Machined Surface Research Articles

Abrasive aqua jet machining of NiTiV ternary smart alloy

ABSTRACT Nickel–titanium shape memory alloys (SMAs) are popular due to their usefulness and functional characteristics. The binary NiTi SMA was alloyed with vanadium to improve memory retention properties. In this study, the abrasive aqua jet machining method was adopted to machine the Ni50Ti48V2 ternary SMA to minimize the kerf taper angle (KTA), surface roughness (Ra) of machined Ni50Ti48V2 surface, and enhance the material removal rate (MRR). RSM-BBD optimization using the input variables such as abrasive flow rate (AFR), water jet pressure (PWJ), nozzle traverse speed (VTS), and standoff distance (SFD) predicted that PWJ and SFD highly contributed to MRR, Ra, and KTA responses. Small variation in variation after optimized AWJM conditions, and XRD analysis confirmed no phase changes after AWJM.

Grain Refinement-Induced Machined Surface Quality Promotion in Ultrasonic Vibration-Assisted Polycrystalline Diamond Cutting of Polycrystalline Cu

Grain Refinement-Induced Machined Surface Quality Promotion in Ultrasonic Vibration-Assisted Polycrystalline Diamond Cutting of Polycrystalline Cu

Multi-scale texture filtering-based quantitative machining mechanism characterisation for ultra-precision machined surfaces

Ultra-precision machined surfaces with nanometric surface texture have realised widespread applications. However, it is still challenging to quantitatively characterise the complex generation mechanisms due to the coupling of the machining factors. To address this issue, this work developed a multi-scale texture filtering-based method for ultra-precision machined surfaces. First, experiments were conducted including diamond turning, diamond milling, and ultra-precision grinding. Next, power spectrum density was utilised to reveal the multi-scale characteristics of surface texture generation. Then, the discrete wavelet transform was proposed for multi-scale texture filtering. Finally, root mean square height Sq was evaluated on S-F surfaces according to ISO 25178-2 and the machining mechanisms were quantitatively characterised by the contribution percentages of each scale. The results demonstrated that surface texture generation was dominated by multi-scale machining mechanisms to manifest as cutting residual, tool mark, material effect, spindle vibration, and ductile-brittle transition. Surface texture characterisation quantitatively indicated that small-scale features (cutting residuals and material effects) are crucial factors at low material removal volume (MRV). Due to the increased MRV, there is a transition since the middle/large-scale features (tool marks, spindle vibration, and ductile-brittle transition) gradually play their important role. This work provides novel thoughts to quantify machining mechanisms by multi-scale surface texture metrology for ultra-precision machining.

Surface integrity and mechanical properties of small elements fabricated through LPBF and post-processed with heat treatment and abrasive machining

AbstractThe present research analyzes the impact of heat treatment atmosphere followed by finishing surface machining of small elements of Inconel 939 fabricated through laser powder bed fusion (LPBF). The analysis involved annealing in two gas mediums, solution treatment, and aging to achieve the desired microstructure and mechanical properties. The finishing surface was performed using various variants of abrasive machining. A more than fivefold reduction in the average roughness height Ra from 5.6 µm to 1.15 µm was achieved using metal balls as an abrasive, which was required for further processing. Residual stress tests have shown that due to heat and abrasive treatment, tensile stresses change into compressive ones. After printing, samples are characterized by tensile residual stresses on the surface (+ 428 MPa), while after heat treatment, compressive stresses occur (− 179 MPa). Abrasive machining with metal balls increases the value of compressive stresses to − 464 MPa. In addition, the impact of post-processing on the microstructure of Inconel 939 was discussed in terms of mechanical properties. The yield strength of 1184 MPa and elongation values of 19.3% were obtained for samples after HT in an argon atmosphere and abrasive machining with a ceramics roller. These studies provide valuable new information on the effective heat treatment and optimization of the finishing machining of Inconel 939, especially in achieving the desired surface roughness, microstructure, and mechanical properties for aerospace applications.

Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes

Generative models, particularly diffusion-based approaches, have gained significant attention recently due to their ability to create realistic outputs. Despite their potential, the application of these models in manufacturing remains largely unexplored. This work presents a framework that addresses this gap by generating machined surface images guided by multiple sensor inputs in manufacturing. The proposed model integrates information from multiple sensors with varying sampling rates using multimodal embedding and employs a latent diffusion model to translate the fused sensor embedding into an image embedding, which is then converted into a machined surface image. The effectiveness of the framework is validated using real-world time-series data, including force, torque, acceleration, and sound, collected from various industrial processes, such as a carbon-fiber-reinforced plastic drilling process. The results demonstrate the model’s ability to predict defects from the generated machined surface images. The proposed approach can potentially revolutionize prognostics and health management (PHM) in smart manufacturing by enabling sensor-guided visual inspection, defect detection, process monitoring, and predictive maintenance.

Investigation of Electrochemical Discharge Machining for Tungsten Carbide: Effects of Electrolyte Composition on Material Removal Rate and Surface Quality

Abstract Tungsten carbide (WC-Co) with a cobalt binder has been widely used in industrial application. Through their high wear resistance and hardness, which make it a challenge to machine. Electrochemical discharge machining (ECDM) is a newly developed hybrid technique used to machine conductive and nonconductive materials. Tungsten carbide machining is an area that needs more investigation. In this study, different types of electrolytes have been tested in the electrochemical machining of tungsten carbide. It has been concluded that tungsten carbide was successfully machined with electrolytes that were either neutral salts or a combination of neutral salts and hydroxides, the highest material removal rate achieved was (0.09250 g/min), and the average surface roughness achieved in this work was measured at (Ra 0.9275 µm). However, deposition took place on the surface of machined tungsten carbide when the samples were treated with sodium hydroxide and potassium hydroxide. EDX analysis of successfully machined tungsten carbide samples reveal the presence of carbon (C) due to diffusion from the base material and oxygen (O), most likely due to oxidation brought on by the high temperatures utilized. Scanning electron microscopy confirmed that the machined surfaces had craters, pores, restricted microcracks, and re-deposited melt particles, among other things.

Broaching Digital Twin to Predict Forces, Local Overloads, and Surface Topography Irregularities

Broaching is a key manufacturing process that directly influences the surface integrity of critical components, impacting their functional performance in sectors such as aeronautics, automotive, and energy. Such components are subjected to severe conditions, including high thermomechanical loads, fatigue, and corrosion. For this reason, the development of predictive models is essential for determining the optimal tool design and machining conditions to ensure proper in-service performance. This study, therefore, presents a broaching digital twin based on hybrid modelling, which combines analytical, numerical, and empirical approaches to provide rapid and accurate predictions of the forces per tooth, local overloads, and surface topography irregularities. The digital twin was validated with a critical industrial case study involving fir-tree broaching of turbine discs made of forged and age-hardened Inconel 718. The accuracy of the digital twin was demonstrated by the results: the average error in force predictions was below 10%, and the model effectively identified the most critical teeth and zones prone to failure. It also predicted surface topography irregularities with an error of less than 15%. Interestingly, the relationship between surface topography irregularities and surface residual stress variations across the machined surface was observed experimentally for the first time.

A Novel Cooling System for High-Speed Axial-Flux Machines Using Soft Magnetic Composites

Demand is high for small, lightweight, and power-dense machines. However, as power increases and size decreases, rejecting losses becomes more difficult. Many novel cooling systems have been developed, which have allowed machines to be made smaller while increasing power. This paper proposes a cooling system making use of soft magnetic composite (SMC) cores to improve cooling specifically in a high-speed axial-flux machine via the use of an integrated cooling channel in the SMC core. A series of experiments on a prototype machine are performed and the experimental data are used to determine a set of parameters for the FEA thermal model. Using the thermal FEA model, a comparison is completed with a traditional closed cooling system using laminated steels and an attached cooling plate.The SMC machine is then simulated at speeds up to 160 krpm and currents up to 8 A. To achieve the same coil temperature between the two designs, the laminated steel model required 4 MPa contact pressure at 10 krpm and 5 MPa contact pressure at 20 krpm. At the same time, the novel design removed approximately 20% more heat per shear air gap surface area and approximately 15% more heat per total machine surface area than the version with the attached cooling plate. Extending the operating range of the model to 160 krpm demonstrated that the maximum temperature rise remained below 180 °C.

Characteristics of a novel elliptical ultrasonic vibration-assisted turning device

ABSTRACT 2D Ultrasonic Vibration-Assisted Turning (2D-UVAT) is a proven method to improve the traditional turning process. This process utilises bi-axial vibrations to enhance the smoothness of the machined surface and to suppress cutting temperature and cutting force. This study investigates the effects of employing a novel non-resonant vibration module, called the RNO vibrator, on the surface roughness, cutting temperature, and cutting force of aluminium alloy 6061-T6 machined using 2D-UVAT technique. The RNO vibrator operates by harnessing flexural hinges to disseminate and amplify the actuated vibrations originating from two orthogonal piezo actuator stacks. This vibrator was designed from a simple bar concept, expecting minimal adjustment on the tool post during installation. The experimental evaluation of the RNO vibrator’s performance was conducted by varying key machining parameters, including spindle speed, depth of cut, feed rate, and vibrational frequency. The study reveals that the introduced RNO vibrator substantially enhances machining quality by diminishing surface roughness and cutting temperature, while concurrently reducing cutting forces. Under the given conditions, the machined workpieces had a surface roughness between 0.41 and 1.25 µm and a cutting temperature ranging from 153.36°C to 180.5°C, while the cutting force was low, typically between 35.3 N and 145.5 N. The vibrator works better at high frequency, particularly at 20 kHz. Eventually, the study suggests that the RNO vibrator has a promising performance as a vibration module for the 2D-UVAT process.

Comparative Evaluation of Bone–Implant Contact in Various Surface-Treated Dental Implants Using High-Resolution Micro-CT in Rabbits’ Bone

This study evaluated the bone-to-implant contact (BIC) of various surface-treated dental implants using high-resolution micro-CT in rabbit bone, focusing on the effects of different treatments on osseointegration and implant stability before and after bone demineralization. Six male New Zealand White rabbits were used. Four implant types were tested: machined surface with anodizing, only etching, sandblasting with Al2O3 + etching, and sandblasting with TiO2 + etching. Implants were scanned with high-resolution micro-CT before and after demineralization. Parameters like implant volume, surface area, and BIC were determined using specific software tools. During demineralization, the BIC changed about 6% for machined surface with anodizing, 5% for only etching, 4% for sandblasting with Al2O3 + etching, and 10% for sandblasting with TiO2 + etching. Demineralization reduced BIC percentages, notably in the machined surface with anodizing and sandblasting with TiO2 + etching groups. Etching and sandblasting combined with etching showed higher initial BIC compared to anodizing alone. Demineralization negatively impacted the BIC across all treatments. This study underscores the importance of surface modification in implant integration, especially in compromised bone. Further research with larger sample sizes and advanced techniques is recommended.

Mathematical Modeling and Generating Method of Hourglass Worm Gear Hob’s Rake Face Based on a Rotating Paraboloid Surface

The rake angles on both sides of the cutting edges of the hourglass worm gear hob significantly influence its cutting performance, which, in turn, plays a decisive role in the surface quality of the machined worm wheel. To balance the rake angles along the tooth height direction of each hob tooth and enhance the overall cutting performance of the hob, this paper proposes a method that utilizes a rotating paraboloid surface to generate the helical rake face of the hourglass worm gear hob. First, the conjugate condition equations for the rake face generated by the rotating paraboloid surface are derived. A mathematical model for the helical rake face of planar double-enveloping hourglass worm gear hob is established. This study explores the influence of two machining parameters on the rake angle, specifically the milling drive ratio coefficient k and the geometric parameter of a parabolic milling cutter p. Through a systematic analysis of the variations in rake angle at the dividing toroidal surface and along the tooth height direction, the optimal parameter values were identified as k = 0.9115 and p = 0.6834. The results show that, after optimization, the hob rake angle range is around ±4.7°, with a maximum rake angle difference of 6.3072° along the tooth height direction, and the rake angles on both sides of the teeth are more balanced. The structure of the rake face is more reasonable, reflecting the feasibility of rotating paraboloid tools for forming tools in the machining of complex surfaces.

Electrochemical jet machining in deep-small holes with gas assistance: generating complex features on internal surfaces

Deep-small holes with internal features have important applications in thermal engineering but pose significant difficulties for traditional machining methods. Electrochemical jet machining (EJM) is an effective surface micromachining technique with numerous merits. Applying EJM to create complicated features on the internal surface of deep-small holes is attractive. However, this concept remains challenging due to the narrow and enclosed processing space. In this work, a novel gas assistance tool is developed to achieve the EJM process in deep-small holes for the first time. The hydrodynamic conditions to realize well-shaped and unsubmerged jets in both open space and deep-small holes using the specifically designed tool are investigated. The appropriate electrolyte flow rate and sidewall orifice dimensions enable the desired jet to be ejected laterally from the tubular cathode sidewall orifice. While in the deep-small hole the assist gas creates a local gas cavity around the orifice to prevent the jet from submerging, forming the jet shape required for EJM and consequently achieving localized machining of the internal surface. Excessive assist gas pressure should be avoided as it causes the jet to incline and deform, resulting in reduced machining accuracy. Furthermore, the influence of the main parameters on machining performance is examined. The developed gas-assisted EJM method demonstrates similar machining characteristics to the conventional EJM process when appropriate gas assistance conditions can produce the well-shaped unsubmerged jet. As such, various features with smooth surfaces and good shape accuracy are successfully machined on the internal surface of deep-small holes.

Optimisation of parameters of a dual-axis soil remediation device based on response surface methodology and machine learning algorithm

To accelerate the recycling of black soil, it is necessary to develop a new type of soil remediation equipment to improve its working efficiency. The one-way test was used to determine the mean level value of the steepest climb test, and the combined equilibrium method was used to determine the upper and lower interval levels of the response surface test for parameter optimisation. Based on the results of the response surface indices, machine learning was performed and the optimal model was determined. The results show that the predictive ability and stability of the decision tree model for the two indicators are better than that of random forest and support vector machine. The optimal parameter combinations determined using the decision tree model are: speed 73 rpm, homogenisation pitch 183 mm, homogenisation time 1 s, descent speed 0.06 m/s. The error between the optimal value of the machine learning prediction model and the actual simulation is 1.1% and 5.72%, respectively. The results of the study show that the effect of optimising the parameters through machine learning achieves a satisfactory prediction accuracy.

Ultrasonic-assisted ultra-precision turning of zinc-selenide with straight-nosed diamond tools

This study proposes a novel ultra-precision machining technology that uses ultrasonic vibration and a straight-nosed diamond tool to improve the processing of the brittle optical material zinc selenide (ZnSe). This research addresses the challenges posed by Poisson's effect in ultrasonic vibration-assisted single-point diamond turning, which causes bending vibration along the depth of cut, resulting in lower machining efficiency and surface quality. This study analyses the relationship between one-dimensional ultrasonic vibrations at the diamond tool edge and induced bending vibrations using both theoretical and experimental methods. By investigating ultrasonic vibration dynamics in the feed direction and at the straight cutting edge, the results showed that ultrasonic vibration helps to improve the ductile-brittle transition ratio of the cutting area and surface quality. These improvements are accomplished by regulating the cutting position at the tool cutting edge, adjusting cutting parameters, and optimizing ultrasonic parameters. The machined surface roughness of ZnSe is reduced by approximately 30-46% at higher feed rates under ultrasonic vibration with straight-nosed diamond tools. The findings demonstrate the potential of this novel technology to reduce tool wear and brittle fractures, resolving the challenge of ultra-precision machining for optical materials.

Characterization of micro-wire electrical discharge machining surface texture by empirical mode decomposition

Surface roughness is a critical factor for the operational efficacy and lifespan of the manufactured components, often serving as a key metric for evaluating manufacturing processes and determining acceptance of a component. Traditional surface roughness measurements are highly sensitive to the fixed standard cut-off length, influencing the scale at which surface texture features are analyzed. Thus, developing techniques with more reliable filtering of surface features is essential for accurate surface metrology. This research proposes a novel method that combines Empirical Mode Decomposition (EMD) and Fast Fourier Transform (FFT) for surface roughness characterization. We applied our method to Nitinol shape memory alloy (equal atomic proportions of nickel and titanium) surfaces processed using micro-Wire Electrical Discharge Machining (μ-WEDM). Our results demonstrate that the EMD-FFT method significantly outperforms traditional filtering, especially for surfaces without distinct patterns like μ-WEDM. This improvement is particularly valuable for materials like Nitinol, which is widely used in critical applications where precise surface roughness control is essential. By accurately assessing the effects of μ-WEDM parameters on surface roughness, the proposed method reveals that for a constant pulse on time and a constant discharge current, the variation of pulse off time is more important than servo voltage. Thus, this method can contribute to optimizing manufacturing processes and producing higher-quality components with improved performance and durability.

Machining error compensation of free-form surfaces by combination of ordinary kriging method and mirror compensation method

ABSTRACT Improving the accuracy and efficiency of free-form surface machining is a common goal of modern manufacturing. In this study, a compensation method combining the ordinary kriging method based on sequential quadratic programming method optimization (SQP-OK) and the mirror compensation method for machining errors of free-form workpieces was applied, with the aim of significantly improving the workpiece machining accuracy. Machining errors on the surface of the workpiece were predicted utilizing the SQP-OK after the workpieces were completed with semi-finishing machining based on the on-machine measurement (OMM) results of the workpiece. Based on the prediction result, the mirror compensation method was applied to calculate the compensation points. Free-form workpieces were compensated for by modifying the NC code for semi-finishing. To verify the effectiveness of the proposed method, two free-form workpieces were machined with computer numerical control (CNC) compensation, applying the proposed method and the method modifying the overall machining allowance. The experimental results revealed that the compensation method proposed in this paper reduced the average value of the machining errors decreased by 28.6% in absolute value, 31.1% in maximum undercut, and 28.8% in maximum overcut for the free-form workpiece, respectively, compared with the compensation method of modifying the overall machining allowance.

Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining

Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining

Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques

Modelling the Solar Intensity of Asaba Town in Nigeria Using Response Surface Methodology and Machine Learning Techniques

Optimizing the surface quality of electrochemical machined Ni-based single crystal superalloy via manipulation of phase and dendrite dissolution

The transpassive dissolution behaviors of Ni-based single crystal superalloy in 10 %NaCl, 10 %NaNO3, and mixed solution are investigated by using potentiodynamic polarization, scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. The alloy exhibits opposite yet complementary dissolution characteristics in NaCl and NaNO3 solutions. The different responses of γ/γ′ phases to Cl− and NO3− contribute to the opposite dissolution behaviors of dendrites and interdendritic regions. The surface flatness is noticeably enhanced in mixed solution due to the synchronous dissolution of the γ/γ′ phases. Our work paves the way for obtaining high-quality surface in electrochemical machining of single crystal superalloys.

Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection.

Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids.