Set Of Machining Parameters Research Articles

Optimisation of drilling parameters for minimum circularity error in FRP composite

In composite materials, the damage is characterised by the delamination and circularity error of drilled holes. The amount of delamination and circularity error depends on different machining parameters such as spindle speed, feed Rate, and plate thickness. The present paper attempts to investigate an optimal combination of process parameters for drilling of composite laminates. The work is carried out on industrial grade FRP composite laminates with varying process parameters. The experiments are designed using a 'Box-Behnken design', grounded in the response surface methodology. Circularity error is considered as the output response as it is prominent in the drilling of composite laminates. ANOVA test is carried out to find the significance of the input parameters and test the empirical model. The effect of input parameters on the amount of circularity error is also investigated. Genetic algorithm (GA) and particle swarm optimisation (PSO) is used to predict the optimal settings of input machining parameters.

An Integrated Solution to Minimize the Energy Consumption of a Resource-Constrained Machining System

Going “energy efficient” has been one of the missions for manufacturers to stay globally competitive. Considering machining as a major manufacturing activity, how to effectively model and control its energy consumption becomes critical. Although researchers have analyzed the energy consumption of machining from the machine, process planning, or shop-floor perspective individually, very litter work has comprehensively studied the concurrent interactions among energy-aware decisions for machine parameter settings, process planning, and shop-floor control. Hence, the work presented in this article undertakes this challenge in the context of a resource-constrained machining system. In particular, the energy characteristics of machining are first analyzed with the consideration of various machine tools, cutting tools, cutting parameters, operation sequences, as well as machine availability. A multiobjective optimization model is then developed to minimize both energy consumption and makespan. The solution is provided through honey bee mating optimization algorithm (HBMOA) combined with shop-floor simulation. In addition, the significance of the proposed approach is exemplified and elucidated by a case study. Note to Practitioners —A well-informed decision made in cutting parameter optimization or process planning relies heavily on the accurate data delivered from the shop floor. In other words, the decision made without the consideration of the shop-floor situation might not achieve the original goal or even fail to materialize. As these factors exist in a real manufacturing cycle, how to integrate shop-floor scheduling with process planning and machining parameter optimization becomes essential for energy-efficient manufacturing. This article undertakes this challenge and develops a multiobjective optimization model, where energy consumption of machining, for the first time to the best of our knowledge, is comprehensively analyzed at both the machine level and shop-floor level. Such broader integration makes this approach more practical and applicable to real industry settings, particularly when the shop-floor resource is limited.

Machinability analysis and hybrid optimization during wet turning of SS304 using coated tools

Abstract In this work, SS304 has been machined using tungsten carbide tool inserts coated with multilayer of TiN/TiAlN under conventional wet cooling condition. Experiments have been conducted based on Taguchi robust technique with L9 orthogonal array. Effects of three important machining parameters cutting speed (CS), feed (F), and depth of cut (DC) on machinability aspects i.e. surface roughness (Ra) and material removal rate (MRR) have been investigated. Further, grey relational technique integrated with genetic algorithm (GA) was used for simultaneous optimization of MRR and Ra. It was observed that the optimized machining parameter setting for MRR and Ra is CS: 170 m/min; F: 0.2 mm/rev; and DC: 0.5 mm. The optimum values of MRR and Ra are 81.39 g/min and 3.14 µm respectively.

188. Optimization of the Pinnacle 9.10 Physics parameters for the volumetric modulated arc treatments with Autoplanning module

Purpose In order to optimize the agreement between the planned and the delivered dose distribution for VMAT treatments and thus obtain better pretreatment dosimetric evaluation, a set of Linac Elekta Synergy Agility specific machine parameters has been tuned in the TPS Pinnacle 9.10 with autoplanning module. Methods The introduction of the autoplanning module in the TPS resulted in a higher complexity of the treatment plans, worst affecting, as a consequence, the outcome of pretreatment dosimetric evaluation. This situation has been confirmed by a retrospective analysis of VMAT treatments of the Head&Neck district delivered in our center since 2015. The set of machine parameters considered in this study included dose rate, leaf motion, gantry speed and dynamic leaf gap. The association between these parameters and the outcome of pretreatment dosimetric evaluation by OCTAVIUS 4D (PTW) with 729 matrix has been performed. Results Our analysis suggests to use more stringent constraints for dose rate (by setting effective rather than nominal value as the maximum dose rate), leaf motion and gantry speed. As for the dynamic leaf gap, a commissioning of two machines was performed including a machine with the minimum achievable value to carry out stereotactic treatments in proximity of risk organs or multiple targets and a machine with a greater value for the remaining treatments. As a result, for the non SRT/SBRT VMAT treatment, these two machines enabled to obtain clinically equivalent plans in terms of PTV and OAR, but with less modulation. Conclusions The dynamic leaf gap resulted as the parameter impacting the most on the complexity of the plans [1] , also affecting the outcome of pretreatment dosimetric evaluation. To have a greater flexibility in the planning phase, it could be helpful to have the possibility of setting the value of this parameter outside the physics of the machine.

Online Tool Condition Monitoring Based on Parsimonious Ensemble.

Existing methodologies for tool condition monitoring (TCM) still rely on batch approaches which cannot cope with a fast sampling rate of a metal cutting process. Furthermore, they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online TCM approach based on Parsimonious Ensemble+ (pENsemble+). The unique feature of pENsemble+ lies in its highly flexible principle where both the ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. This paper presents advancement of a newly developed ensemble learning algorithm, pENsemble, where the online active learning scenario is incorporated to reduce the operator's labeling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilizing two real-world manufacturing data streams: 1) metal turning and 2) 3-D-printing processes and comparisons with well-known algorithms were carried out. Furthermore, the efficacy of pENsemble+ was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of the operator's labeling effort.

Experimental investigations into transient roughness reduction in ball-end magneto-rheological finishing process

ABSTRACTBall-end magneto-rheological finishing (BEMRF) process is a variant of magneto-rheological finishing (MRF) which is capable of finishing magnetic as well as nonmagnetic materials to finish surfaces up to the order of nanometers. The development of this process has been fairly recent and the literature has focused on quantifying and analyzing the effects of finishing on different materials with it. This research work presents a time-based roughness reduction study of BEMRF process to find out the behavior of machining parameters in finishing over lengthy time intervals. EN31 steel has been chosen as the work material for this study. Firstly, a machining parameter optimization is carried out for finishing of EN31 steel with BEMRF process. A detailed time-based experimental study is then carried out to find the effect of time on finishing with BEMRF process. The time-based study revealed a transient roughness reduction phenomenon in which a particular set of machining parameters was capable of reducing the surface roughness only up to a limited roughness value. The study concluded a database for finishing of EN31 steel with BEMRF process which can be used for optimum time-based finishing with BEMRF process.

Multi-objective optimization of WEDM process parameters of Ti49.4-Ni50.6 shape memory alloy for orthopaedic implant application

Purpose: This paper focuses on the investigation on the effect of process parameters such as pulse on time (Ton), pulse off time (Toff), spark gap set voltage (SV), wire feed (WF) and wire tension (WT) on the responses such as the material removal rate (MRR), surface roughness (SR), kerf width (KW) and dimensional deviations (DD) of Ti49.4Ni50.6 (at.%) shape memory alloy (SMA) machined by WEDM. Ti-Ni SMA has fascinating properties and biocompatibility, it is considered for the present work. Design/methodology/approach: As per the Taguchi technique, L18 orthogonal array experiments on WEDM have been performed. The signals to noise (S/N) ratio plots are analysed to determine the influence of process parameters. Analysis has been tested through analysis of variance (ANOVA). SEM images are taken to confirm the results offering better surface quality. Findings: It was observed that pulse on time is the most significant factor for MRR and SR with the contribution of 35.69% and 59.02% respectively. The SV is a significant factor for KW and DD with contributions of 47.35% and 30.03% at 95% confidence level. A multi-response optimization has been carried out using grey relational analysis (GRA) to determine the optimum combination of process parameters. It is shown that, through GRA the optimal machining parameter setting such as A2B1C3D2E1 i.e. (pulse on time of 115 machine unit, pulse off time of 20 machine unit, spark gap set voltage of 90 V, wire feed of 6 m/min and wire tension of 3 machine unit) has been observed for maximum MRR and minimum SR, KW and DD. Young’s modulus checked for biocompatibility. Research limitations/implications: Heat treatment process like annealing is found to be most suitable to recover shape memory effect of WEDMed samples.

Mathematical Methods of Surface Roughness Evaluation of Areas with a Distinctive Inclination

Surface roughness is one of the most significant evaluation factor in metal machining operations. Despite the small descriptiveness is usually quantified as the arithmetical mean roughness Ra especially in the automotive industry. In order to maintain the desired surface roughness, the appropriate setting of machining parameters is important to be set before the actual cutting process. The objective of this research is to analyse the effect of machining parameters on surface roughness of stainless steel X153CrMoV12-1 in CNC milling of slope surfaces with sintered carbide tool. The data were processed using multiparametric statistical methods to find optimal results. Linear regression models and probability dendrograms of similarities were determined for cutting conditions, cutting tools and slope of machined surfaces.

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.

ANN-NSGA-II dual approach for modeling and optimization in abrasive mixed electro discharge diamond grinding of Monel K-500

Hybrid machining processes (HMPs) have attracted the attention of investigators from both academia and industry due to their enhanced process performance and capabilities while machining so-called difficult-to-cut materials. In this paper, the dual approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was used to model and optimize a new HMP called as Abrasive Mixed Electro Discharge Diamond Grinding (AMEDDG). Due to complex nature of AMEDDG process, the choice of an appropriate coalition of input factors is an effortful job for machinist. The central composite rotatable design was used to plan the experiments and ANN model was established to observe the effect of input machining parameters viz. Wheel speed, powder concentration, pulse current, and pulse-on-time on material removal rate (MRR) and average surface roughness (Ra). The established ANN model was found to be capable of forecasting the output responses within tolerable limits for the chosen set of machining parameters. An ANN-NSGA-II based dual approach was applied for multi-objective optimization of control factors in AMEDDG, and experimental validation directs that optimal data was in tolerable limits.

Dense yttria-stabilized zirconia obtained by direct selective laser sintering

This work demonstrated the possibility to manufacture dense yttria-stabilized zirconia (YSZ) parts by direct laser beam sintering using a commercial 3D SYSTEMS machine. To achieve this, several requirements of the process itself had to be met. First, an efficient laser-matter interaction had to be achieved. Indeed, a major issue to overcome was the intrinsic nonabsorbance property of the YSZ powder at the Nd:YAG laser wavelength. This issue was solved by adding a small amount of graphite to the YSZ powder that resulted in a simple mechanical blending of the two compounds. The graphite adjunction allowed an increase in the absorbance value from 2% to approximately 60%. The chemical composition of the powder blend and the density and the particle size distribution of the starting material used in this work enabled adequate heat transfers due to the appropriate thermophysical properties. Finally, a set of machine parameters that allows to manufacture parts with a high relative density, good geometrical accuracy, and mechanical strength was experimentally determined. Ultimately, it was found that the direct SLS allows the reproducible manufacture of simple YSZ parts with a relative density of 96.5%.

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.

Effect of Width of Kerf on Machining Accuracy and Subsurface Layer After WEDM

Wire electrical discharge machining is an unconventional machining technology that applies physical principles to material removal. The material is removed by a series of recurring current discharges between the workpiece and the tool electrode, and a ‘kerf’ is created between the wire and the material being machined. The width of the kerf is directly dependent not only on the diameter of the wire used, but also on the machine parameter settings and, in particular, on the set of mechanical and physical properties of the material being machined. To ensure precise machining, it is important to have the width of the kerf as small as possible. The present study deals with the evaluation of the width of the kerf for four different metallic materials (some of which were subsequently heat treated using several methods) with different machine parameter settings. The kerf is investigated on metallographic cross sections using light and electron microscopy.

Utility approach for multi target streamlining of process parameters in wire EDM

This paper depicts the improvement of multireaction enhancement system utilizing utility technique to foresee and select the ideal setting of machining parameters in wire electro-release machining (WEDM) process. Investigations were arranged utilizing Taguchi's L27 orthogonal exhibit. A wide range of Wire EDM control variables such as pulse on time duration, pulse off time duration, servo voltage along with wire feed rate were judged for investigation. Multi reaction enhancement was performed for both cutting pace (CS) and surface unpleasantness (SR) utilizing utility idea to discover the ideal procedure parameter setting. The level of essentialness of the machining parameters for their impact on the CS and SR were controlled by utilizing investigation of fluctuation (ANOVA). In present study utility approach method used to optimize the process parameter in wire EDM of magnesium Al6061/SiC/Graphite hybrid composite with zinc covered brass wire electrode. The approach depicted here is relied upon to be profoundly useful to assembling enterprises, and furthermore different territories, for example, aviation, car and apparatus making businesses. The parameters corresponding to experiment run number 7 are pulse on time 108 units (Level 1), pulse off time 60 units (Level 3), peak current 230 units (Level 3), gap set voltage 60 units (Level 3), wire feed 3 units (Level 1) and wire tension 4 units (Level 1) are the best combination to achieve better surface roughness and cutting speed.

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.

Experimental study and empirical analyses of abrasive waterjet machining for hybrid carbon/glass fiber-reinforced composites for improved surface quality

Poor surface quality is one of the critical defects after trimming of fiber-reinforced plastic (FRP) composites through both conventional and non-conventional machining processes. With the recent introduction of hybrid composites from different fiber reinforcements, this makes the trimming or cutting of them challenging. Therefore, an experimental study was attempted to elucidate the effect and relationship between the machining parameters in the abrasive waterjet cutting, namely abrasive flow rate, hydraulic pressure, and stand-off distance, and traverse rate on the surface roughness of the machined composites. An optimum setting of machining parameters and mathematical modeling equation were obtained by applying the response surface methodology for improving the surface quality. It is apparent that the abrasive flow rate and stand-off distance contributed the most in affecting the surface roughness of the hybrid FRP composites. The mathematical relationship, which is in the form of quadratic function, has been validated with confirmation test in order to optimize the surface roughness.

Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function

In this study, 25 (five factors at two-level factorial design) design of experiment was applied to investigate a set of optimal machining parameters to achieve a minimum surface roughness value for Abies nordmanniana. Wood specimens were prepared using different values of spindle speed, feed rate, depth of cut, tool radius, and cutting directions. Average surface roughness $$ \left( {R_{z} } \right) $$ values were applied using a stylus. The objectives were to: (1) obtain the effective variables of wood surface roughness; (2) analyze which of these factors had an impact on variability in the CNC machining process; (3) evaluate the optimal cutting values within the range of different cutting levels of machining parameters. The results indicate that the design of experiment (DOE) based on the desirability function approach determined the optimal machining parameters successfully, leading to minimum R a compared to the observed value. Minimum surface roughness values of tangential and radial cutting directions were 3.58 and 3.21 µm, respectively.

Parameter optimization of electrochemical machining process using black hole algorithm

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Nanoscale electric discharge‐induced FLG peeling off during the STM electric lithography

Scanning tunnelling microscope (STM) electric lithography exhibits its unique advantages in the nanoscale positioning accuracy, in situ measurement, atomic manipulation and ultra-precise fabrication. Nowadays various nanoscale structures and devices have been delivered from STM electric lithography by versatile means of the local mechanical force, the chemical reaction and the thermal effects. In this paper, the authors have innovatively utilised the nanoscale electric discharge effect induced by the STM electric lithography to peel off the site-specific few layer graphene (FLG) flakes on the highly oriented pyrolytic graphite sample. The piece number, distribution, area and thickness of the exfoliated graphene flakes generated under the different machining parameter settings were quantitatively analysed. It has been found out that both of the machining gap distance and the bias voltage amplitude play the decisive roles in the exfoliation of the FLG flakes. Moreover, the machining current was measured to validate the occurrence of the nanoscale electric discharge during the STM electric lithography processes. It directly proves the relevance of FLG flakes peeling off to the electric discharge. This work has confirmed the processing capability of the STM electric lithography for the FLG flakes peeling off, which is desired in the fabrication of graphene-based nanodevices.

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.