Optimal Setting Of Machining Parameters Research Articles

An ensemble approach to maximize metal removal rate for chatter free milling

In the present work, a new ensemble approach is adopted to obtain an optimal set of machining parameters resulting in chatter free milling at higher Metal Removal Rate (MRR). In order to achieve this, a modified LMD based on cubic spline interpolation is used to analyze the recorded audio signals during machining. Further, these decomposed chatter signals are explored to ascertain chatter severity in terms of a new dimensionless chatter indicator (CI). Total 27 experiment based on full factorial design are performed considering 3 levels of each machining parameters viz. axial depth of cut (D), feed rate (F) and spindle speed (N). CI and MRR are calculated for these 27 tests. Further, regression models of CI and MRR are developed using RSM (Response Surface Methodology) approach. Finally, multi-objective particle swarm optimization (MOPSO) is invoked to optimize these regression models for obtaining best possible range of input parameters for minimal chatter and maximum MRR. Moreover, to validate the presented methodology, more tests are performed considering the ascertained optimal set of parameters. Verification results proved that the developed optimal solution is feasible and can be used for higher productivity with chatter free milling.

Experimental study of EDM milling of 3D-shaped diffuser for film cooling holes on turbine blades

Cylindrical and diffuser-shaped film cooling holes on aero-engine turbine blades are usually machined by electrical discharge machining (EDM) by means of drilling and die-sinking approaches in a sequence. However, this sequential machining on different types of EDM machines is prone to deteriorate the machining accuracy since the inevitable secondary clamping error. Besides, since the nature of electrode wear, the shape of the tip of tool electrode is constantly changing during machining. To ensure the geometry accuracy of the diffuser, the worn tool electrode has to be replaced or dressed online frequently, resulting in a low machining efficiency. To address these problems, this paper proposes a machining method for diffuser shaped film cooling holes by a compound approach in combination of high speed EDM drilling and milling implemented on an EDM drilling machine with tubular electrodes and high-pressure inner flush. To obtain the optimal machining parameters, experimental investigation is carried out based on the experimental design method. Machining performance in terms of longitudinal tool electrode wear ratio, machining time, and machining stability under different machining parameters, such as pulse duration, pulse interval, peak current, capacitance, spindle speed, and flushing pressure, were taken into account. And the range analysis method is then adopted to obtain an optimal set of machining parameters. By applying the obtained optimal machining parameters, the proper compensation parameter and tool path overlapping ratio of the EDM milling process can be obtained. In the milling of an inversed pyramid, the material removal rate can reach as high as 14.02 mm3/min, the relative geometrical error can be controlled within 3.58%, and the maximum recast layer thickness was 6.8μm. Machining experiments of diffuser shaped film cooling holes verify the capability and effectiveness of the proposed method.

Optimization of Machining parameters in wet and cryogenic Machining using TOPSIS approach

Machining processes are a prevalent part of any manufacturing industry. However, despite its popularity and successful implementation in multiple applications, certain machining processes, like the cutting operation, suffer from increase in temperature of the working part and tool during the operation. The detrimental effects of increased temperature are currently rectified using non-ecofriendly wet coolants made of oil and water. In an attempt to provide a more environmentally friendly solution, this paper focuses on studying the use of cryogenic cooling for machining and the optimal set of machining parameters to be set for the implementation of the same have been studied using TOPSIS approach.

Optimization of machining parameters when machining beyond recommended cutting speed

Purpose Even though austenitic stainless steels have been extensively used in industries, owing to some of the characteristics of the material, its performance in machining is difficult to understand, in particular at high cutting speeds. There is no availability of dependable and in-depth studies pertinent to this matter. In this work, performance of AISI 304 austenitic stainless steel was studied in terms of surface roughness (Ra) and material removal rate (MRR) at high cutting speeds. Subsequently, parametric optimization and prediction for responses were carried out. Design/methodology/approach Turning operations were conducted using L9 orthogonal array and the outcomes were analyzed to attain optimal set of machining parameters for the responses using signal-to-noise (S/N) ratio and Pareto analysis of variance (ANOVA). In the present work, the cutting speed values were considered beyond the recommended range as designated by tool manufacturers. Finally, multiple regression models were developed to predict responses. Findings From the results, 350 m/min was found to be a significant speed. The investigation reveals that even though the speeds are taken beyond the recommended values, the results are favorable. The optimal machining parameters values for surface quality obtained were cutting speed of 350 m/min, feed of 0.15 mm/rev and depth of cut of 2.0 mm. In case of MRR, the optimal values were: cutting speed of 400 m/min, feed of 0.25 mm/rev and depth of cut of 2.0 mm. It was found out that there was an improvement in Ra and MRR (around 15 and 4%) due to optimization. The results indicate that Pareto ANOVA is easier than S/N ratio. This revealed that the feed rate and depth of cut were mostly affected parameters for Ra and MRR. The developed models are capable of predicting the responses accurately. Practical implications The outcome of the work reveals that even though the speeds were taken beyond the recommended value, the results are favorable for manufacturing industries when the tool cost is considered insignificant. Originality/value No work was reported on machining of the chosen material beyond the recommended cutting speed. Moreover, it was observed from the past works that cutting speeds were limited to 100–300 m/min.

Modeling and optimization for surface roughness and tool flank wear in hard turning of AISI 4340 steel (35 HRC) using TiSiN-TiAlN nanolaminate coated insert

PurposeThe present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.Design/methodology/approachIn this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.FindingsOptimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.Originality/valueThis work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

Parametric optimization in wire electric discharge machining of DC53 steel using gamma phase coated wire

DC53 is considered as superior steel grade by die and mold making industries as compared to D2 and D3 steel due to its excellent mechanical characteristics like high hardness, wear resistance and strength. DC53 steel finds applications in making stamping dies, punches and cutting tools. In wire electric discharge machining (WEDM) process, gamma phase coated wire offers superior machining performance over uncoated brass and zinc coated electrode wires. This work was performed to study the effect of machining variables like sparks on duration, sparks off duration, arc-on time, and servo voltage on response variables, namely kerf width (KW), cutting speed (CS), material removal rate (MRR), and surface roughness (Ra) for WEDM of DC53 steel. Taguchi L18 orthogonal array was used for experimental design. Analysis of variance showed that sparks on duration is the most significant factor affecting KW, CS, and MRR with percentage contribution of 48.25 %, 80.21 %, and 45.21 %, respectively. Optimal set of machining parameters resulted in 14.02 % improvement in MRR and 19.24 % reduction in workpiece surface roughness. Moreover, gamma phase coated wire was found to have 30–35 % faster cutting rate as compared to uncoated brass wire.

Modeling and multi-objective optimization of WEDM of spark plasma sintered boron carbide considering preferences of users

In this research work, development of a multi-response optimization technique was performed, using Genetic Algorithm (GA) optimization technique in wire electrical discharge machining (WEDM) keeping in mind about the priorities of multiple users. Boron carbide has been selected as work piece for experiment. The effect of five process parameters such as pulse on time (Ton), pulse off time (Toff), peak current (IP), water pressure (WP), servo feed (SF), were investigated on the responses such as machining speed and surface roughness (Ra) in WEDM operation. Further, two responses were modeled empirically through regression analysis. The developed models can be used by the machinists to predict the machining as well as the surface characteristics over a wide range of machining input parameters. The analysis of variance (ANOVA) is also applied to investigate the effect of influential input parameters. Finally, the confirmation experiments were conducted with optimal set of machining parameters and minor deviations have been found.

Selection of optimal processing condition during WEDM of compocasted AA6061/cenosphere AMCs based on grey-based hybrid approach

ABSTRACTInvestigation on the machining of Aluminium Matrix Composites (AMCs) through non-conventional approach are the current attention over the conventional machining approach of those may produces additional complexity in industrial manufacturing. The current research focused on investigating the machinability characteristics of the newly prepared compocasted AA6061/ cenosphere AMCs implementing a non-traditional material removal process via spark erosion called Electro Discharge Machining (Wire-EDM). The performance measure of the system for the different combinations of independent process variables were considered as cutting rate (CR), Kerf Width (KW) and Surface Roughness (SR). Experimental design were carried out based on Response Surface Methodology (RSM) employed face centered Central Composite Design (CCD) required for the analysis. A hybrid approach comprises of grey relation theory in conjunction with RSM Grey- Response Surface Methodology (GRSM) were executed for the prediction of optimal settings of machining condition which yield the optimum performance characteristics of the system. The model fit summary revealing an adequate fitness of the developed model to the actual system, as the R-square value was found to be 0.9607,close to unity and a larger (<4.0) adequate precision value of 20.87. Noticeable improvement were attained on cutting speed (3.234%), kerf width (2.7415%) and surface roughness (7.053%) implementing the optimal setting of machining parameters based on the proposed hybrid GRSM approach. Generous improvement was also noticed on the 3D optical Wire Electro Discharge (WED) machined surface texture demonstrating the quality of surface roughness and texture using hybrid GRSM predicted processing conditions.

Experimental Investigation into Influence of Lathe Cutting Parameters on Surface Roughness for Ferrous and Non- Ferrous Alloys

Optimization of lathe cutting parameters is very important as they form the best suitable conditions for the machining operations. For the efficient use of a CNC Lathe, a set of optimum cutting parameters (speed, feed and depth of cut) is an essential requirement. Surface Roughness, which heavily depends on these cutting parameters, is one of the most frequently used standards to define the quality of turned components. In this work, a correlative study of cutting parameters and the surface roughness for ferrous (stainless steel 304) and non–ferrous alloy (aluminum) material is carried out and presented. Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) techniques are employed to investigate the influence of cutting parameters on surface roughness values. Results from contour plots are obtained to investigate the patterns of factors and the responses. The combination of optimum experimental parameters can be found by machining these ferrous and non-ferrous materials in CNC turning center and finding the least surface roughness parameters. ANOVA analysis, integrated with Design Expert© software, is used to determine effective ratios of the parameters and subsequently the relationships between input parameters and their responses relationship are established. The minimum surface roughness results in reference to spindle rpm, feed rate, and depth of cut are determined and estimation of the optimal surface roughness values (Ra) for least surface roughness are the results obtained in the study. This study presents the findings of an experimental investigation into the effect of turning parameters like cutting speed. Feed rate and depth of cut by turning ferrous (stainless steel 304) and non-ferrous material (Aluminium) in the CNC turning center and then checked the surface roughness values with Mitutoyo SJ-301 instrument. The effects of parameters and their correlation with the surface roughness and the optimal values have been analysed. These results establish a firm relationship and correlation between cutting parameters and surface roughness and in doing so, results also achieve an optimal set of machining parameters for select ferrous and non-ferrous materials.

A Correlative Analysis of Machining Parameters with Surface Roughness for Ferrous and Non- Ferrous Alloy Materials

Average Surface Roughness (Ra) is one of the most frequently used texture parameters to define the quality of turned components. The roughness values of a turned surface much depends on cutting parameters such as cutting speed, feed rate and depth of cut. Optimization of these parameters is very important in relation to surface roughness as they reveal the best suitable conditions for the turning operation. In this project, a correlative study of machining parameters and the surface roughness for ferrous (stainless steel 304) and non–ferrous alloy (Aluminium) material is carried out. Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) techniques are employed in this analysis to explain the influence of different cutting parameters on surface roughness values. The combination of optimum experimental parameters can be found by machining these ferrous and non-ferrous materials in CNC turning center and finding the least surface roughness parameters. ANOVA analysis, integrated with Design Expert software, is used to determine effective ratios of the parameters and subsequently the relationships between input parameters and their responses relationship are established. The minimum surface roughness results in reference to spindle rpm, feed rate, and depth of cut are determined and estimation of the optimal surface roughness values (Ra) for least surface roughness are the results obtained in the study. In case of stainless steel 304, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 1.33 microns are found to be 220 m. min-1, 0.2 mm. rev-1 and 0.3 mm respectively. In case of Aluminium, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 2.8 microns are 200 m. min-1, 0.2 mm. rev-1 and 1.15 mm respectively. These results reaffirm that RSM and ANOVA techniques are useful and efficient in achieving optimal set of machining parameters for select ferrous and non-ferrous materials in correlating the surface finish values.

Analysis of surface morphology and topography of pure aluminium machined using WEDM

Wire electrical discharge machining (WEDM) represents the effective technology of unconventional machining used for the manufacture of components with complex shapes, for machining of hard materials following heat treatment or of non-ferrous metals that are difficult to machine with conventional processes. One non-ferrous material is pure aluminium 99.5 which is used for this experiment: for the design of the experiment, machine parameters were setup for the fabrication of 33 samples aluminium 99.5. The objective of this study was to find the key parameters of machine setup for the manufacture of high-precision components with the required surface quality. For this purpose, an analysis of the morphology of the surface layer was made using electron microscopy (SEM), including a local analysis of the chemical composition (EDX). In addition, the surface topography using 3D profilometer and profile and area parameters of the surface quality were evaluated, including 3D colour filtered and unfiltered images of surfaces taken using light microscopy. The prepared metallographic preparations allowed an analysis of the sub-surface area, including a local chemical microanalysis of “recast layer” using EDX. This study discovered the optimal setting of machining parameters (gap voltage=70V, pulse on time=6µs, pulse off time=50µs, discharge current=25A and wire feed=14m·min−1) for the best quality of machined surface and the narrowest width of kerf for precision machining.

Optimization of electrical discharge machining process parameters for Al6061/cenosphere composite using grey-based hybrid approach

Fly ash has congregated considerable attention as a potential reinforcement for aluminum matrix composites (AMCs) to enhance selective properties and reduce the cost of fabrication. However, poor machinability of such AMCs limits their application. The present study focuses on the preparation of cenosphere fly ash reinforced Al6061 alloys by compo casting method. X-ray diffraction analysis of the prepared AMCs exposes the presence of cenosphere particles without any formation of other intermetallic compounds. In this study, electrical discharge machining (EDM) was engaged to examine the machinability of the prepared metal matrix composite (MMCs). The measured performance characteristics for the various combinations of input process parameters were considered to be MRR, EWR and SR. Face centered central composite design (CCD) of response surface method (RSM) was employed to design the number of experimental trials required and a hybrid approach of grey-based response surface methodology (GRSM) was imposed for predicting the optimal combination of processing parameter in EDM process. Generous improvement was observed in the performance characteristics obtained by employing both the optimal setting of machining parameters. The optical 3D surface profile graphs of the ED machined surface also revealed the improvement in surface quality and texture employing the optimal processing conditions proposed by hybrid GRSM approach.

Analysis of sanding parameters on surface properties and coating performance of red oak wood

ABSTRACTSanding is the most common machining process used to prepare wood surfaces prior to coating. Hence, to improve surface quality and coating performance during this process, an optimal set of machining parameters should be established according to wood species and its final use. The effects of grit size and feed speed on surface properties and coating performance of red oak wood were studied. Surface quality was assessed by surface roughness, scanning electron micrographs, and wetting analyses. Coating performance was evaluated using pull-off adhesion tests on coated surfaces before and after an accelerated weathering. The results showed that smoother surfaces are obtained as the grit size number of the abrasive increased. On the other hand, minor differences were observed on cell damages and surface roughness as feed speed changed. Also, there was little difference among treatments concerning wetting behavior and surface energy. Pull-off strength was more affected by changes in grit size than feed speed. A P100-grit size program with a feed speed of 7 m/min resulted in a suitable roughness which provided good adhesion and high pull-off strength after aging test.

Modelling and multi objective optimization of WEDM of commercially Monel super alloy using evolutionary algorithms

In this research work, development of a multi response optimization technique has been undertaken, using traditional desirability analysis and non-traditional particle swarm optimization techniques (for different customer's priorities) in wire electrical discharge machining (WEDM). Monel 400 has been selected as work material for experimentation. The effect of key process parameters such as pulse on time (TON), pulse off time (TOFF), peak current (IP), wire feed (WF) were on material removal rate (MRR) and surface roughness(SR) in WEDM operation were investigated. Further, the responses such as MRR and SR were modelled empirically through regression analysis. The developed models can be used by the machinists to predict the MRR and SR over a wide range of input parameters. The optimization of multiple responses has been done for satisfying the priorities of multiple users by using Taguchi-desirability function method and particle swarm optimization technique. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. Finally, the confirmation experiments were conducted for the optimal set of machining parameters, and the betterment has been proved.

Optimizing Multi Characterstics in Machining of AISI 4340 Steel Using Taguchi’s Approach and Utility Concept

This paper aims to develop the multi response optimization technique for predict and select the optimal setting of machining parameters while machining AISI 4340 steel using utility concept. The experimental studies in machining were carried out under varying conditions of process parameters, such as cutting speed (v), feed (f) and different cooling conditions (i.e. dry, wet and cryogenic in which liquid nitrogen used as a coolant) by using uncoated tungsten carbide insert tool. Experiments were carried out as per Taguchi’s L9 orthogonal array with the utility concept and multi response optimization were performed for minimization of specific cutting force (K S ) and surface roughness (R a ). Further statistical analysis of variation (ANOVA) and analysis of mean (ANOM) were used to determine the effect of process parameters on responses K S and R a based on their P value and F value at 95 % confidence level. The optimization results proved that, cutting speed 57 m/min, feed 0.248 mm/min and cryogenic cooling is required for minimizes K S and R a .

Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production

Wire-cut electric discharge machining is a nontraditional technique by which the required profile is acquired using sparks energy. Concerning wire-cut electric discharge machining, high cutting rates and precision machining is necessary to improve productivity and achieve high quality of machined workpieces. In this research work, an experimental investigation was introduced to achieve higher productivity of the wire electrode associated with sustainable production in terms of product quality and less heat-affected zone. For this purpose, the effects of machining parameters including peak current, pulse on time and wire preloading were investigated using adaptive neuro-fuzzy inference system along with the Taguchi method. From this study, the optimal setting of machining parameters to achieve higher productivity and sustainability was identified. Moreover, Neuro-fuzzy modeling was successfully used to build an empirical model for the selection of machining parameters to achieve higher productivity at highest possible surface quality and minimum cost for sustainable production.

Surface alloying of H11 die steel by tungsten using EDM process

The objective of this study is to investigate the phenomenon of surface alloying by electrical discharge machining (EDM) process using tool electrodes manufactured by powder metallurgy (PM) process. Efforts have been made to find out the machining conditions that facilitate the transfer of desirable alloying elements (tungsten and carbon) to the machined surface. The quality of the machined surface is the key issue while surface alloying in EDM process. The Taguchi method and L18 orthogonal array are used to find the best level of process parameters in order to achieve the high surface finish and microhardness. Experiments are conducted on hot die steel H11 using electrode (copper-tungsten) manufactured by PM process. Six input parameters are considered, and their significance is investigated by analysis of variance (ANOVA). Different techniques like scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and X-ray diffraction (XRD) have been used to ascertain the characteristics of the machined surface. Results show improvement in microhardness of the alloyed surface by 83 %. Surface machined at optimal setting of machining parameters for best surface roughness gives Ra value of 4.08 μm. EDS of the machined surface confirms the material migration from tool, as the alloyed layer shows significant increase in percentage of tungsten and carbon. The XRD shows the formation of cementite (Fe3C) and tungsten carbide (W3C) on the machined surface which is responsible for the substantial increase in microhardness. SEM shows a crack-free surface which indicates that surface alloying has been carried out without any adverse impact on surface quality.

Application of Desirability Analysis for Optimizing the Micro Wire Electrical Discharge Machining (μWEDM) Parameters

Close tolerance and precision requirements of biomedical components and miniaturization of sensors has given micro wire electrical discharge machining (μWEDM) a substantial amount of research attention. The process parameters like gap voltage, capacitance, wire feed rate and wire tension play an important role in influencing the quality characteristics of the machined parts. The challenge lies in selecting the optimal machining parameter combination to achieve the desired surface finish and metal removal rate in a multi input multi output process like μWEDM. The process parameters were varied at three levels and Taguchi’s L9 orthogonal array was used to design and conduct the experiments. Desirability analysis was applied for predicting the optimal setting of machining parameters and ANOVA results had revealed the significant role of wire feed rate and gap voltage in affecting the quality characteristics of the process

Parameter optimization of multi-pass turning using chaotic PSO

Determination of an optimal set of machining parameters is needed to produce an ordered product of considerable quality and minimal manufacturing cost. The nonlinear and highly constrained nature of machining models restricts the application of classical gradient based techniques for handling such problems. The present study focuses on obtaining the optimal machining conditions during multi-pass turning operations. Methodology used is, a chaotic PSO namely Totally Disturbed Particle Swarm Optimization (TDPSO), an enhanced variant of PSO is employed for obtaining the optimal machining conditions during multi-pass turning operations subject to various constraints. In TDPSO, the phenomenon of chaos is embedded at different stages of PSO in order to make the search process more efficient. Results obtained by TDPSO are compared with results available in literature and it is observed that TDPSO is quite efficient for dealing with such problems.

Multiple Process Parameter Optimization of WEDM on AISI304 Using Utility Approach

Wire EDM is an exceptionally precise, efficient and economical manufacturing route in many applications, allowing customers to design parts for optimum function, without the many restrictions of other metal working process. This paper describes the development of multi-characteristic optimization technique using Utility concept to predict and select the optimal setting of machining parameters of Wire EDM on AISI304 material. Multi characteristic optimization was performed for MRR,SR and DD by using utility concept to find out the optimal process parameter setting .The level of significance of the machining parameters for their effect on the output response were determined by using analysis of variance(ANOVA). The experiment has been done using Taguchi orthogonal array L27. Each experiment was carried out with different conditions of input parameters. The input parameters used for optimization are pulse on time, pulse off time, servo voltage, wire feed, wire tension, and di-electric pressure. The response table and ANOVA for overall utility value were established and the best combination of parameters was obtained. The results revealed that optimum combination of process parameters for multi process parameter optimization, by using utility approach was of, pulse on time level1, pulse off time level 2, servo voltage level1, water pressure level3, wire feed level3 and wire tension level 3.