Optimization Of Machining Parameters Research Articles

Evaluation of the machinability performance of PH 13-8 Mo maraging steel used in the aerospace industry

This study focused on investigating the impacts of machining parameters on cutting force (Fc), surface roughness (Ra), cutting power (Pc), and specific cutting energy (SCE) and determining optimum machining parameters in turning PH 13-8 Mo stainless steel. For this purpose, an experimental design was set with Taguchi's L32 orthogonal array by identifying cutting parameters such as cutting speed (Vc), feed rate (f), and depth of cut (ap) at different levels. The turning tests were carried out on the CNC lathe using a coated carbide cutting tool under dry-cutting conditions. Signal/Noise (S/N) ratio and analysis of variance (ANOVA) were used to determine the impacts of cutting parameters on Fc, Ra, Pc, and SCE values obtained after turning experiments. In addition, regression analysis was performed to identify the correlation between Fc, Ra, Pc, and SCE and cutting parameters through linear and quadratic regression models. The main findings of the study are that the lowest Fc (A1B1C3), Ra (A1B1C4), Pc (A1B1C1) and SCE (A2B4C3) values were measured at 287.8 N, 0.59 µm, 697.16 W and 1549.55 J/mm3 in turning PH 13-8 Mo martensitic stainless steel. ANOVA, the most important process parameter affecting Fc, Ra, Pc, and SCE was feed rate at the rate of 85.57%, 84.93%, 71.10% and 60%. It was observed that the mathematical model findings obtained with the test results are in good agreement. In general, wear mechanisms such as adhesion, BUE, chips, crater, notch, fracture, and chipping on the cutting tool were detected in the turning of PH 13-8 Mo stainless steel.

Ultrasonic tool shank with multiple vibration mode for micro-nano drilling: Design, optimization and experiment

The machining effects of hole drilling is limited by merely changing the amplitude. Adjusting ultrasonic vibration frequency to expand the matching range of machining parameters optimization is necessary to improve hole drilling effects. Therefore, the design, optimization, and experiment evaluation for a novel integrated, fined, low-cost dual-frequency ultrasonic tool shank is proposed, which can work in three vibration modes for micro-nano precision drilling. The principle of the dual-frequency ultrasonic transducer is introduced. The electro-mechanical equivalent circuit model with step horn is applied to study the dual-frequency characteristics. The transducer displacement equation model based on the wave equation theory is deduced to find the shared vibration node of the first and third frequency. Sensitivity analysis by equivalent circuit model and Finite element method (FEM) simulation of the key parameter is both conducted to optimize ultrasonic transducer geometry dimension. The vibration node of mounting flange is specified in an identical position for the dual vibration modes. A prototype of ultrasonic tool shank is fabricated and evaluated in experiments. It demonstrates that the dual working frequency of the ultrasonic transducer is 34.9 kHz and 104.5 kHz in the first and third mode resonant vibration, which is benefit to the low and high frequency drilling. Three working modes of the low, high, and coupling frequency vibration is measured by a self-developed ultrasonic generator. When 40 V voltage is excited, the vibration amplitudes of the low and high frequency are 17.8 µm, and 9.3 µm, respectively, which can meet with the drilling requirement. Moreover, the coupling vibration with the low and high mode are excited simultaneously, and the motion trajectory of the coupling vibration is observed in a “M” shape, which is a novel trajectory for the precision micro-nano drilling. Micro drilling experiments show that the 35 kHz ultrasonic vibration drilling can achieve better machining effect than conventional drilling (CD). The 105 kHz ultrasonic drilling outperforms the CD, 35 kHz and coupling ultrasonic drilling at cutting force reduction. The coupling ultrasonic vibration drilling achieves the lowest chipping diameter. It is to be noted that the 105 kHz-8 µm produce the fined smoothly surface. The 35 kHz, 105 kHz and coupling ultrasonic drilling can produce better surface micro morphology with less quantity and size of craters than that of CD.

Thermal analysis of in-situ laser assisted diamond cutting of fused silica and process optimization

Fused silica is typically a difficult material to machine using conventional methods due to its high brittleness and hardness, despite its significance in optical systems such as high-power lasers and astronomical telescopes. Laser assisted diamond cutting has emerged as a promising and cost-effective alternative for machining fused silica. This study investigated the machinability and thermal response of fused silica during in-situ laser assisted diamond cutting through finite element analysis and cutting experiments. The temperature dependence of thermal parameters, identified by Beetle Antennae Search, was taken into consideration in the thermal model. To minimize surface roughness and determine the optimal machining parameters, various methods were employed, including analysis of variance, signal-to-noise ratio, and Particle Swarm Optimization coupled with Artificial Neural Network. The results indicated that various factors involved in in-situ laser assisted diamond cutting had a significant impact on the thermal response of fused silica, resulting in different surface roughness. Among these factors, spindle speed, feed speed, cutting depth, and laser power contributed to surface roughness by 17.34 %, 12.28 %, 12.03 %, and 48.31 %, respectively. By utilizing the optimal combination of parameters, which comprised a spindle speed of 2700 rpm, feed speed of 1.7 mm/min, cutting depth of 2.7 μm, and laser power of 12.5 W, a smooth surface finish of Sa 12.9 nm could be obtained.

Force modeling of vertical surface grinding considering wheel-workpiece contact geometry

Vertical surface grinding (VSG) is an effective method that can adapt to multiple machining tasks, including stock removal machining to get high productivity and finish machining to obtain high-quality surfaces. Force prediction can reveal some mechanisms in the VSG process, conducing to the improved machining quality and efficiency. This paper aims to develop a novel analytical force model considering the actual wheel-workpiece contact geometry (WWCG). In the modeling process, the cup grinding wheel was divided into many micro-cutting layers along the wheel axis, and the expressions of geometrical-kinematic parameters were derived to characterize the material removal at different positions of the primary grinding zone (PGZ). A single-grain grinding force model was proposed based on chip formation and plastic pile-up mechanisms from an energy perspective. Based on the analysis of multiple-grain interactions at each micro-cutting layer, equations of the local and total grinding forces in the VSG process were then derived by synthesizing the single-grain forces. Findings show that the developed model could accurately predict the magnitudes of total grinding forces and provide the distributions of local grinding forces. The WWCG variation could significantly affect the grinding forces by altering the material removal rate of different micro-cutting layers. This work provides a new method for predicting grinding forces and theoretical guidance for optimizing machining parameters and tailoring the edge profiles of cup grinding wheels to adapt to specific machining tasks.

Modeling and analysis of process parameters in EDM of Ni35Ti35Zr15Cu10Sn5 high-temperature high entropy shape memory alloy by RSM Approach

The Electrical Discharge Machining (EDM) technique demonstrates proficiency in fabricating precise and intricate geometries, especially in challenging-to-machine materials like high-entropy shape memory alloys. Analyzing and optimizing machining parameters are crucial for their direct impact on mechanical properties and overall product efficiency. The main responses chosen to evaluate the processes are material removal rate (MRR), electrode wear rate (EWR), and surface roughness (Ra). At the same time, the associated machining conditions were discharge current (Ip), pulse-on time (Ton), and pulse-off time (Toff). EDM is a multi-response process; therefore, the method of Response Surface Methodology (RSM) is utilized to assess the influence of machining parameters on Ni35Ti35Zr15Cu10Sn5 (at%) high-temperature high entropy shape memory alloy (HT-HE-SMA) using a copper electrode. Based on a center composite design (CCD), experiments were analyzed using Minitab19 software. To identify the most influential parameters, a thorough analysis of variance (ANOVA) at various significance levels (5%) was performed, checking the sufficiency of all fitted second-order regression models. Discharge current, pulse-on time, and pulse-off time were identified as significant factors that affect output (MRR, EWR, and Ra). The model adequacy of the current experimental investigation is perfect, with determination coefficients (R2) of 97.82% for MRR, 95.36% for EWR, and 99.53% for Ra.

Copper Surface Treatment with deep UV Ultrafast Laser for Improved Photocathode Photoemissive Properties

Cesium Telluride (Cs2Te) constitutes today the photoemissive semiconductor material of choice for electron accelerators due to its high quantum efficiency (QE) in the deep ultraviolet (DUV) spectral range, and capability to produce high charge over a long operation lifetime. Unfortunately, its chemical instability requires ultra-high vacuum (in the 10-10 mbar range). This inevitably complicates Cs2Te photocathode handling, and increases the overall cost compared to metallic counterparts. Copper photocathodes are alternative candidates, and although they are much more tolerant in terms of vacuum requirements, their use in high average current photo-injectors is limited due to their orders of magnitude lower QE (around 10-5 per unit). With the development of nanophotonics, plasmonic phenomena can now be exploited to tailor a new range of effects in the photoemission process. In this work, we focus on direct laser fabrication of nanostructures for plasmonic electric-field enhancement on copper, and study their potential for enhancing the quantum yield. We develop a methodology to fabricate the nanostructures by irradiating the Cu surface with 257 nm femtosecond pulses, well above copper’s work function. We directly obtained nanostructures 100-200 nm, matching the plasmonic resonance for photoinjector wavelengths. The study is accompanied by a parametric scan allowing to obtain the optimal laser machining parameters, and the analysis of the nanostructure morphologies obtained.

Quantification of micro-damage evolution process in ceramics through extensive analysis of multi-source heterogeneous data

Due to the randomness of hard and brittle material damage evolution, assessing damage and interpreting mechanisms is challenging. This study focuses on analyzing material damage mechanisms by employing extensive multi-source heterogeneous data to extract quantitative information. A machine vision-based method for detecting and assessing defects in ceramics was proposed, achieving 98 % accuracy. Leveraging this method in scratching experiments, the progression of damage and micro-failure mechanisms in Si3N4 was investigated through the analysis of defect area distribution, force and acoustical signals. Results revealed that the distribution of defect areas conforms to a three-parameter Weibull distribution. At scratch depths of 0.01, 0.02 mm, approximately 90 % of damage accumulated within 1e2–1e4 μm2; conversely, 0.03–0.06 mm led to a decrease to 30 – 40 %. Extensive crack propagation occurs within 0.02–0.03 mm, resulting in the intersecting formation of spalling blocks. Damage evolution occurs in three stages: shallow and small cracks with scattered spalling blocks in the first stage, deep and long cracks with continuous and irregularly shaped spalling blocks in the second stage, and nearly no cracks with continuous and regularly shaped blocks in the third stage. This study not only proposes an efficient method to detect and evaluate ceramic defects, but also deeply understands the evolution process of material damage mechanism. Considering defect distribution and statistical trends, this research not only provide guidance for the optimization of machining parameters, especially in the scene of specific precision and high removal rate. At the same time, the damage evolution and failure mechanism of Si3N4 provide useful experimental basis and data support for deepening the understanding of material behavior during ultra-precision grinding.

Optimization of Processing Machine Parameters for Separating Efficiency of Periwinkles

ABSTRACT The efficient processing of periwinkle is vital to unlocking its economic potentials, thus meeting our nutritional needs. This study aims to optimize the processing parameters using numerical simulation to achieve maximum periwinkle separation efficiency(SE). Models were developed to relate the processing factors and operating parameters with periwinkle SE. The optimum periwinkle machines’ SE of 78.56% and 77.27% were obtained at 133.6 rpm, 1.21 m/s, 0.23 kg/s, and 7.2 min for Tympanotonus fuscatus; 140 rpm,1.23 m/s, 0.19 kg/s, and 8 min for Pachymelania aurita. The results showed that CS, AS, FR and HCT had significant effects on periwinkle meat SE.

AISI D2 Soğuk İş Çeliğinin Delinmesinde Deneysel ve Sonlu Elemanlar Analizi Yoluyla Delik Kalitelerinin İyileştirilmesi ve İşleme Parametrelerinin Optimizasyonu

Increasing supply chain problems in recent years have a negative impact on costs in many areas. Due to the global crisis, raw material prices increased and the manufacturing sector was seriously affected. Production planning and material saving should be done correctly in order to minimize the losses in the manufacturing sector. The most efficient way to reduce production costs is to use suitable cutting tools and choose the right machining parameters. In this study, the drilling of AISI D2 steel, which is widely used in the mold industry, was examined and optimum machining parameters were determined. TiN coated drills are used for drilling operations. The effects of machining parameters on Surface Roughness (Ra) and Thrust Force (Fz) were investigated by Taguchi and ANOVA. Surface roughness and thrust force values during drilling were measured. Thrust force values were also analyzed numerically by the Finite Element (FE) method. Ra and Fz were also low at low feed rate and low cutting speeds. While the feed rate affected Ra the most, the Fz affected the cutting speed the most. The lowest Ra and Fz were at 30 mm/min feed rate and 25 m/min cutting speed. According to the ANOVA, it was determined that the machining parameters were effective on both Ra and Fz. R2 values for Ra and Fz are calculated over 95%.

Fabrication of Electron Beam Melted Titanium Aluminide: The Effects of Machining Parameters and Heat Treatment on Surface Roughness and Hardness

Titanium aluminide alloys have gained attention for their lightweight and high-performance properties, particularly in aerospace and automotive applications. Traditional manufacturing methods such as casting and forging have limitations on part size and complexity, but additive manufacturing (AM), specifically electron beam melting (EBM), has overcome these challenges. However, the surface quality of AM parts is not ideal for sensitive applications, so post-processing techniques such as machining are used to improve it. The combination of AM and machining is seen as a promising solution. However, research on optimizing machining parameters and their impact on surface quality characteristics is lacking. Limited studies exist on additively manufactured TiAl alloys, necessitating further investigation into surface roughness during EBM TiAl machining and its relationship to cutting speed. As-built and heat-treated TiAl samples undergo machining at different feed rates and surface speeds. Profilometer analysis reveals worsened surface roughness in both heat-treated and non-heat-treated specimens at certain machining conditions, with higher speeds exacerbating edge cracks and material pull-outs. The hardness of the machined surfaces remains consistent within the range of 32–33.1 HRC at condition 3C (45 SFM and 0.1 mm/tooth). As-built hardness remains unchanged with increasing spindle and cutting head speeds. Conversely, heat-treated condition 3C surfaces demonstrate greater hardness than condition 1A (15 SFM, and 0.04 mm/tooth), indicating increased hardness with varying feed and surface speeds. This suggests crack formation in the as-built condition is considered to be influenced by factors beyond hardness, such as deformation-related grain refinement/strain hardening, while hardness and the existence of the α2 phase play a more significant role in heat-treated surfaces.

Performance assessment of vegetable-based additive enriched cutting fluid for eco-friendly machining environment.

The investigation focuses on determining the effects of canola oil-based cutting fluid with three different volume percentages of boric acid additives over the machining forces and surface roughness while turning hardened AISI 1018 mild steel. Experiments were carried out under Taguchi's design of the experiment concept. The minimum quantity lubrication (MQL) technique was followed to minimize the cutting fluid consumption. The homogeneity of the additives dispersed in the fluid has been validated through a zeta potential study. Machining forces and surface roughness were considered as chief machining objectives. The hybrid mathematical model, grey relational analysis (GRA)-artificial neural network (ANN), has been implemented to assess the performance of developed cutting fluid. The results explored that the canola oil cutting fluid with 5 wt% of boric acid additive exhibits lesser cutting forces and surface roughness. The optimal machining parameters identified by the hybrid modeling are 665rpm of cutting speed, 35mm/min of feed rate, and 0.3mm of depth of cut, along with 5 wt% of boric acid composition in cutting fluid. The results explore the 2.677 times improvement in machining objective in comparison with a non-optimal set of parameters. The implementation of hybrid modeling is considered to be a novel attempt to minimize the machining objectives. It has been recorded a negligible error percentage of 0.66% between GRA and ANN prediction.

Industrial Robots in Mechanical Machining: Perspectives and Limitations

Recently, the need to produce from soft materials or components in extra-large sizes has appeared, requiring special solutions that are affordable using industrial robots. Industrial robots are suitable for such tasks due to their flexibility, accuracy, and consistency in machining operations. However, robot implementation faces some limitations, such as a huge variety of materials and tools, low adaptability to environmental changes, flexibility issues, a complicated tool path preparation process, and challenges in quality control. Industrial robotics applications include cutting, milling, drilling, and grinding procedures on various materials, including metal, plastics, and wood. Advanced robotics technologies involve the latest advances in robotics, including integrating sophisticated control systems, sensors, data fusion techniques, and machine learning algorithms. These innovations enable robots to adapt better and interact with their environment, ultimately increasing their accuracy. The main focus of this study is to cover the most common industrial robotic machining processes and to identify how specific advanced technologies can improve their performance. In most of the studied literature, the primary research objective across all operations is to enhance the stiffness of the robotic arm’s structure. Some publications propose approaches for planning the robot’s posture or tool orientation. In contrast, others focus on optimizing machining parameters through the utilization of advanced control and computation, including machine learning methods with the integration of collected sensor data.

Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (I p ), Pulse on time (T on ) and Pulse off time (T off ) were the significant parameters which were affecting the tool wear rate (TWR) while the I p , T on and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in I p leads to increase in the wear rate of the tool. The data obtained from experiments were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R 2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses.

Study on tool wear and optimization of machining parameters in laser-assisted fast tool servo machining of glass-ceramic

Glass-ceramic is difficult to be ultra precision machined due to its high hardness and brittleness. Laser-assisted fast tool servo machining (LAFTSM) of glass-ceramic optical free-form surface was carried out with tool wear as the characteristic value to study the machining quality of glass-ceramic. Orthogonal experiments on LAFTSM were conducted using the Taguchi method (TM). The range of tool wear reduction obtained by comparing laser-assisted machining (LAM) with fast tool servo (FTS) machining is 48.83%–64.12%. The order of contribution of each machining parameter obtained through variance analysis to the reduction of tool wear is: spindle speed > laser power > feed rate > piezoelectric frequency. The optimal combination of machining parameters that can minimize tool wear obtained through signal-to-noise ratio (S/N) analysis is: spindle speed 55 rpm, feed rate 0.01 mm/rev, piezoelectric frequency 8 Hz, laser power 75 W. Artificial neural network (ANN) and genetic algorithm (GA) were used to fit and optimize the machining parameters and experimental results in TM orthogonal experiments. The fitting values of ANN are highly consistent with the orthogonal experimental results. The optimal combination of machining parameters obtained after GA optimization analysis is: spindle speed 50 rpm, feed rate 0.015 mm/rev, piezoelectric frequency 4 Hz, laser power 75 W. Experiments were conducted using the optimal combination of machining parameters of TM and ANN, the results showed that ANN performs better than TM in predicting minimum tool wear and optimizing machining parameters. This study provides a reference for LAFTSM and the research methods of tool wear.

Investigation of machining quality indicators and effect of tool geometry parameters during the machining of difficult-to-machine metal

Producing high-quality surfaces from hard and brittle metals presents a formidable challenge that calls for a meticulous machining strategy and an effective cutting tool. The mechanism of tool failure, chip formation behavior, and micro-morphology of the machined surface are crucial factors in determining a correlation between the used parameters and the machined surface quality. Among the metrics employed to assess the effectiveness of the tool. However, the determination of optimum machining parameters requires the repetition of precise experimental investigations with every change in the cutting conditions. The current research work proposes a set of suggestions for choosing the optimal tool parameters based on the micro-morphological characteristics of the tool and the machining performance indicators. A WC-Co ball end mill tool was used for the machining of an H13 tool steel workpiece to establish the relationship between changes in tool geometrical parameters and the machining performance indicators. Tool geometry with a rake angle of 3°, a clearance angle of 10°, and a helix angle of 32° was found to have the least wear band length at 56 μm. Moreover, the impact of tool geometry on the quality of the surface produced through machining was also investigated.

Enhancing lead adsorption capacity prediction in biochar: a comparative study of machine learning models and parameter optimization.

Machine learning models for predicting lead adsorption in biochar, based on preparation features, are currently lacking in the environmental field. Existing conventional models suffer from accuracy limitations. This study addresses these challenges by developing back-propagation neural network (BPNN) and random forest (RF) models using selected features: preparation temperature (T), specific surface area (BET), relative carbon content (C), molar ratios of hydrogen to carbon (H/C), oxygen to carbon (O/C), nitrogen to carbon (N/C), and cation exchange capacity (CEC). The RF model outperforms BPNN, improving R2 by 10%. Additional features and particle swarm optimization enhance the RF model's accuracy, resulting in an 8.3% improvement in R2, a decrease in RMSE by up to 56.1%, and a 55.7% reduction in MAE. The importance ranking of features places CEC > C > BET > O/C > H/C > N/C > T, highlighting the significance of CEC in lead adsorption. Strengthening the complexation effect may improve lead removal in biochar. This study contributes valuable insights for predicting and optimizing lead adsorption in biochar, addressing the accuracy gap in existing models. It lays the foundation for future investigations and the development of effective biochar-based solutions for sustainable lead removal in water remediation.

Characterization and optimization of abrasive water jet machining parameters of aluminium/silicon carbide composites

Due to the complexity of high temperature and cutting tool wear, most machined components are still facing problems in terms of harder functional fillers that reinforce aluminium matrix composites. Conversely, abrasive water jet machining (AWJM) incredibly useful for the cutting of anisotropic and non-homogeneous metal matrix composites. In this research article, silicon carbide (SiC) particulates were utilized as reinforcement in the AA6026 matrix material (AA6026/SiC) and machined using AWJM under different process parameters namely SiC loading, traverse speed and stand-off distance. Two different compositions of SiC (4, and 8 wt%) were considered to fabricate AA6026 composites using the stir casting. In addition, outputs have been examined, e.g., surface roughness, material removal rate, and kerf angle. An optical microscope, scanning electron microscope, Brinell hardness tester and universal testing machine have been used to characterize the matrix material AA6026 and its composites. Microstructural analysis revealed that the inclusion of SiC particulates in AA6026 affects the very fine grain size of the composite. Furthermore, the 8 wt% composite exhibits the evolution of the Al-Si eutectic phase during solidification. Processing of these composites was performed using the L27 orthogonal geometry, successfully improving the parameters of the abrasive water jet process. The output response shows that reducing the SiC load improves the surface roughness under the key parameters of traverse speed and stand-off distance. However, increasing the SiC loading increases the material removal rate and kerf angle under the key parameters, namely traverse speed, and stand-off distance.

Evolution of the Interelectrode Gap during Co-Rotating Electrochemical Machining

A new co-rotating electrochemical machining method is presented to machine the complex structure inside annular parts such as flame tubes and aero-engine casings. Due to the unique shape and motion of electrodes, it is difficult to accurately compute the electric field intensity in the machining area. In this paper, the complex electric field model is simplified by conformal transformation, and the analytical solution of electric field intensity is exactly calculated. A material removal model is built on the basis of the electric field model, and the dynamic simulation of the material removal process is realized. The effects of the cathode radius, applied voltage, feed rate and initial interelectrode gap on the interelectrode gap (IEG) and material removal rate (MRR) are analyzed. The simulation results indicate that the MRR is always slightly less than the feed rate in a quasi-equilibrium state, resulting in a slow reduction in IEG. In addition, the final machining state is not affected by the initial IEG, and the MRR in a quasi-equilibrium state is determined by the feed rate. Several comparative experiments were carried out using the optimized processing parameters, in which the MRR and IEG were measured. The convex structures were successfully machined inside the annular workpiece with optimum machining parameters. The experimental results are in good agreement with the theoretical results, indicating that the established model can effectively predict the evolution process of MRR and IEG.

Multi-attribute optimization of drilling CFRP/Ti-6Al-4V alloy hybrid stacks using VIKOR

Drilling carbon reinforced polymer composites (carbon fiber-reinforced polymers, CFRP) and the hole quality produced are highly critical and significantly influence the performance and reliability of structures used in aircraft components. When CFRP composites are stacked together with titanium alloy, the damage caused by the machining operation is incalculable due to the burr and chip evacuation of metallic phases. In this article, efforts were made toward the identification of optimal machining conditions in drilling CFRP/titanium hybrid stacks. Experimental investigations on dry drilling of CFRP/titanium stacks were conducted with the aid of tungsten carbide drills. This research article discusses an improved compromise ranking method named VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) to determine the optimal machining parameters in drilling of CFRP/titanium stacks to produce high-quality holes. Attributes measured from experimental trials are: delamination factor of CFRP, burr height of titanium alloy, thrust force, torque, surface roughness, roundness and hole diameter of CFRP and titanium alloy. Experimental trial 13 with a combination of speed of 30 m/min, feed of 0.025 mm/rev was chosen as an ideal machining parameter in drilling CFRP/Ti-6Al-4V stacks without any trade-off. Confirmation tests convey that the results agree well with optimal parameters and the decision making in drilling such multimaterial stacks is highly consistent and judicious. In regard to the optimal parameters determined, investigations on tool life were performed and a tool life of 20 min for 0.10 mm flank wear land was observed.

Multi objective optimization of process parameters in hard turning of AISI 52100 steel with surface irregularities using GRA-PCA

Dry hard-turning is a cost-effective, efficient manufacturing method for AISI 52100 hardened bearing steel. Surface Defect Machining (SDM) is a novel approach to address surface roughness, deteriorations, residual stresses, and metallurgical changes on machined steel. SDM involves exposing workpieces to surface irregularities, reducing cutting resistance, and enhancing surface integrity and finish. In the present work, surface irregularities are formed on the surface of the workpiece in the form of indentations. Using the response surface method’s central composite design (CCD), 32 experimental runs were conducted to determine the optimal process parameters by varying the cutting and tool geometry parameters while AISI52100 steel hard turning (HT). Due to its complexity, multi-objective optimization is more challenging to study. The present work aims to evaluate the effects of input parameters on maching force, surface roughness, and workpiece surface temperature. Further, machining parameters optimization is performed employing the Grey relational analysis integrated with principal component analysis (GRA-PCA). Analysis of variance (ANOVA) was used to examine the impact of cutting and tool geometry parameters on grey relational grade (GRG). ANOVA revealed that feed has the highest influence on GRG, followed by depth of cut, nose radius, cutting speed, and negative rake angle. Cutting speed of 800 rpm, feed rate of 0.04 mm/rev, depth of cut of 0.5 mm, nose radius of 1 mm, and negative rake angle of 15° are the optimum combination of process parameters.