Optimization Of Machining Process Parameters Research Articles

Geometrical Properties and Machining Process Parameters Optimization on EN 24 by Using VMC

Geometrical Properties and Machining Process Parameters Optimization on EN 24 by Using VMC

An optimized milling force prediction method for multi-axis ball-end finish milling

By accurately predicting milling force in finish milling, simulation of the milling process can be effectively conducted, followed by the provision of effective recommendations on optimization of machining process parameters. The prediction of ball-end milling force poses a significant challenge due to the influence of the cutter workpiece engagement (CWE) calculation and cutting force coefficient identification. This paper proposes a precise approach for predicting milling force, which involves an improved calculation method of CWE and an optimized identification approach for cutting force coefficients based on a mechanistic model. In the calculation of CWE, an optimization method considering the dynamic change of undeformed slice thickness and CWE boundary calculation is proposed. Furthermore, an improved approach incorporating interference signal removal and multiple nonlinear regression fitting methods is utilized for cutting force coefficient identification. Subsequently, the resulting prediction is validated through comparison with measurements and demonstrates a prediction accuracy exceeding 94%. The conclusion drawn is that the proposed prediction method for multi-axis ball-end finish milling force exhibits high accuracy in its predictions.

Višekriterijumska optimizacija zasnovana na verovatnoći kao osnova za primenu novog robustnog dizajna na parametre mašinske obrade

Introduction/purpose: New robust design by means of probability-based multi-objective optimization takes the arithmetic mean value of the performance indicator and its deviation as twin independent responses of the performance indicator. The aim of this article is to check the applicability of new robust design in optimizing machining process parameters. To conduct the examination in detail, the robust design for optimal cutting parameters to minimize energy consumption during the turning of AISI 1018 steel at a constant material removal rate is applied as well as the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Methods: In the spirit of the probability-based method for multi-objective optimization, the arithmetic mean value of the performance indicator and its deviation are taken as two independent responses of the performance indicator to implement robust design. Each of the above twin responses contributes one part of the partial preferable probabilities to the performance indicator of the alternatives in the treatment. The arithmetic mean value of the performance indicator should be assessed as a representative of the performance indicator according to the function or the preference of the performance indicator, and the deviation is the other index of the performance indicator, which has the characteristic of the 99 smaller-the-better in general. Furthermore, the square root of the product of the above two parts of the partial preferable probability forms the actual preferable probability of the performance indicator. Moreover, the product of partial preferable probabilities gives the total preferable probability of each alternative, which is the overall and unique index of each alternative in the robust optimum. Results: The paper gives the rational optimum cutting parameters for minimizing energy consumption during the turning of AISI 1018 steel at a constant material removal rate and the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Conclusion: The application study indicates its rationality and convenience of new robust optimization in the optimization of machining process parameters.

Optimization of machining process parameters of high pressure die casting for improve the quality of manufactured product

Inthiswork, pressure die casting (PDC) process parameterswith aluminium die casting alloy ADC12 hasbeenevaluated for a realistic industrial manufacturing line product. This study shows the observations on the basis of Taguchi based model onroughness value (Ra) and porosity (Φ)of final finished product in PDC process. A set of proposed setting is suggested for the manufacturing of final product. Several parameters have been studied for roughness value (Ra) and porosity(Φ)of the final product. The result shows the significantimprovementinroughness found minimum i.e. for the S/N ratio of −9.21796 with respect to metal temperature at 620 °C; with a Ist phase pressure at 5 N/mm2; IInd Phase Pressure at 30 N/mm2 and Die open time at 7 s and maximum at S/N ration of −3.04577 with respect to metal temperature at 580 °C; Ist Phase Pressure at 5 N/mm2; IInd Phase Pressure at 25 N/mm2 and Die open time at 6 s similarly porosity found minimum i.e. S/N ration of 2.00741 with respect to metal temperature at 620 °C; Ist Phase Pressure at 5 N/mm2; IInd Phase Pressure at 30 N/mm2 and Die open time at 7 s and maximum at S/N ration of 8.635966 with respect to metal temperature at 620 °C; Ist Phase Pressure at 15 N/mm2; IInd Phase Pressure at 25 N/mm2 and Die open time at 7 sat parametric settingsofPDC process.

Optimization of process parameters in plasma arc cutting for TWIP steel plates

The purpose of this research paper is to understand, analyze and optimize the process parameters involved in the water–acetone plasma arc cutting for Twinning Induced plasticity (TWIP) steel plates. TWIP steel plates emphasized now a days more vividly because of their characteristic applications in the aerospace and submarine fields. Cutting is one of the basic machining operation that is usually performed for many applications. In the industries for production purposes the accuracy plays a vital role, in order to archive the accurate products the machining process parameters optimization is to be performed. In the present research the water–acetone plasma arc cutting the cutting of TWIP steel plates is done to understand the parameters involved in it and further to optimize those process parameters. The work is initialized with the design of experiments (DOE), after formulating the DOE with necessary an sufficient conditions the formulation of Taguchi’s L9 orthogonal array is formed based on the listed factors and their operable conditions. Then the analysis of variance (ANOVA) is made for the assessment of key parameters. At the end the response surface methodology (RSM) is adopted for the optimization of process parameters. The input process parameters considered are voltage in volts with values of 240 V, 270 V and 290 V, current in Amperes with values of 65A, 70A and 75A, Speed of cut in mm/sec with values of 0.5 mm/s, 1 mm/s and 1.5 mm/s. Using the RSM the optimal conditions obtained as voltage of 240 V, current of 65 amps and speed of cut as 1.5 mm/s. By adopting these input process parameters values the output values obtained as material removal rate of 5.79 mm3/s, surface roughness as 65 µm and time of cut as 246 s.

Effect of Drilling Process Parameters on Surface Roughness of LM6/B4C Composites

Metal matrix composites are a new course of materials with superior properties to those of the components. Such materials ' machining is distinct from that of traditional materials. So the optimization of machining process parameters becomes inevitable. By applying Taguchi's Signal-to-Noise ratio method, this paper examines the effects of drilling process parameter such as feed, spindle speed, drill material and percentage reinforcement on the drilled hole’s surface roughness. Variance analysis was used to evaluate each system parameter's contribution to surface roughness. The composites were manufactured by stir casting technique using aluminium alloy (LM6) as matrix material and boron carbide particulates at 3%, 6% and 9% by weight as material for the reinforcement. There are four factors investigated each at three levels, so 34 which implies 81 experiments has to be conducted, but by using Design of Experiments approach 27 experiments were conducted using L27 orthogonal array The minimum surface roughness measured for the hole was 1.08 μm at combination of 3000 rpm spindle speed, 50 mm/min feed rate, 3% reinforcement and Carbide drill.

Cutting stability and optimization of process parameters for the large-pitch screw high-feed turning

In this study, the machining characteristics of the large-pitch screw in high-feed turning are initially analyzed, and a dynamic model for the screw subjected to moving forces is proposed to study the chatter, which has been taken into consideration “quasi-regeneration effect” of the machined surface and the actual machining path. Then, a stability model of the lobes between the amount of feed and spindle speed in the turning screw finishing stage is achieved, taking into consideration deformation of the workpiece and the tool, on the basis of the vibration equation of the workpiece and the dynamic equation of the tool, and is experimentally verified as well. The dynamics model, and stability domain provide a basis upon which multiobjective process parameter optimization method including the amount of feed and spindle speed can be put forward during high-feed turning process. Experiments are likewise performed and the results show that no chatter occurs, verifying the optimization of machining process parameters for large-pitch turning. Meanwhile, it should be noted that the surface quality and shape accuracy of the machined surface are equivalent to the high-feed turning process.

Optimization of machining process parameters in CNC turning process of IS2062 E250 Steel using coated carbide cutting tool

This paper proposed the methodology of optimization technique in CNC turning process in IS2062 E250 Steel done by carbide coated cutting tool. Optimization experiment was conducted by the Taguchi L9 orthogonal array for CNC turning process. Carbide cutting tool is preferred to cutting process which has the less wear occurrence and maintains good strength during machining process. In experiment input parameters are speed feed and depth of cut and output parameters surface roughness and hardness. Finally analysis of variance which is used to determine the optimum level of parameters input parameters on the effective responses.

NUM ERICAL AND EXPERIMENTAL INVESTIGATIONS OF CUTTING FORCE FOR NAB-CBN COMPOSITE

The alloy of nickel aluminum bronze (NAB) presents superior properties such as highstrength, excellent wear resistance and corrosion resistance, so it’s used in a wide range ofengineering applications. Usually, this material undergo to a series of machining processesto achieve the final shape. In this paper, NAB alloy reinforced with cubic boron nitride(CBN) particles was fabricated by powder metallurgy route. Design of experiments has beenused to study the effect of main turning parameters on the main cutting force for NAB-CBNcomposite. Numerical modelling was developed to predict the main cutting force requiredduring turning process under different machining conditions. The results of numerical modelshowed that the maximum error comparing with the experiment results is not exceed13.86%, which suggests that the developed model are reasonable. Optimization ofmachining process parameters was done using response surface method.

Optimization of machining process parameters in conventional turning operation of Al5083/B4C composite under dry condition

Experiments are performed on Al5083 metal matrix composite reinforcement of 7%B4C. The turning process is carried out by using HSS single point cutting tool under the dry condition. In the present experiment the turning parameters such as cutting speed, feed rate and depth of cut are being optimized by considering constant length of material being machined. In the metal cutting process the proper cutting parameter will affect the quality of the finished product, In Ex-situ form B4C particle used for reinforcing the material. The reinforced aluminium matrix composites have some special properties which make them use for various purposes. The presence of B4C particle in Al matrix makes them difficult in machining, for the achieving of industrial requirement. The influence of B4C particle in Al5083 was studied experimentally and optimal machining condition is studied in this experiment. In this present study, the BA, ABC and PSO algorithm is applied to determine the optimal parametric combinations and compare between them for achieving better and higher machining performance and it is observed that the among, PSO algorithm gives better results.

Multi response optimization in wire electrical discharge machining of Inconel X-750 using Taguchi’s technique and grey relational analysis

AbstractThe effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This study presents an investigation on cutting speed and surface roughness of Inconel X-750 using Wire-cut electric discharge machining process. Six input process parameters of Wire-cut Electro Discharge Machining—spark gap voltage, pulse-on time, pulse-off time, wire feed rate, peak current and wire tension—were chosen to study their effects on the selected responses. Taguchi’s design of experiments methodology has been used for planning, designing and conducting the experiments. The grey relational analysis has been applied in conjunction with the Taguchi’s parameter design approach for the optimization of multiple responses. The analysis of variance has been applied to identify the significance of different parameters. Finally the confirmation experiments were performed to validate the predicted optimized results.

Machining process parameters optimization for heavy-duty CNC machine tools in sustainable manufacturing

Machining process parameters (MPP) directly affect the machining quality and efficiency of heavy-duty CNC machine tools (HCMT). The selection of MPP is very important to effectively improve machining performance. Machining performance has been closely related to the HCMT running state. In order to maintain HCMT sustainably manufacturing with high accuracy and low consumption after machining performance degradation for a long time running, MPP should be re-optimized according to the current state of the machine tools. Thus, this paper proposed a MPP optimization method for running HCMT to obtain optimal MPP based on current running state. A multi-objective optimization model was built, considering both the linear factors such as machining time and machining cost and nonlinear factors such as chatter in machining process. The nonlinear factors were reflected by the nonlinear dynamic model of machining process. Furthermore, a grid optimization algorithm was introduced to search the optimal MPP from the multi-objective optimization model. Finally, a case study was implemented to verify the feasibility of the nonlinear dynamic model and the superiority of the multi-objective optimization method compared with single-objective optimization method.

Thin-Walled Part Machining Process Parameters Optimization based on Finite-Element Modeling of Workpiece Vibrations

Arising vibrations when milling thin-walled parts are the most essential factor that limits machining productivity and surface location accuracy. Such vibrations have large amplitude and often result in irreversible workpiece damage on finishing stage. Vibrations appear as a result of resonance of workpiece natural frequencies and cutting force spectrum harmonics from tooth- passing impacts.To eliminate resonance vibrations it is suggested to tune spindle speed in such a way, that workpiece natural frequencies differs from tooth-passing frequency harmonics. In practice changing of natural frequencies due to stock removal and changing of cutter position during machining process need to be considered.To estimate natural frequencies and mode shapes of workpiece mounted on NC machine worktable and its evolution during the process it is reasonable to apply widespread finite element modeling software. Using of FEM methods for vibration process modeling is considered. Also practical example of machining thin blade of compressor's aerodynamic model is considered.

Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm

This present study focused on the effect of machining parameters such as helix angle of cutter, spindle speed, feed rate, axial and radial depth of cut on temperature rise in end milling. A prediction model of the temperature rise was developed using response surface methodology. The experiments were conducted on Al 6063 by high-speed steel end mill cutter based on central composite rotatable designs consisting of 32 experiments. The temperature rise was measured using K-type thermocouple. The adequacy of the model was verified using analysis of variance. The given model is utilized to analyze direct and interaction effect of the machining parameters with temperature rise. The optimization of machining process parameters to obtain minimum temperature rise was done using genetic algorithms. A source code using C language was developed to do the optimization. The obtained optimal machining parameters gave a value of 0.173 °C for minimum temperature rise.

OPTIMIZATION OF CNC WEDM PROCESS PARAMETERS OF HIGH CHROMIUM HIGH CARBON (HCHC) DIE STEEL USING TAGUCHI METHODOLOGY

This paper presents an investigation on the effect and optimization of machining process parameters on the Material removal rate (MRR) and surface roughness (SR) in CNC wire electrical discharge machining (WEDM) operations. The cutting of High Chromium High Carbon (HCHC) die steel work piece using CNC WEDM by using Taguchi methodology has been reported. Many experiments were conducted to consider the effect of Current, Voltage, Wire speed and Wire tension on the MRR and SR response. An L9 (3*4) Taguchi standard orthogonal array (OA) is chosen for design of experiments and the main influencing factor are determined for each given machining criteria by using Analysis of variance (ANOVA). We found out the optimal parameter setting for MRR and SR. The analysis of the Taguchi methodology reveals that. In general the Current significantly affect the MRR and SR. Experimental observed values are provided to verify this methodology.

Machining Process Parameters Optimization Based on Grid Optimization Algorithm

Machining process parameters directly affect the machining quality and efficiency of heavy-duty CNC machine tools, selecting correctly machining process parameters can improve the machine’s machining performance effectively. This paper presents a machining process parameters optimization method based on grid optimization algorithm for heavy-duty CNC machine tools. In this method, a multi-objective optimization model will be established, which considers not only the linear constrains of machining process parameters, such as machining time and machining cost, but also the non-linear constrain, such as chatter in machining process. Grid optimization algorithm will be adapted to search the optimal combination of machining process parameters from the multi-objective optimization model. In the end, this paper will present an example to verify superiority of the multi-objective optimization method by comparing with single-objective optimization method.

Overview of NSGA-II for Optimizing Machining Process Parameters

This paper presents an overview on NSGA-II optimization techniques of machining process parameters. There are many multi objective optimization (MoGA) techniques involved in machining process parameters optimization including multi-objective genetic algorithm (MOGA), strength Pareto evolutionary algorithm (SPEA), micro genetic algorithm (Micro-GA), Pareto-archived evolution strategy (PAES), etc. This paper reviews the application of non dominated sorting genetic algorithm II (NSGA-II), classified as one of MoGA techniques, for optimizing process parameters in various machining operations. NSGA-II is a well known, fast sorting and elite multi objective genetic algorithm. Process parameters such as cutting speed, feed rate, rotational speed etc. are the considerable conditions in order to optimize the machining operations in minimizing or maximizing the machining performances. Unlike the single objective optimization technique, NSGA-II simultaneously optimizes each objective without being dominated by any other solution.

Concurrent optimization of machining process parameters and tolerance allocation

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.

Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms

The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques.