Search Results (538)

The excellent mechanical properties of AISI 201 make it well-suited for applications in industrial components, transportation, and household appliances. However, during machining, this material generates high cutting forces and temperatures, leading to rapid tool wear and high costs. To address this issue, micro-grooves were designed on the rake face in areas prone to thermal and mechanical stress concentration. Through machining experiments focusing on workpiece surface quality, it was found that micro-grooved tools produced superior surface quality, specifically manifested in lower surface roughness, reduced work hardening, and shallower hardened layer depth. Experiments demonstrate that under identical conditions, increasing the cutting speed with tool M reduces the workpiece surface roughness by 0.096 μm to 0.236 μm compared to tool O. Under identical conditions, increasing the feed rate with tool M reduces the workpiece surface roughness by 0.070 μm to 0.236 μm compared to tool O. As cutting speed varies, the absolute surface hardness of workpieces machined by tool M decreases by approximately 39.85 HV, representing a hardness reduction of 14.5%. As feed rate varies, the surface hardness of workpieces machined with tool M is suppressed to a level 10.2%–14.2% lower than that of tool O. As cutting depth varies, the surface hardness of workpieces machined with tool M is suppressed to a level 10.0%–14.7% lower than that of tool O. Additionally, micro-grooved tools demonstrated superior chip curling and breaking performance. This tool design approach, optimized for tool durability and workpiece surface quality, establishes a research foundation for tool design targeting difficult-to-machine materials. Full article

Magnesium alloys, like AZ31, possess a desirable low weight and high specific strength, which make them favorable for aerospace and auto applications, yet their difficulty to machine limits their broader implementation for the industry. Electrical discharge machining (EDM) is an effective technology for machining difficult-to-machine materials, particularly when the materials are reinforced with ceramic and graphene-based fillers. This study examines the impact of reinforcement percentage (R) and different electrical discharge machining (EDM) parameters such as current (I), pulse on time (T_on) and pulse off time (T_off) on the material removal rate (MRR) and surface roughness (SR) of AZ31/B₄C/GNPs composites. The combined reinforcement range varies from 2 wt.% to 4 wt.%. The Taguchi design (L27) is utilized to conduct the experiments in this study. ANOVA of the experimental data indicated that current (I) significantly affects MRR and SR, exhibiting the greatest contribution of 44.93% and 51.39% on MRR and SR, respectively, among the variables analyzed. The surface integrity properties of EDMed surfaces are examined using SEM under both higher and lower material removal rate settings. Diverse machine learning techniques, including linear regression (LR), polynomial regression (PR), Random Forest (RF), and Gradient Boost Regression (GBR), are employed to construct an efficient predictive model for outcome estimation. The built models are trained and evaluated using 80% and 20% of the total data points, respectively. Statistical measures (MSE, RMSE, and R²) are utilized to evaluate the performance of the models. Among all the developed models, GBR exhibited superior performance in predicting MRR and SR, achieving high accuracy (exceeding 92%) and lower error rates compared to the other models evaluated in this work. This work demonstrated the synergy between techniques in optimizing EDM performance for hybrid composites using a statistical design and machine learning strategies that will facilitate greater use of hybrid composites in high-precision engineering applications and advanced manufacturing sectors. Full article

Shape memory alloy (SMA) materials are valued for their shape memory effect, superelasticity, and biocompatibility, making them an ideal choice for applications in biomedical, aerospace, and actuator fields. Nickel–titanium (NiTi) SMA is a promising biomedical material. It is widely used in the manufacture of biomedical instruments, devices, implants, and surgical tools. However, its complex thermo-mechanical behavior and poor machinability pose challenges for conventional machining. To manufacture high-quality nitinol parts, traditional machining processes are being replaced by advanced machining technologies. Electric discharge machining (EDM) is an advanced machining technique whose mechanism of material removal involves erosion caused by plasma formation and spark generation. It has proven effective for processing difficult-to-machine materials. This review summarizes EDM and its variants, including hybrid EDM, with a focus on machining NiTi-SMA materials for biomedical, aerospace, microelectromechanical systems, and automotive applications, and systematically explores key factors such as process parameters, material removal mechanisms, surface integrity, tool wear, and optimization strategies. This review begins with an introduction to nitinol (i.e., NiTi-SMA) and its variants, followed by an in-depth discussion of plasma formation, spark generation mechanisms, and other key aspects of EDM. It then provides a detailed analysis of notable past research on the machining of NiTi SMA materials using EDM and its variants. This paper concludes with insights into future research directions, aiming to advance EDM-based machining of SMA materials and serve as a valuable resource for researchers and engineers in the field. Full article

Electric discharge machining processes are prominent in the fastest-growing industries because of their accuracy, achievable complex workpiece shapes, and cost-effectiveness. Furthermore, the machining of high-quality difficult-to-machine alloys is becoming critical in the aerospace, manufacturing, and defence industries. While the optimisation of EDM parameters is essential for improving machining outcomes, it is also important to consider the trade-offs between different performances metrics, such as material removal rate and part accuracy. Part accuracy in terms of dimensional and geometric deviations from nominal values was rarely considered in the literature, if not by the authors. Balancing these factors remains a challenge in the field of EDM. Therefore, this work aims to carry out a multi-objective optimisation of both MRR and part accuracy. A Ni-based alloy (Inconel-625) was used that is widely used in creep-resistant turbine blades and vanes and turbine disks in gas turbine engines for aerospace and defence industries. Four performance indices were optimised simultaneously: two related to the performance of the EDM process and two connected with the form deviations of the manufactured surfaces. Multi-hole copper electrodes having different diameters and three process parameters were varied during the experimental tests. Grey relational analysis and the Adaptive Neuro-Fuzzy Inference System method were used for optimisation. Grey relational analysis found that the following values of the process parameter—0.16 mm of multi-hole electrode diameter, 12 Amperes of Peak current, 200 µs of pulse on time and 0.2 kg/m² as dielectric pressure—produce the optimal performance, i.e., a material removal rate of 0.099 mm³/min, an electrode wear rate of 0.0002 g/min, a circularity deviation of 0.0043 mm and a cylindricity deviation of 0.027 mm. From the experimental examination using multi-hole electrodes, it is concluded that the material removal rate increases and the electrode wear rate decreases because of the availability of higher spark discharge areas between the electrode and work material interface. The Adaptive Neuro-Fuzzy Inference System models showed minimum mean percentage error and, therefore, better performance in comparison with regression models. Full article

Deep hole structures are widely used in the fields of aerospace, engineering machinery, marine, etc. During the deep hole machining processes, especially for boring procedures, the vibration phenomenon caused by the large aspect ratio of boring tools seriously restricts the machining accuracy and production efficiency. Therefore, extensive research has been devoted to the design and development of damped boring tools with different structures to suppress machining vibration. According to varied vibration reduction technologies, the damped boring tools can be divided into active and passive categories. This paper systematically reviews the advancements of vibration reduction principles, structure design, and practical applications of typical active and passive damped boring tools. Active damped boring tools rely on the synergistic action of sensors, actuators, and control systems, which can monitor vibration signals in real-time during the machining process and achieve dynamic vibration suppression through feedback adjustment. Their advantages include strong adaptability and wide adjustment capability for different machining conditions, including precision machining scenarios. Comparatively, vibration-absorbing units, such as mass dampers and viscoelastic materials, are integrated into the boring bars for passive damped tools, while an energy dissipation mechanism is utilized with the aid of boring tool structures to suppress vibration. Their advantages include simple structure, low manufacturing cost, and independence from an external energy supply. Furthermore, the potential development directions of vibration damped boring bars are discussed. With the development of intelligent manufacturing technologies, the multifunctional integration of damped boring tools has become a research hotspot. Future research will focus more on the development of an intelligent boring tool system to further improve the processing efficiency of deep hole structures with difficult-to-machine materials. Full article

Microtextured cutting tools are widely used because of their excellent performance in cutting difficult-to-machine materials. The cutting performance of cutting tools largely depends on the size parameters of the microtextures used. This study focuses on the machining of titanium alloy Ti-6Al4 V using microgrooved cutting tools under dry-cutting conditions. Special emphasis is placed on exploring cutting performance under specific combinations of microgroove parameters. To determine the optimal parameter combination for cutting, the effects of different microgroove parameters (including the diameter, depth, spacing, and spacing between grooves and cutting edges) on cutting force, tool wear, and chip morphology were investigated. In this study, femtosecond laser technology was used to prepare microgroove-textured cutting tools with different parameters, and the cutting performance of these tools was analyzed. The results show that, when the groove diameter is 80 μm, the depth is 60 μm, the spacing is 80 μm, and the distance between the groove and the tool tip is 120 μm, the cutting performance of the tool is optimal: the cutting force is reduced by 13.9%, the degree of tool wear is minimized, and the degree of chip curling is more uniform. The research results can be applied to the actual processing of Ti-6Al4 V, which can help tool design, selection, and microtexture parameter optimization. Full article

Refractory high-entropy alloys (RHEAs) are categorized as difficult-to-machine materials due to their excellent mechanical properties. Electrical discharge machining (EDM) is a special processing method for RHEAs, which faces challenges such as low machining efficiency. In this work, electrochemical discharge machining (ECDM) was proposed for (TiVZrTaW)_99.5N_0.5 and (TiVZrTa)W₅ (at. %, denoted as W20N0.5 and W5, respectively) RHEAs, and their machining performances were investigated and compared with EDM. At a peak current of 25 A, the material removal rate (MRR) using ECDM is more than twice that of EDM for W20N0.5 (reaching to 1.24 mm³/min) and 1.5 times higher than that for W5. Both W20N0.5 and W5 RHEAs exhibited higher MRR in ECDM based on the analyses of the influence of top diameter, bottom diameter, machining depth, and surface roughness (Ra). The process and mechanisms of material removal were examined through the microstructural morphology and elemental distribution analyses. This work proposed a more effective route for machining RHEAs by ECDM compared to the conventional EDM. Full article

With the rapid development of advanced machining technologies such as high-speed cutting, dry cutting, and ultra-precision cutting, as well as the widespread application of various difficult-to-machine materials, the surface degradation problems such as wear, oxidation, and delamination faced by tools in the service process have become increasingly prominent, seriously restricting the performance and service life of tools. Nanocoatings, with their distinct nano-effects, provide superior hardness, thermal stability, and tribological properties, making them an effective solution for cutting tools in increasingly demanding working environments. For example, the hardness of the CrAlN/TiSiN nano-multilayer coating can reach 41.59 GPa, which is much higher than that of a single CrAlN coating (34.5–35.8 GPa). This paper summarizes the most common nanocoating material design, coating deposition technologies, performance evaluation indicators, and characterization methods currently used in cutting tools. It also discusses how to improve nanocoating performance using modulation analysis of element content, coating composition, geometric structure, and coating thickness. Finally, this paper considers the future development of nanocoatings for cutting tools in light of recent research hotspots. Full article

To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it is crucial to first detect and characterize it in terms of spatial configuration and geometry. Currently, the technologies available on the market that do not require an operator at the stope are only applicable in specific mine layouts or use 2D camera images of the surroundings that can be observed from a control room for teleoperation. However, due to missing depth information, estimating distances is difficult. This work presents a novel approach to muck pile detection developed as part of the EU-funded Next Generation Carbon Neutral Pilots for Smart Intelligent Mining Systems (NEXGEN SIMS) project. It uses a stereo camera mounted on an LHD to gather three-dimensional data of the surroundings. By applying a topological algorithm, a muck pile can be located and its overall shape determined. This system can detect and segment muck piles while driving towards them at full speed. The detected position and shape of the muck pile can then be used to determine an optimal attack point for the machine. This sensor solution was then integrated into a complete system for autonomous loading with an LHD. In two different underground mines, it was tested and demonstrated that the machines were able to reliably load material without human intervention. Full article

Boron (B) is widely used as a neutron-absorbing nuclide and has significant applications in the nuclear industry. B₄C/Al composites combine the high hardness of B₄C with the ductility of Al, making them commonly used neutron-absorbing materials. Under current preparation methods, the poor wettability and low reactivity of B₄C with molten Al limit its effective incorporation into the matrix, and the addition of B₄C in B₄C/Al composites has reached its threshold limit, making it difficult to achieve breakthrough improvements in neutron absorption performance. However, incorporating additional B elements into the B₄C/Al composite can break this limit, effectively enhancing the material’s neutron absorption performance. Nevertheless, research on the impact of this addition on the mechanical properties of the composite remains unclear. The requirements for B₄C/Al composites as spent fuel storage and transportation devices include high mechanical strength and certain machinability. This study fabricated B₄C/Al composites with varying B contents (5 wt.%, 10 wt.%, and 15 wt.%), and the influence of B addition on the microstructure and mechanical properties of B₄C/Al composites was investigated. The results demonstrate that the composites exhibit a density of approximately 99% with well-established interfacial bonds. Increasing B content leads to a higher quantity of interfacial reaction products Al₃BC and AlB₂, enhancing the Vickers hardness to 370.93 HV. The bending strength and fracture toughness of composites with 5 wt.% and 15 wt.% B addition decreased, whereas those with 10 wt.% B exhibited excellent resistance to crack growth and high-temperature plastic deformation due to a high content of ductile phase. Full article

Titanium alloys (Ti-6Al-4V) are widely used in the aerospace field. However, as a typical difficult-to-machine material, titanium alloys have a low thermal conductivity, a high chemical activity, and a significant adiabatic shear effect. In conventional milling (CM), the temperature in the cutting zone rises sharply, leading to tool adhesion, rapid wear, and damage to the workpiece surface. This article systematically investigated the influence of process parameters on the surface roughness, cutting force, and cutting temperature in the ultrasonic-vibration-assisted milling (UAM) process of titanium alloys, based on which multi-objective optimization process of the milling process parameters was conducted, by utilizing the grey relational analysis method. An orthogonal experiment with four factors and four levels was conducted. The effects of various process parameters on the surface roughness, cutting force, and cutting temperature were systematically analyzed for both UAM and CM. The grey relational analysis method was employed to transform the optimization problem of multiple process target parameters into a single-objective grey relational degree optimization problem. The optimized parameter combination was as follows: an ultrasonic amplitude of 6 μm, a spindle speed of 6000 rpm, a cutting depth of 0.20 mm, and a feed rate of 200 mm/min. The experimental results indicated that the surface roughness Sa was 0.268 μm, the cutting temperature was 255.39 °C, the cutting force in the X direction (FX) was 5.2 N, the cutting force in the Y direction (FY) was 7.9 N, and the cutting force in the Z direction (FZ) was 6.4 N. The optimization scheme significantly improved the machining quality and reduced both the cutting forces and the cutting temperature. Full article

Objective: With a global prevalence ranging from 50% to 100%, gingivitis is considered the most common oral disease in adults worldwide. It is characterized by clinical signs of inflammation, such as redness, swelling and bleeding, on gentle probing. Although it is considered a milder form of periodontal disease, gingivitis plays an important role in overall oral health. Early detection and treatment are essential to prevent progression to more severe conditions. Typically, diagnosis is performed by dental professionals, as individuals are often unable to accurately assess whether they are affected. Therefore, the aim of the present study was to determine to what degree gingivitis is visually detectable by an easy-to-use camera-based application. Materials and methods: Standardized intraoral photographs were taken using a specialized intraoral camera and processed using a custom-developed filter based on a machine-learning algorithm. The latter was trained to highlight areas suggestive of gingivitis. A total of 110 participants were enrolled through ad hoc sampling, resulting in 320 assessable test sites. A dentist provided two reference standards: the clinical diagnosis based on bleeding on probing of the periodontal sulcus (BOP) and an independent visual assessment of the same images. Agreement between diagnostic methods was measured using Cohen’s kappa statistic. Results: The agreement between the application’s output and the BOP-based clinical diagnosis was low, with a kappa value of 0.055 [p = 0.010]. Similarly, the dentist’s visual assessment of clinical photos showed low agreement with BOP, with a kappa value of 0.087 [p < 0.001]. In contrast, the agreement between the application and the dentist’s photo-based evaluations was higher, with a kappa value of 0.280 [p < 0.001]. Conclusions: In its current form, the camera-based application is not able to reliably detect gingivitis. The low level of agreement between dentists’ visual assessments and the clinical gold standard highlights that gingivitis is difficult to identify merely visually. These results underscore the need to refine visual diagnostic approaches further, which could support future self-assessment or remote screening applications. Full article

Carbon fiber (CF) and carbon fiber-reinforced polymers (CFRPs) are widely used in the aerospace, automotive, and energy sectors due to their high strength, stiffness, and low density. However, significant waste is generated during manufacturing and after the use of CFRPs. Traditional disposal methods like landfilling and incineration are unsustainable. CFRP machining processes, such as drilling and milling, produce fine chips and dust that are difficult to recycle due to their heterogeneity and contamination. This study investigates the oxidation behavior of CFRP drilling waste from two types of materials (tube and plate) under oxidative (non-inert) conditions. Thermogravimetric analysis (TGA) was performed from 200 °C to 800 °C to assess weight loss related to polymer degradation and carbon fiber integrity. Scanning electron microscopy (SEM) was used to analyze morphological changes and fiber damage. The optimal range for removing the polymer matrix without significant fiber degradation has been identified as 500–600 °C. At temperatures above 700 °C, notable surface and internal fiber damage occurred, along with nanostructure formation, which may pose health and environmental risks. The results show that partial fiber recovery is possible under ambient conditions, and this must be considered regarding the harmful risks to the human body if submicron particles are inhaled. This research supports sustainable CFRP recycling and fire hazard mitigation. Full article

The deformation behavior of polymers is influenced not only by service conditions such as temperature and the strain rate but also significantly by the formation process. However, existing simulation frameworks typically treat injection molding and the in-service mechanical response separately, making it difficult to capture the impact of the thermal history on large deformation behavior. In this study, the deformation behavior of injection-molded polycarbonate (PC) was investigated by accounting for its thermal history during formation, achieved through combined experimental characterization and constitutive modeling. PC specimens were prepared via injection molding followed by annealing at different molding/annealing temperatures and durations. Uniaxial tensile tests were conducted using a Zwick universal testing machine at strain rates of 10⁻³–10⁻¹ s⁻¹ and temperatures ranging from 293 K to 353 K to obtain stress–strain curves. The effects of the strain rate, testing temperature, and annealing conditions were thoroughly examined. Building upon a previously proposed phenomenological model, a new constitutive framework incorporating thermal history effects during formation was developed to characterize the large deformation behavior of PC. This model was implemented in ABAQUS/Explicit using a user-defined material subroutine. Predicted stress–strain curves exhibit excellent agreement with the experimental data, accurately reproducing elastic behavior, yield phenomena, and strain-softening and strain-hardening stages. Full article