time, voltage and duty factor is selected as EDM control parameters. Response surface methodology (RSM) is utilized to create a design of experiment (DOE) plan. Thirty experiments were conducted as per the DOE. The experimental results are used to develop a predictable quadratic regression model for MRR using RSM. This MRR regression model is optimized using an advanced optimization technique, the Rao-1 algorithm. This is a new algorithm and easier to implement. The study reveals current and voltage are identified as the most influential factors in the MRR.

Optimization of Material Removal Rate, Surface Roughness and Kerf Width in Wire-ED Machining of Ti-6Al-4V Using RSM and Grey Relation

Wire-Electric Discharge (WED) Machining is one of the most suitable machining techniques for machining hard-to-cut materials such as Titanium, with precision. It is of utmost importance to optimize the control parameters to achieve the desired levels of machining performance characteristics. Considering this goal, this research investigates the effects of current, pulse on time (Ton) and pulse off time (Toff) on the material removal rate, surface roughness and kerf width of WED machined Ti-6Al-4V. The results of optimization showed that, current – 5.19 A, Ton – 20 µs, Toff – 30 µs, is the optimized settings for machining of Ti-6Al-4V alloy using molybdenum electrode for the best machining performance. Based on the analysis of grey relational grades, the order of influence of the control parameter is ranked as: Ton – I, Toff – II and Current – III. The efficacy of GRA based approach was evaluated through confirmation experiments wherein the theoretical predictions showed errors < 3%.

Optimization of material removal rate and surface characterization of wire electric discharge machined Ti-6Al-4V alloy by response surface method

Wire electric discharge machining (WEDM) is one of the foremost methods which has been utilized for machining hard-to-cut materials like Titanium alloys. However, there is a need to optimize their important operating parameters to achieve maximum material removal rate (MRR). The present paper investigates the effect of control factors like current, pulse on time (Ton), pulse off time (Toff) on MRR of machining of Ti-6Al-4V alloy. The study showed that, increase in current from 2 A to 6 A results in a significant increase in MRR by 93.27% and increase in Ton from 20 μs to 35 μs improved the MRR by 7.98%, beyond which there was no improvement of MRR. The increase in Toff showed a counterproductive effect. Increase in Toff from 10 μs to 30 μs showed an almost linear decrease in MRR by 52.77%. Morphological study of the machined surface showed that cut surface consists of recast layer on which microcracks were present, and revealed the presence of globules, ridge-structured formations of recast layers and voids. In addition, a regression model was developed to predict the MRR with respect to the control factors, which showed a good prediction with an R2 value of 99.67%.

Optimization of material removal rate of tempered glass by using electrochemical spark machining

Electrochemical Spark Machining (ECSM) is a hybridization process of (EDM) and (ECM) in which material removal is initiated by the impact of electrolysis and electroplating phenomenon based on faraday’s law. In this investigation, the development of electrochemical spark machining is conducted which is the conversion from the conventional drilling machine. The L9 orthogonal array was employed in this study to optimize the regulated parameters using a gravity feed method with the tool & electrolyte in a static condition. MRR has undergone the analysis of variance, which resulted that voltage is the most significant parameter that has higher effectiveness on material removal from the substrates, followed by power cycle and concentration. The optimized value was verified by performing the confirmation experiment. The optimal predicted value of MRR in ECSM is 0.23366 mm3/min at the control factors values of voltage 80 V, electrolyte concentration 30 % & power cycle 80 %.

Experimental investigations and optimization of MWCNTs-mixed WEDM process parameters of nitinol shape memory alloy

Excellent characteristics of multi-walled carbon nanotubes (MWCNTs), such as higher toughness and stiffness, enlarged strength, and high thermal conductivity, make them an attractive choice to improve surface characteristics and machining performance. In the current study, MWCNTs mixed with dielectric fluid in the wire electrical discharge machining (WEDM) process was used to enhance the machining performance of Nitinol shape memory alloy (SMA). Significance of WEDM machining variables such as current, pulse-on time (Ton), pulse-off time (Toff), and variation in powder concentration of MWCNTs are studied on material removal rate (MRR) and surface roughness (SR). The addition of MWCNTs substantially improves the machining performance by increasing MRR and simultaneously reducing the SR. Improvement in the MRR of 75.42% and SR of 19.15% is achieved with the use of MWCNTs at 1 g/L in comparison to the conventional WEDM process. An advanced parameterless TLBO algorithm is used for simultaneous optimization of multiple responses. An advanced parameterless TLBO algorithm is used to find the optimal solution of multiple responses. Single objective optimization result has yielded maximum MRR of 0.5262 g/min at a current of 5 A, Ton 110 μs, Toff 1 μs and MWCNTs amount of 1 g/L while minimum SR of 1.27 μm at a current of 1 A, Ton 1 μs, Toff 24 μs and MWCNTs amount of 1 g/L. MOTLBO algorithm is used for simultaneous optimization of MRR and SR. Lastly, the surface integrity of machined surfaces using a field emission scanning electron microscope (FESEM) is also studied to evaluate the effect of MWCNTs on recast layer thickness (RLT) and other surface defects. The incorporation of MWCNTs has shown a substantial reduction in RLT and other surface defects such as reduction in globules of debris, melted material deposition, micro-crack-free, and micro-pores-free surfaces.

Machining of Ti-6Al-4V biomedical alloy by WEDM: investigation and optimization of MRR and Rz using grey-harmony search

PurposeThe purpose of this paper is to investigate the optimized setting of wire-cut electrical discharge machining (WEDM) parameters at which material removal rate (MRR) and mean roughness depth (Rz) set a compromise. The problem in the processing of Ti-6Al-4V by conventional processes is a high strength, high hardness, high tool wear. Due to which WEDM is adopted to machine Ti-6Al-4V biomedical alloy. Ti-6Al-4V alloy has a number of applications in the engineering and medical industries due to its high strength biocompatibility.Design/methodology/approachThe effect of control factors (i.e. pulse on-time: Pon; pulse off-time: Poff; servo voltage: SV) on the MRR and Rz is investigated in the present research. The planning of experiments is done using a Taguchi-based L9 orthogonal array. The percentage influence of each factor on responses is also evaluated. The multi-objective optimization is done using the grey approach initially. After that, the results were also calculated using harmony search (HS). Therefore, a hybrid approach of grey and HS is used to find the optimized values of MRR and Rz.FindingsThe maximum value of grade calculated by grey-HS is 0.7879, while in the case of the experimental run the maximum value of grey grade is 0.7239. The optimized setting after improvisation at this grade value is Pon: 130 µs; Poff: 45 µs and SV: 70 V for MRR and Rz collectively. The validation of the suggested setting is completed by experimentation. The values of MRR and Rz are coming out to be 6.4 mm3/min and 13.84 µm, which represents improvised results after the implementation of the HS algorithm.Originality/valueThe integration of the grey approach with the HS principle in the manufacturing domain is yet to be explored. Therefore, in the present research hybrid approach of grey-HS is implemented in the manufacturing domain having applications in medical industries.

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 &amp; 0.0074 g/min, 0.0177 &amp; 0.0033 g/min, and 0.032 &amp; 0.01 radians respectively. MRR, TWR, and DoT improved by 49.1, 4.5, and 43.3 %, respectively.

Parametric optimization of MRR & TWR of the Al6061/SiC MMCs processed during die-sinking EDM using different electrodes

Aluminium metal matrix composites (AMMC) have become increasingly prevalent because of their lighter mass in the aerospace and automobile businesses. Machining of such composites was challenging because of the reinforcement influence. Compared to other unconventional machining methods, AMMC machining by EDM provides better machining attributes. The workpiece's electrical conduction affects machinability via EDM. A comparative analysis on fabrication and EDM of Al6061/SiC composite using different sorts of electrodes, i.e., copper and brass was carried out during the investigation. A L27 orthogonal array was employed to generate mathematical configurations. The consequences on the material removal rate (MRR) and tool wear rate (TWR) on the individual process parameters, i.e., gap voltage (V), pulse off time (Toff), current (I) and pulse on time (Ton) have been studied. Studies indicated that brass electrode attained a relatively high material removal rate opposed to copper electrode. Although copper electrode achieved lower value than brass electrode for tool wear rate.