Multi-objective Optimization Of Machining Parameters Research Articles

Multi-objective optimization of machining parameters of mild steel AISI 1018 under compressed air-assisted cooling by using genetic algorithm

Multi-objective optimization of machining parameters of mild steel AISI 1018 under compressed air-assisted cooling by using genetic algorithm

MULTI-OBJECTIVE OPTIMIZATION OF MACHINING PARAMETERS FOR EDM OF MAGNESIUM ALLOY- ZE41 USING MARCOS ALGORITHM AND DIFFERENT WEIGHING METHODS: SD, CRITIC AND MEREC

This paper presents multi-objective optimization of machining parameters, i.e., electrode materials, peak current, pulse on time and pulse off time in EDM of magnesium alloy-ZE41. The objectives such as metal removal rate, tool wear rate, surface roughness, recast layer thickness and radial overcut were optimized using MARCOS algorithm and three objective weighing methods, i.e., SD, CRITIC and MEREC. Taguchi’s L27(34) orthogonal array was used for conduction of experiments. ANOVA results showed that electrode material, interaction of electrode material and peak current and pulse on time were the most significant parameters for all weighing methods. Comparative study indicated that the percentage improvement in performance at optimal parameter setting were 37.21%, 32.30% and 10.68% for SD, CRITIC and MEREC respectively. Confirmation results indicated that all objectives except tool wear rate were improved positively for SD and CRITIC, but all objectives except metal removal rate deteriorated for MEREC.

Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing

Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking machining energy, machining time and machining cost as optimization objectives. Next, the IHNN for multi-objective optimization of STEP-NC machining parameters combining with Pareto theory, improved immune algorithm and non-monotone activation function is designed. Based on it, the optimal Pareto solutions of STEP-NC machining parameters are obtained, which intelligently realizes the multi-objective optimization of STEP-NC machining parameters and provides a decision support for the decision-maker. Finally, performance comparisons among the IHNN, classic non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are done by classic test functions with three objectives and multiple constraints which correspond to the mathematical model established in this paper, and its effectiveness and feasibility is verified by case study.

Multi-objective optimization of machining parameters in complete peripheral milling process with variable curvature workpieces

The mechanism of peripheral milling with variable curvature has yet to be revealed. There is an urgent need to develop a composite optimization approach for the full milling process, including rough and fine machining. The influence of curvature on the energy consumption mechanism in peripheral milling is investigated in this paper. Firstly, the geometric parameters of peripheral milling are analyzed, and the material removal rate calculation method with variable curvature is proposed. The effects of workpiece curvature, machining parameters, and tool wear on energy consumption and surface quality are revealed. Secondly, prediction models for machine tool specific energy and surface roughness are developed in accordance with the examined material removal mechanism. Then, the carbon emission calculation method for the complete peripheral milling process is put forward based on the developed energy consumption mechanism. Finally, a genetic algorithm with elite strategy is used to optimize the multi-dimensional and multi-objective machining parameters. The objective is to achieve the maximum machining efficiency, minimize carbon emissions, and attain excellent surface quality during the complete milling process. The research aims to concurrently optimize machining parameters for rough and fine machining processes, thereby enabling effective and environmentally friendly peripheral milling while maintaining machining quality. The determination coefficient R2 for the proposed model exceeds 0.97, while the average accuracy of predictions surpasses 96 %. The research holds particular importance in providing guidance for peripheral milling operations involving workpieces with variable curvature.

A novel MOALO-MODA ensemble approach for multi-objective optimization of machining parameters for metal matrix composites

Machining of metal matrix composites (MMCs) has now become an essential task in shaping them to their final usable forms for various industrial applications. These machining operations may range from drilling of simple through holes to cutting and shaping of the composites into complex shapes. Determination of the optimal process parameters during their machining presents a combinatorial optimization problem, which may turn out to be more complex due to involvement of various material dependent parameters of the MMCs, like particle size, reinforcement percentage etc. The importance of optimization increases manifold due to conflicting settings of different process parameters for attaining the desired quality characteristics. In this paper, two nature-inspired optimization algorithms, i.e. multi-objective antlion optimization (MOALO) and multi-objective dragonfly algorithm (MODA) are applied for optimization of various process parameters during machining of MMCs. To mitigate uncertainties in global optimality of the predicted Pareto optimal fronts arising due to stochastic nature of the metaheuristics, an ensemble approach integrating MOALO and MODA techniques is proposed here. It is demonstrated with the help of two case studies that MOALO-MODA ensemble is far superior to its individual counterparts and thus, can be employed for development of robust and reliable Pareto optimal fronts.

Evaluation and Improvement of Greenness for Milling AL6061 Alloy through Life Cycle Assessment and Grey Relational Analysis.

Modern manufacturing industries thrive on greenness, which means ensuring acceptable environmental impacts and required surface quality of the products during the manufacturing process. However, there is a conflict between surface quality and environmental performances in the milling process. The current research only considers energy consumption rather than total environmental impacts. In this respect, this research focuses on the multiobjective optimization of machining parameters for balancing the surface quality (i.e., surface roughness, Ra) and total environmental impact (TEI), which includes raw materials usage, energy consumption, and output pollutant emission during the milling of AL6061 alloy. First, life cycle assessment (LCA) of the milling process is used for evaluating the TEI. Then, multiobjective optimization is conducted using Grey Relational Analysis. The results indicated that the improvement of Ra and TEI can be achieved with higher cutting speed, higher depth, and wet conditions in milling. The optimization work showed that cutting speed of 165 m/min, feed rate of 0.28 mm/rev, depth of cut of 2 mm, and width of cut of 3 mm are the optimal combination among existing experiments. Compared to single objective optimization results, multiple responses (Ra and TEI) can be improved simultaneously.

Multi-objective optimization of machining parameters in hybrid powder-mixed EDM process by response surface methodology and normalized fuzzy logic algorithm

Multi-objective optimization of machining parameters in hybrid powder-mixed EDM process by response surface methodology and normalized fuzzy logic algorithm

Multi-objective optimization of machining parameters during green machining of aerospace grade titanium alloy using Grey–Taguchi approach

This paper presents multi-objective optimization of aerospace-grade titanium alloy using the Gray–Taguchi approach. Experimentation has been conducted with varying cutting conditions according to Taguchi's L9 orthogonal array. Tool nose wear, tool flank wear, surface roughness and material removal rate are employed as process characteristics. The experimental outcomes are statistically analyzed using the Grey–Taguchi approach. Statistical and sensitivity analysis are also performed. Studies revealed that cutting speed and feed rate are having a significant influence on process performances. Micro analysis of cutting tool is also performed to study the kind of wear. Furthermore, chips were also analysed using scanning electron microscope to study its features and morphology to obtain a better insight into the process.

A hybrid approach for prediction of machining performances of glass fiber reinforced plastic (Epoxy) composites

In recent years, Glass Fiber Reinforced Plastic (GFRP) composites are more potential materials for usage in various structural applications in aerospace and automobile industries, owing to its favorable properties, such as light weight, specific strength, high elastic modulus and excellent corrosion resistance. This paper presents a new hybrid approach for multi-objective optimization of machining parameters on GFRP composite using Taguchi coupled grey relational (GR) and desirability function (DF) approach. The end milling of GFRP composite was performed as per Taguchi’s L16 orthogonal array by considering three input parameters: viz cutting speed (V), feed rate (F) and depth of cut (D), each at four levels. The material removal rate (MRR), surface roughness (SR) and tool wear (TW) were chosen as output responses. The main effect plot was used to determine the optimum level of machining parameters on the multiple responses. An experimental result revealed that, the optimum level of parameters obtained by GR analysis is V2F3D4. Similarly, the optimum level of parameters obtained by DF approach is V2F4D4. Moreover, analysis of variance (ANOVA) was applied to examine the significance of the parameters. Based on the ANOVA results, it has been proved that the depth of cut was the most significant parameter, trailed by feed rate and cutting speed, respectively. It was also confirmed that the P-values of depth of cut for both analyses are 0.017 and 0.039, which is less than 0.05. Finally, the confirmation tests exhibited that the DA approach was employed better in terms of determining the optimum level of machining parameters as compared to GR analysis.

Effect of ultrasonic impact treatment on the surface integrity of nickel alloy 718

Ultrasonic impact treatment (UIT) is a type of surface strengthening technology that can improve the fatigue properties of materials by improving the surface quality, residual stress, and other aspects. In this study, the influence of ultrasonic impact parameters on the surface integrity of nickel alloy 718 was studied. The micro stress concentration caused by the surface morphology was also explored. The cosine and exponential decay functions were used to fit and characterize the distribution of residual stress and work hardening in the surface material. The results showed that the feed rate had the greatest influence on surface roughness, stress concentration, and surface residual stress. It was not appropriate to evaluate the surface hardening effect only by the number of impacts per unit area, the ultrasonic impact parameters such as feed speed and pre extrusion depth should also be considered. The grain refinement was obvious after UIT. The multi-objective optimization of machining parameters was performed with the objective of surface stress concentration and residual stress. A surface with a smaller surface stress concentration factor and larger compressive residual stress can be obtained simultaneously using medium linear velocity, medium pre extrusion depth, and smaller feed rate.

Multi-objective optimization of machining parameters during milling of carbon-fiber-reinforced polyetheretherketone composites using grey relational analysis

Short carbon-fiber-reinforced composites, especially short carbon-fiber-reinforced polyetheretherketone composites (CF-PEEK), are used extensively in the engineering field because of their superior properties. However, their surface quality and material removal rate need to be optimized to satisfy design and processing requirements. This work focused on a multi-objective optimization to minimize the surface roughness and maximize the material removal rate during machining by grey relational analysis with an analysis-of-variance (ANOVA) and response surface methodology before a multi-objective mathematical model was established. The statistical significance of the predicted model was examined by using an ANOVA to obtain the optimal machining parameters (spindle speed, feed rate, cut depth). The optimal combination of cutting parameters was a spindle speed of 2600 rpm, a feed rate of 720 mm/min, and a cut depth of 1.8 mm.

Multi-objective optimization of high-pressure jet-assisted turning of Inconel 718

Proper selection of cooling/lubricating technique is very important in the machining of difficult-to-cut materials such as nickel-based alloys due to their poor machinability. Among the available cooling/lubricating techniques, high-pressure jet-assisted machining offer remarkable opportunities in terms of increasing the productivity, reducing temperature in the cutting zone, excellent chip-breaking ability, and reduction of the costs associated with cooling/lubrication fluids. This article presents experimental investigation and multi-objective optimization of machining parameters of the high-pressure jet-assisted turning of Inconel 718 with coated carbide tools. The Taguchi L27 orthogonal array was used for the experimental design. Diameter of the nozzle, distance between the impact point of the jet and the cutting edge, pressure of the jet, cutting speed, and feed were considered as machining parameters. In order to optimize the six performance characteristics, namely cutting power, hydraulic power, material removal rate, surface roughness, cutting tool temperature, and chip length, Taguchi-based grey relational analysis and genetic algorithms were applied as multi-objective optimization approaches to identify the optimal levels of machining parameters. Moreover, the weights of the responses were determined by employing the analytic hierarchy process. Finally, a confirmation experiment with the machining parameters in their optimal levels was conducted with the aim to demonstrate the effectiveness of proposed multi-objective optimization approaches.

A novel multi-objective optimization approach of machining parameters with small sample problem in gear hobbing

Low carbon gear hobbing is an environmentally friendly way to machine massive workpieces. The appropriate process parameter decision-making is of great significance to improve processing quality, reduce the machining time, production cost, and carbon emission in gear manufacturing. This paper first proposes a support vector machine/ant lion optimizer/gear hobbing (SVM/ALO/GH) integrated approach to do the multi-objective optimization of machining parameters for solving small sample problem of batch production. The first population of process parameters is generated by the multi-class SVM method. Pareto improvement and ALO algorithm are employed to obtain the optimal process parameters. Finally, the case study is presented to give a clear picture of the application of the optimization approach. The results uncover that the proposed SVM/ALO/GH method has better performance than the improved back propagation neural network/differential evolution (IBPNN/DE) algorithm over the small sample problem.

Multi-objective optimization of machining parameters in multi-pass turning operations for low-carbon manufacturing

Most recent studies on machining parameter optimization in machining operations focused on reducing machining cost and energy consumption. However, environmental impacts caused by manufacturing activities were not involved in those studies, which can be quantified by equivalent carbon dioxide emissions. In this study, a direct method was proposed to quantify the carbon emissions generated during multi-pass turning operations. Moreover, machining parameter optimization models of multi-pass turning operations in dry and wet cut environments were established using an experimental design method. Three objectives were considered in both models: carbon emissions, operation time, and machining cost. Furthermore, a multi-objective teaching–learning-based optimization algorithm was used to deal with the models. The optimization results indicated that the use of cutting fluids could significantly reduce carbon emissions and machining cost and improve production efficiency in multi-pass turning operations.

Effect of process parameters on the electrical discharge machining of aluminum metal matrix composites through a response surface methodology approach

AbstractThis work presents the multiobjective optimization of machining parameters during the electrical discharge machining (EDM) of aluminum (Al)-silicon carbide (SiC) metal matrix composites (MMC). The process parameters considered were current, pulse on-time, dielectric flushing pressure, and SiC particles. A copper rod was used as an electrode. An Al-SiC MMC with Al 6061 as matrix and SiC particles having three different sizes (i.e., 15, 25, and 40 μm) were used as workpieces. The experiments were planned using design of experiments through response surface methodology (RSM). The mathematical models were developed to predict the better performance measures such as the material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR), and cylindricity (CY). The desirability approach in RSM was performed for optimization. It was found that the MRR increases with increasing peak current, pulse on-time, flushing pressure, and particle size. The EDM parameters are to be analyzed for the MRR, EWR, SR, and CY. The best one is proposed for validation.

A Simple GA Based Approach for Multi-Objective Optimization of Machining Parameters

A simple approach to multi-objective optimization of machining parameters is presented. Regression analysis of experimental data is carried out to obtain the correlation between cutting parameters and response variables. Finally, Genetic Algorithm (GA) toolbox of MATLAB is used to carry out multi-objective optimization of two objective functions (surface roughness “Ra” & material removal rate “MRR”). Genetic algorithm is found to be a powerful tool for multi-objective optimization of machining parameters in this study.

Multi-objective optimization of machining parameters considering energy consumption

This paper deals with multi-objective optimization of machining parameters for energy saving. Three objectives including energy, cost, and quality are considered in the optimization model, which are affected by three variables, namely cutting depth, feed rate, and cutting speed. In the model, energy consumption of machining process consists of direct energy (including startup energy, cutting energy, and tool change energy) and embodied energy (including cutting tool energy and cutting fluid energy); machining cost contains production operation cost, cutting tool cost, and cutting fluid cost; and machining quality is represented by surface roughness. With simulation in Matlab R2011b, the multi-objective optimization problem is solved by NSGA-II algorithm. The simulation results indicate that cutting parameters optimization is beneficial for energy saving during machining, although more cost may be paid; additionally, optimization effect on the surface roughness objective is limited. Inspired by the second result, optimization model eliminating quality objective is studied further. Comparing the non-dominated front of three-objective optimization with the one of two-objective optimization, the latter is proved to have better convergence feature. The optimization model is valuable in energy quota determination of workpiece and product.