Machined Surface Research Articles

A new method for fast and accurate identification of tool-workpiece relative vibration based on machining surface topography

A new method for fast and accurate identification of tool-workpiece relative vibration based on machining surface topography

Exploratory data analysis & EDS analysis on tool chip adhesion under dry and nano minimum quantity lubrication machining environment

Adhesion, abrasion, plowing and transverse displacement are the major causes of origin of friction. During dry machining of ductile materials, chips are fused with the rake face of the cutting tool and contribute to the formation of the built up edge. Built up edge protects the rake face of the tool from wear, but makes the machined surface rough. To improve the tribological properties and to decrease the tool-chip adhesion, cutting fluids are used. Cutting fluids are added to the machining zone to minimise cutting temperature and friction, but they result in environmental and health degradation. Surface texturing of tools is a potential way to modify the tribological properties of mating surfaces in order to increase the tribological qualities and decrease the tool-chip adhesion. The performance of surface textured tools depends upon orientation, dimple depth, width and pattern. Under lubrication regime, microholes in surface textured tools acts both as lubrication reservoir and traps wear debris to reduce abrasive wear. Nano Minimum Quantity Lubrication (nano-MQL) is an advanced lubrication technique used in machining processes, specifically designed to minimize the use of lubricants while maintaining effective lubrication. This method involves delivering an extremely small amount of high-performance lubricant directly to the cutting zone. The MoS2 lubricant with sunflower oil is typically in the form of nanodroplets, which are much smaller than conventional minimum quantity lubrication (MQL) droplets. The Exploratory Data Analysis (EDA) methodology enables the identification of patterns, trends, and outliers within the collected data, offering insights into the complex interplay of factors affecting the machining of Aluminium Alloy AA2024. At a spindle speed of 3500 rpm the feed force decreases by 37% under dry environment when compared with nano-MQL environment. Under dry environment average surface roughness reduces by 27% when compared with nano-MQL environment.

Resolving high-strain-rate scratch behavior of Ti6Al4V in experiment and meshless simulation

The outstanding strength-to-weight ratio and corrosion resistance of titanium have made it the material of choice in the aerospace industry and medicine. The alpha–beta alloy Ti6Al4V is particularly preferred for its excellent mechanical and bio-compatible properties. Despite its advantages, the low thermal conductivity and poor tribological performance of titanium pose significant challenges during manufacturing and in operation. This research offers deep insights into the high strain rate behavior of Ti6Al4V under abrasive load, such as e.g. experienced in machining, by modifying the standard scratch test setup and using optimized Johnson–Cook material parameters to perform Material Point Method (MPM) simulations. The MPM simulations provide accurate predictions of the data gathered through high strain rate scratch experiments. We found an increase in the von Mises stress distribution as well as the normal and tangential forces required to perform a scratch of the same depth as the strain rate increases. The morphology of the scratch profiles also showed an increase in the height of the ridges that form as the scratching speed increases. These findings are in line with the increase in yield strength and work hardening with growing strain rate. This study bridges the gap between simulation models and experimental observations by providing insights for improved machining strategies and surface treatments that can enhance the performance of Ti6Al4V in demanding applications.

Application of Abrasive Water Jet Cutting on Rubber Wood Plastic Composites

Wood-plastic composites (WPCs) possess a diverse arrange of advantageous characteristics resulting from the combination of wood and plastic. These include a prolonged lifespan, cost-effectiveness, and environmental friendliness. However, the net shape can only be roughly achieved by the extrusion method used to make WPCs. WPCs require additional machining to comply with the dimensional accuracy and assembly conditions. As a result, more research into the cutting process of this composite is needed. In this work, abrasive water jet cutting was used to cut WPCs manufactured from rubber wood flour and polypropylene (PP). The emphasis was on two distinctive architectures of 1-layered and 3-layered WPCs with a thickness of 10 mm. The effects of cutting factors including water pressure, traverse speed, and abrasive mass flow rate were taken into account. The study employed a full factorial analysis to investigate the correlation between cutting parameters and cutting performance, including kerf characteristics and machined surface roughness. The abrasive and water pressure in the experiment had a significant effect in cutting both materials. In some cutting conditions, without an abrasive, non-through cutting happens, particular for the 3-layered WPC structures, that are particularly strong. An increase in water pressure decreased machining surface roughness and kerf width. The cutting parameters of 350 MPa water pressure, 30 mm/s traverse speed, and 6.67 g/s abrasive flow rate were suggested for both the 1-layered and 3-layered WPCs structures. This procedure was considered suitable for post-cutting without requiring for surface finishing.

Performance evaluation of self-propelled rotary tools and fixed round inserts in machining

This study evaluates the analysis of round inserts (Fixed and Self-Propelled Rotary Tools) when machining hardened steel. Self-propelled rotary Tools (SPRTs) exploit the entire perimeter of the insert rather than a specific cutting edge, minimizing insert wear and maximizing insert life. It is considered an environmentally friendly process. Tool and insert geometry have a direct effect on the surface roughness. Previous studies highlighted the significant effects of one inclination angle on surface roughness. Despite previous research that investigated only one inclination angle, in this research, the effect of both inclination angles has been investigated, and a new method for simultaneous application has been used. The optimal use of both inclination angles has significantly improved the quality of the machined surface. Therefore, an optimal holder has been designed and fabricated based on the cutting forces and component shapes, allowing inclination angle changes. The full factorial design of the experiment is adopted, and the effects of the horizontal and vertical inclination angles and feed rate in fixed tool and SPRT turning have been evaluated, each in 3 levels with a total of 54 experiments. The equivalent insert radius is defined using the inclination angles and an analytical model is developed concerning the equivalent insert radius to predict surface roughness. The proposed model accurately estimated roughness in fixed tool turning for an R-square of 0.81 and 0.76 for SPRT turning. In the best condition, the surface roughness has been measured at 0.472 μm in the fixed tool turning and 0.409 μm in SPRT turning.

Simulation Study on Residual Stress Distribution of Machined Surface Layer in Two-Step Cutting of Titanium Alloy.

Ti-6Al-4V titanium alloy is known as one of the most difficult metallic materials to machine, and the machined surface residual stress distribution significantly affects properties such as static strength, fatigue strength, corrosion resistance, etc. This study utilized finite element software Abaqus 2020 to simulate the two-step cutting process of titanium alloy, incorporating stages of cooling, unloading, and de-constraining of the workpiece. The chip morphology and cutting force obtained from orthogonal cutting tests were used to validate the finite element model. Results from the orthogonal cutting simulations revealed that with increasing cutting speed and the tool rake angle, the residual stress undergoes a transition from compressive to tensile stress. To achieve greater residual compressive stress during machining, it is advisable to opt for a negative rake angle coupled with a lower cutting speed. Additionally, in two-step machining of titanium alloy, the initial cutting step exerts a profound influence on the subsequent cutting step, thereby shortening the evolution time of the Mises stress, equivalent plastic strain, and stiffness damage equivalent in the subsequent cutting step. These results contribute to optimizing titanium alloy machining processes by providing insights into controlling residual stress, ultimately enhancing product quality and performance of structural part of titanium alloy.

Polishing Machine Control System Based on B-Spline Curve Trajectory

In this study, a B-spline trajectory control algorithm is presented, showcasing its ability to achieve smooth trajectory control within an embedded motion control system. Traditional surface machining methods involve converting interpolated trajectories into G-code using numerical control (NC) software, which is then downloaded and executed on the numerical control system. This research introduces a specialized third-degree B-spline interpolation method tailored for curved surface trajectories, aiming to enhance machining efficiency. A significant strength of this project lies in the holistic design of an embedded system, encompassing both hardware circuitry and software logic. Utilizing the cost-effective STM32 architecture, a B-spline discrete velocity interpolation algorithm is implemented, validated through experiments conducted on a polishing machine system. The project involves MATLAB software for data simulations and experiments on a polishing machine using B-spline curve interpolation, confirming the practicality and effectiveness of the third-degree B-spline algorithm within an embedded system.

Estimation of surface quality for turning operations using machine learning approach

The present article examines the effects of machining parameters on machined surfaces to determine optimum turning parameters for AISI-316 under dry machining environment. L27-OA with different levels of Cutting Speed (CS), Feed Rate (FR) and Depth-of-Cut (DOC) is used for experimentation. Surface Roughness (Ra) and Material Removal Rate (MRR) are considered as the response parameters. Among three Machine Learning (ML) models viz. Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Gradient Boosting Regression (GBR), GBR yielded the best results, with significantly higher R2 scores and lower RMSE values. An integration of GBR-PSO algorithms is used to determine 50 sets of, Pareto solutions and desirability analysis rendered the most suitable input parameter values as 111.66 m/min for CS, 0.15 mm/rev for FR and 1.23 mm DOC. At validation stage, Ra and MRR predicted by ML have a nominal difference of 15.19% and 0.115% compared to measured values.

Surface quality enhancement in ultrasonic vibration-assisted electrochemical machining

The aviation industry's evolution has introduced electrochemical machining (ECM) as a promising non-traditional technology for aero-engine blades. However, the surface quality of challenging-to-cut materials is compromised due to insoluble electrolytic products on the machined surface. To enhance ECM's surface quality, ultrasonic-assisted ECM (UA-ECM) was introduced for effective electrolytic product removal. This study conducted numerical simulations involving coupled flow field and cavitation models. It also performed observation experiments and a series of UA-ECM trials with varying inlet pressures (0.1 MPa, 0.2 MPa, 0.3 MPa, 0.4 MPa, and 0.5 MPa), vibration amplitudes (21 µm, 17 µm, 13 µm, 8 µm, 4 µm, and 0 µm), and current densities (31.9 A/cm2, 42.1 A/cm2, 61.1 A/cm2, 76 A/cm2, and 100.9 A/cm2). These aimed to elucidate the mechanism of tool ultrasonic vibration's influence on surface quality and corroborate the simulation findings. Notably, under conditions of low inlet pressure (0.1 MPa), high vibration amplitude (21 µm), and high current density (100.9 A/cm2), complete removal of electrolytic products, inhibition of pitting defects, and attainment of a mirror surface with a surface roughness of Ra = 0.14 µm were achieved, with no change in hardness. This clearly demonstrates that the surface quality of ECM on complex surfaces can be significantly enhanced by ultrasonic assistance.

Method for profile reconstruction of the workpiece surface during groove micromilling

A leading trend in modern industry is hybrid manufacturing methods. The intention is to integrate additive and subtractive methods into a single machine. In such a process, it is difficult to evaluate the quality of the manufactured component between successive processes. A solution that would make it possible to determine the quality of a component produced by incremental technologies on the basis of forces recorded in a machining process is not yet available. In the present study, an attempt was made to reconstruct the surface of the machined workpiece solely on the basis of recorded forces during the groove micromilling process. A workpiece with a known surface geometry in the form of a sinuous wave was machined. The geometry of the workpiece corresponded to a structure typical of the selective laser sintering method for metallic powders. A process conducted with two different axial cutting depths was considered. The reconstructed surface profiles were evaluated by comparative analysis with the real machined surface.

Investigation of the Surface Characteristics of GCr15 in Electrochemical Machining.

Bearing steel (GCr15) is widely used in key parts of mechanical transmission for its excellent mechanical properties. Electrochemical machining (ECM) is a potential method for machining GCr15, as the machining process is the electrochemical dissolution of GCr15 regardless of its high hardness (>50 HRC). In ECM, NaNO3 solution is a popular electrolyte, as it has the ability to help in the nonlinear dissolution of many metallic alloy materials, making it useful for precision machining. However, due to high carbon content of GCr15, the electrochemical dissolution of GCr15 is unique, and there is always a black layer with high roughness on the machined surface, reducing the surface quality. In order to improve the electrochemical machining of GCr15 with a high surface quality, the surface characteristics of GCr15 in ECM were investigated. The anodic polarisation curve in the NaNO3 electrolyte was measured and electrochemical dissolution experiments were conducted with different current densities. SEM, XRD, and XPS were employed to analyse the surface morphology and composition formed on the machined surface at different current densities. The initial results showed that there were two parts (black part and bright part) formed on the machined surface when a short circuit occurred, and the test results suggested that the black part contained a mass of Fe3O4 while the bright part was composed of mainly Fe and Fe3C. Further investigation uncovered that a black flocculent layer (Fe3O4) always formed in a low current density (32 A/cm2) with high roughness. With the current density increased, the amount of black flocculent layer was reduced, and Fe3C particles appeared on the machined surface. When the current density reached 81 A/cm2, the entire flocculent oxide layer was removed, only some spherical Fe3C particles were inserted on the machined surface, and the roughness was reduced from Ra7.743 μm to Ra1.783 μm. In addition, due to exposed Fe3C particles on the machined surface, the corrosion resistance of the machined surface was significantly improved. Finally, circular arc grooves of high quality were well manufactured with current density of 81 A/cm2 in NaNO3 electrolyte.

Evaluation of radiation leakage in X-ray security inspection machine using a CZT spectrometer

Leakage radiation from X-ray security inspection machines is important, and measurement based on ionization chamber or scintillator detector is widely used. The leakage radiation is closely related to the size and the passing time of the luggage, the lead equivalent, the opening angle of the lead curtain, and the response time of the measuring instrument. To characterize the distribution of leakage radiation from the X-ray security inspection machine accurately, a small-volume CZT(CdZnTe) spectrometer was used to measure the energy spectra at a distance of 5 cm from the surface of the inspection machine. By designing and controlling the opening angle of the lead curtain according to the size of the luggage passing through the entrance and exit, the radiation dose was determined based on the measured energy spectra combined with the G(E)-function method. The results show that the maximum relative deviation between the air kerma rate and the ambient dose equivalent rate calculated by the G(E) function method with the standard dose rate does not exceed ±5%. The maximum relative deviation of the dose rate linear verification in the 137Cs radiation field is less than 2.5%. A calibrated CZT detector was utilized to measure the radiation leakage on the surface of the X-ray security inspection machine. It was discovered that the presence of the luggage items and the opening angle of the lead curtain will increase the leakage radiation dose on the surface of the security inspection machine system. This study provides a new approach for measuring scattered radiation of X-ray security inspection machines.

Role of spark energy and frequency on wire-EDM performances of Al-2Mg alloy and Al-2Mg/20 Al3Fe composite: a comparative study

ABSTRACT The performance of wire-electric discharge machining (WEDM) method is inevitably affected by the electric spark. A comparative study is presented related to the influences of spark energy (SE, 1.68–8.40 mJ) and spark frequency (SF, 12.82–15.15 kHz) on machining responses of Al-2Mg/20 vol.% in-situ Al3Fe composite with respect to Al-2Mg alloy. Such comparison is scanty in the literature. Machining performances were measured concerning material removal rate (MRR), cutting speed (CS), and surface roughness (SR) apart from the morphological evolution of machined surfaces. Irrespective of machining condition (for instance, at SE of 5.04 mJ), composite exhibited lower CS (1.27 mm/min), MRR (4.20 mm3/min), and SR (4.42 µm) due to the insulating effect of reinforcement than alloy (i.e. 1.70 mm/min, 5.81 mm3/min, and 4.82 µm, respectively). For both alloy and composite, all three machining performances increased linearly with rising the SE and SF, although the influence of SE was more pronounced over SF. With increasing SE, the difference in the magnitudes of CS and MRR between alloy and composite widened, while the same for SR narrowed. Formations of micro-cracks, lumps of debris, craters, and re-solidified layers were observed more on alloy machining surface than composite; these features enlarged with increasing SE and SF.

Graphene nanoplatelets-water nanofluids: A sustainable approach to enhancing Ti-6Al-4V grinding performance through minimum quantity lubrication

This study introduces a stable, water-based graphene nanoplatelets (GNPs) nanofluid as a sustainable cutting fluid for grinding Ti-6Al-4V alloy under minimum quantity lubrication (MQL). Using ethanol and sodium deoxycholate (SDOC) surfactant, the dispersion stability of GNPs in water was remarkably extended from 1 h to 57 days. This novel nanofluid formulation enhances wettability, thermal conductivity, and tribological properties. Grinding tests showed a 0.35 mg/mL water-GNPs concentration reduced grinding forces by 74.44 % and 33.37 % compared to dry and conventional flood lubrication, respectively, while improving surface roughness. Raman spectroscopy confirmed graphene's presence on the machined surface, highlighting the potential of water-GNPs nanofluids as an eco-friendly alternative to traditional metalworking fluids, enhancing sustainability in manufacturing.

On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture

Abstract On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.

Novel machine learning-driven multi-objective optimization method for EDM trajectory planning of distorted closed surfaces

Highly distorted closed surfaces pose significant challenges for machining trajectory planning due to their intricate surface constraints and closed structures. Despite these challenges, components with such features are prevalent in industries like aerospace. This paper presents a machine learning-driven multi-objective optimization method for electrical discharge machining (EDM) trajectory planning of highly distorted closed surfaces. The method transforms the structural design of forming electrodes and trajectory planning into a multi-objective decision problem. And a discrete point trajectory planning method, guided by surface average curvature, is employed to determine the optimal position and orientation of the electrode. Additionally, an elite dataset, generated using the Monte Carlo method and Arena's Principle, is utilized to train an artificial neural network (ANN). This network predicts hyperparameters for the nonlinear optimization problem. Based on the proposed method, a multi-objective optimization model is formulated for an integral shrouded blisk, considering minimization of iteration count, axial motion, and maximization of machining surface quality. The Pareto front is utilized to obtain the optimal EDM trajectory. Experimental results demonstrate a 17.38 % reduction in the overall machining cycle duration using this trajectory, and the surface roughness and profile accuracy satisfy the design specifications, which proves the effectiveness of this method.

Deciphering the Factors Controlling Hydrogen and Methyl Spillover upon Methane Dissociation on Rh/Cu(111) Single‐Atom Alloy

AbstractSpillover of adsorbed species from one active site to another is a key step in heterogeneous catalysis. However, the factors controlling this step, particularly the spillover of polyatomic species, have rarely been studied. Herein, we investigate the spillover dynamics of H* and CH3* species on a single‐atom alloy surface (Rh/Cu(111)) upon the dissociative chemisorption of methane (CH4), using molecular dynamics that considers both surface phonons and electron‐hole pairs. These dynamical calculations are made possible by a high‐dimensional potential energy surface machine learned from density functional theory data. Our results provide compelling evidence that the H* and CH3* can spill over on the metal surface at experimental temperatures and reveal novel dynamical features involving an internal motion during diffusion for CH3*. Increasing surface temperature has a minor effect on promoting spillover, as geminate recombinative desorption becomes more prevalent. However, the poisoning of the active site can be mitigated by the frequent gaseous molecular collisions that occur under ambient pressure in real‐world catalysis, which transfer energy to the trapped adsorbates. Interestingly, the bulky CH3* exhibits a significant spillover advantage over the light H* due to its larger size, which facilitates energy acquisition. These insights help to advance our understanding of spillover in heterogeneous catalysis.

Deciphering the Factors Controlling Hydrogen and Methyl Spillover upon Methane Dissociation on Rh/Cu(111) Single-Atom Alloy.

Spillover of adsorbed species from one active site to another is a key step in heterogeneous catalysis. However, the factors controlling this step, particularly the spillover of polyatomic species, have rarely been studied. Herein, we investigate the spillover dynamics of H* and CH3* species on a single-atom alloy surface (Rh/Cu(111)) upon the dissociative chemisorption of methane (CH4), using molecular dynamics that considers both surface phonons and electron-hole pairs. These dynamical calculations are made possible by a high-dimensional potential energy surface machine learned from density functional theory data. Our results provide compelling evidence that the H* and CH3* can spill over on the metal surface at experimental temperatures and reveal novel dynamical features involving an internal motion during diffusion for CH3*. Increasing surface temperature has a minor effect on promoting spillover, as geminate recombinative desorption becomes more prevalent. However, the poisoning of the active site can be mitigated by the frequent gaseous molecular collisions that occur under ambient pressure in real-world catalysis, which transfer energy to the trapped adsorbates. Interestingly, the bulky CH3* exhibits a significant spillover advantage over the light H* due to its larger size, which facilitates energy acquisition. These insights help to advance our understanding of spillover in heterogeneous catalysis.

Enhancing hydrochar production and proprieties from biogenic waste: Merging response surface methodology and machine learning for organic pollutant remediation

The valorization of biogenic waste by hydrothermal carbonization is widely discussed in research. However, to our knowledge, no study has combined almond shells and olive pomace to synthesize a solid carbon material. The purpose of this study is to enhance the hydrochar process from AS and OP using RSM methodology and machine learning models: ANN, SVM and XG-Boost. Subsequently, a study was carried out on the removal of organic pollutants by the synthesized material. The optimum Co-HTC operating conditions obtained at 180 C, 90 min with acid catalyst corresponding to 71.51 % and 87.13 % for mass yield and carbon retention rate respectively according to RSM-CCD. The comparison between RSM-CCD and ML in terms of prediction concludes that RSM remains more efficient in terms of planning and optimization. However, ANN is more suitable for modeling and predicting mass yields and carbon retention rates. Hydrochar’s physicochemical properties were evaluated by the use of spectroscopic methods like FTIR, SEM, XRD, and CHNO. To conclude, we studied the performance of HCop in methylene blue adsorption, varying the following parameters: pH, contact time, initial dye concentration, adsorbent dose and temperature. In addition, kinetic and isothermal models were studied to describe the dominant mechanisms in the MB adsorption process. The MB maximal adsorption using HCop obtained at pH 9, with an initial dye concentration of 100 mg. L−1, 40-min contact time, 0.1 g/L adsorbent dose, and a temperature between 25 and 30 °C. In conclusion, these results provide important information on the use of Co-HTC to convert biogenic wastes into high-performance carbon materials for the appropriate removal of organic pollutants. More studies are needed to use the material in other fields of application.

Investigation of the milled surface quality of SiCp/Al composite materials with a moderate SiCp volume fraction

Silicon carbide particle–reinforced aluminum matrix (SiCp/Al) composite materials are challenging to machine, leading to significant surface quality issues arising during milling. In this study, a single-factor experiment was conducted using carbide-coated tools to mill SiCp/Al composite materials with a 45% volume fraction of silicon carbide particles (SiCps). Scanning electron microscopy, roughness measuring instruments, and a three-dimensional optical profiler were employed to investigate the formation mechanisms of the machined surface morphologies, as well as the effects of milling parameters (milling speed, feed per revolution, and milling depth) on the surface micro-morphology of the SiCp/Al composite materials. This study assessed the suitability of various roughness characterization parameters for evaluating the milled surface of SiCp/Al composite materials, including the two-dimensional profile arithmetic mean deviation (Ra), profile root mean square deviation (Rq), and microscopic surface roughness 10-point height (Rz), as well as the three-dimensional arithmetic mean deviation of the surface profile (Sa), and root mean square deviation of the surface profile (Sq) roughness characterization parameters to evaluate the milled surface of the SiCp/Al composite materials. The findings suggest that the three-dimensional roughness characterization parameters more accurately reflected the changes in the surface roughness than the two-dimensional ones. When the milling speed increased from 0.05 to 1.2 mm/rev, Sa first decreased from 0.514 to 0.498 µm and then increased to 0.537 µm, while Sq first decreased from 0.637 to 0.616 µm and then increased to 0.658 µm. Ra, Rz, and Rq all increased with higher values of feed per revolution and milling depth.