Search Results (60)

Corrosion is a complex, surface-initiated process that demands nanoscale, real-time characterization to understand its initiation and propagation. Atomic force microscopy (AFM) and scanning Kelvin probe force microscopy (SKPFM) have emerged as powerful tools in corrosion science, enabling high-resolution imaging and electrochemical mapping under realistic conditions. This review, inspired by pioneering work at KTH by Professors Christofer Leygraf and Jinshan Pan, highlights advanced analytical strategies that extend the capabilities of AFM and SKPFM beyond traditional line-profile analysis. Techniques such as power spectral density (PSD) analysis, multimodal Gaussian histogram fitting, statistical roughness quantification, and deconvolution methods are discussed in the context of case studies on aluminum alloys, stainless steels, magnesium alloys, biomedical implants, and protective coatings. By integrating in situ imaging, electrochemical mapping, and statistical data processing, these approaches provide deeper insights into localized corrosion, micro-galvanic coupling, and surface reactivity. Future directions include coupling AFM-based methods with high-speed imaging, machine learning, and spectro-electrochemical techniques to accelerate the development of corrosion-resistant materials and enable probabilistic diagnostics of corrosion initiation susceptibility. Full article

Titanium alloy Ti-6Al-4V possesses excellent mechanical and corrosion-resistant properties; therefore, it is widely employed in aerospace, automotive, and biomedical fields. However, its poor machinability restricts traditional processing methods. To overcome this limitation, the current work presents a symmetry analysis approach to evaluate the effects of tool coating on the micro-electric discharge machining (micro-EDM) characteristics of Ti-6Al-4V. Tungsten carbide (WC) microelectrodes were fabricated in three forms: uncoated, copper-coated, and carbon-coated. The chemical vapor deposition (CVD) method was used to coat the carbon layer, and the integrity of the coating was confirmed by Energy-Dispersive X-ray Spectroscopy/Analysis (EDS/EDX). The effect of input variables—namely, voltage, capacitance, and spindle rotational speed—on two responses was studied—the machining depth (Z-axis displacement) and tool wear rate (TWR)—using a Taguchi L9 orthogonal array. Analysis conducted using Minitab statistical software 17 revealed that both voltage and capacitance contributed to the response parameters as optimized variables. The comparative study showed that the copper- and carbon-coated WC microtool could obtain a better Z coordinate and lower tool wear ratio compared with those of the uncoated tool. The findings confirm that applying thin conductive coatings to WC tools can significantly improve the stability, precision, and overall symmetry of the micro-EDM process when machining difficult-to-cut titanium alloys. Full article

This article comprehensively reviews the tribological behavior of a Ti-6Al-4V alloy manufactured via electron beam powder bed fusion (EB-PBF), an additive manufacturing process for aerospace and biomedical applications. EB-PBF Ti-6Al-4V demonstrates wear resistance that is superior or comparable to conventional Ti-6Al-4V. The reported average friction coefficient ranges between ~0.22 and ~0.75 during sliding wear in dry and lubricated conditions against metallic and ceramic counterparts when loading 1–50 N under varied surface and heat treatment conditions, and between 1.29 and 2.2 during fretting wear against EB-PBF Ti-6Al-4V itself. The corresponding average specific wear rates show a broad range between ~8.20 × 10⁻⁵ mm³/Nm and ~1.30 × 10⁻³ mm³/Nm during sliding wear. Lubrication reduces the wear rates and/or the friction coefficient. Wear resistance can be improved via machining and heat treatment. Wear anisotropy is reported and primarily attributed to microhardness variations, which can be mitigated through lubrication and post-processing. The effects of applied load and frequency on EB-PBF Ti-6Al-4V are also discussed. The wear resistance at elevated temperatures shows a mixed trend that depends on the counterpart material and the testing methods. Wear mechanisms involve oxide tribo-layer formation, abrasive wear, and adhesive wear. Current limitations, future research directions, and a standardization framework are also discussed. Full article

This study explores a novel approach to enhance the surface properties of Ti-Al alloys for biomedical applications by creating a compositional gradient layer through aluminum deposition using Electrical Discharge Machining (EDM). The primary goal was to develop a metallurgically bonded intermetallic zone that supports strong adhesion and improved compatibility for subsequent hydroxyapatite (HA) deposition. Aluminum was deposited onto a Ti6Al4V substrate via EDM under controlled conditions, followed by thermal and thermochemical treatments to induce diffusion and intermetallic phase formation. Comprehensive analyses using optical and electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and X-ray diffraction (XRD) revealed the formation of well-adhered layers composed of complex Ti-Al intermetallics such as TiAl₂ and TiAl₃, along with oxide phases including TiO₂ and Al₂O₃. Thermal and thermochemical treatments further improved surface hardness, reaching up to 1057 HV, and influenced the diffusion behavior of aluminum, titanium, and vanadium. Adhesion tests confirmed that the untreated and thermochemically treated layers exhibited superior mechanical stability, while thermal treatment alone led to brittleness and delamination. These findings demonstrate that a properly engineered intermediate aluminide layer can significantly improve the performance of bioceramic coatings, particularly HA, by providing enhanced structural integrity and biocompatibility. 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

This study investigates the surface modification of Ti-6Al-4V alloy through the electrical discharge machining (EDM) process to improve its suitability for orthopedic and dental implant applications. The analysis focused on evaluating the morphological, wettability, roughness, hardness, and biocompatibility properties of the modified surfaces. Samples were subjected to different dielectric fluids and polarities during EDM. Subsequently, optical microscopy, roughness measurements, Vickers microhardness, contact angle tests, and in vitro cytotoxicity assays were performed. The results demonstrated that EDM processing led to the formation of distinct layers on the sample surfaces, with surface roughness increasing under negative polarity by up to ~304% in Ra and 305% in Rz. Additionally, wettability measurements indicated that the modified surfaces presented a lower water contact angle, which suggests enhanced hydrophilicity. Moreover, the modified samples showed a significant increase in Vickers microhardness, with the highest value reaching 1520 HV in the recast layer, indicating improvements in the mechanical properties. According to ISO 10993-5, all treated samples were classified as non-cytotoxic, presenting RGR values above 75%, similar to the untreated Ti-6Al-4V alloy. Therefore, it is concluded that surface modification through the EDM process has the potential to enhance the properties and safety of biomedical implants made with this alloy. Full article

Tribological processes in extreme environments pose serious material challenges, requiring coatings that resist both wear and corrosion. This review summarizes recent advances in protective coatings engineered for extreme environments such as high temperatures, chemically aggressive media, and high-pressure and abrasive domains, as well as cryogenic and space applications. A comprehensive overview of promising coating materials is provided, including ceramic-based coatings, metallic and alloy coatings, and polymer and composite systems, as well as nanostructured and multilayered architectures. These materials are deployed using advanced coating technologies such as thermal spraying (plasma spray, high-velocity oxygen fuel (HVOF), and cold spray), chemical and physical vapor deposition (CVD and PVD), electrochemical methods (electrodeposition), additive manufacturing, and in situ coating approaches. Key degradation mechanisms such as adhesive and abrasive wear, oxidation, hot corrosion, stress corrosion cracking, and tribocorrosion are examined with coating performance. The review also explores application-specific needs in aerospace, marine, energy, biomedical, and mining sectors operating in aggressive physiological environments. Emerging trends in the field are highlighted, including self-healing and smart coatings, environmentally friendly coating technologies, functionally graded and nanostructured coatings, and the integration of machine learning in coating design and optimization. Finally, the review addresses broader considerations such as scalability, cost-effectiveness, long-term durability, maintenance requirements, and environmental regulations. This comprehensive analysis aims to synthesize current knowledge while identifying future directions for innovation in protective coatings for extreme environments. Full article

Attaining dimensional accuracy in CNC-machined parts is essential for high-precision manufacturing, especially when working with materials that exhibit varying mechanical and thermal characteristics. This research provides a thorough experimental comparison of manual and automated metrological systems, specifically the Digital Vernier Caliper (DVC) and Coordinate Measuring Machine (CMM), as applied to five different engineering alloys through five progressively machined axial zones. The study assesses absolute error, relative error, standard deviation, and measurement repeatability, factoring in material hardness, thermal conductivity, and surface changes due to machining. The results indicate that DVC performance is significantly affected by operator input and surface irregularities, with standard deviations reaching 0.03333 mm for Bronze C51000 and relative errors surpassing 1.02% in the initial zones. Although DVC occasionally showed lower absolute errors (e.g., 0.206 mm for Aluminum 6061), these advantages were countered by greater uncertainty and poor repeatability. In comparison, CMM demonstrated enhanced precision and consistency across all materials, with standard deviations below 0.0035 mm and relative errors being neatly within the 0.005–0.015% range, even with challenging alloys like Stainless Steel 304. Furthermore, a Principal Component Analysis (PCA) was conducted to identify underlying measurement–property relationships. The PCA highlighted clear groupings based on sensitivity to error in manual versus automated methods, facilitating predictive classification of materials according to their metrological reliability. The introduction of multivariate modeling also establishes a new framework for intelligent metrology selection based on material characteristics and machining responses. These results advocate for using CMM in applications requiring precise tolerances in the aerospace, biomedical, and high-end tooling sectors, while suggesting that DVC can serve as an auxiliary tool for less critical evaluations. This study provides practical recommendations for aligning measurement techniques with Industry 4.0’s needs for accuracy, reliability, and data-driven quality assurance. Full article

The miniaturization of smart materials has become a new trend in the modern manufacturing industry due to its enormous application in the aerospace, biomedical, and automobile sectors. Nickel–titanium (NiTi)-based binary shape memory alloys (SMAs) are one of the smart materials with certain supreme features like shape memory effect, pseudo-elasticity, high ductility, strong corrosion-resistance, and elevated wear resistance. For this, several micro-machining processes have been developed to machine NiTi SMAs. This paper summarizes all of the conventional and non-conventional micro-machining processes employed to machine NiTi SMAs. In this review process, the surface integrity, dimensional accuracy of the machined surface, cutting force and tool wear analysis during conventional and non-conventional micro-machining of NiTi SMA are evaluated mostly with the aid of input process variables like cutting speed, depth of cut, width of cut, types of coolants, tool coating, discharge voltage, capacitance, laser fluence, pulse duration, scan speed, electrolysis concentration and gap voltage. The optimization of process parameters using different methods during conventional and non-conventional micro-machining of NiTi SMAs is also analyzed. The problems faced during conventional micro-machining of NiTi SMAs are overcome by non-conventional micro-machining processes as discussed. The present study aims to recognize potential developments in the improvement of the micro-machinability of NiTi SMAs. Full article

One of the most popular alloys for biomedical applications is TiAl6V4. Even though TiAl6V4 is widely used, it faces several challenges. Firstly, TiAl6V4 is prone to stress shielding caused by the difference in Young’s moduli of the alloy (110 GPa) and human bones (20–30 GPa). Secondly, there is the presence of cytotoxic elements, aluminum and vanadium. Researchers have proposed Ti-35Nb-7Zr-5Ta (TNZT) alloy to overcome these disadvantages, an excellent substitute for natural human bones. This alloy offers a lower elastic modulus (up to 81 GPa), much closer to human bones than TiAl6V4 alloy. Also, TNZT alloy contains no cytotoxic elements and has excellent biocompatibility and high corrosion resistance. Given the positive outcomes on powder-mixed micro electro-discharge machining (PM-μ-EDM) of Ti alloy using hydroxyapatite (HA) powder, we studied the machinability of TNZT alloy using HA powder mixed-μ-EDM by changing the HA powder concentration (0, 5, and 10 g/L), gap voltage (90, 100, and 110 V), and capacitance (10, 100, and 400 nF) according to the Taguchi L9 method. Machining performance metrics such as material removal rate (MRR), overcut, and circularity were examined using a tungsten carbide tool of 237 µm diameter. The results showed an overcut of 10.33 µm, circularity of 8.47 µm, and MRR of 6030.89 µm³/s for the lowest energy setup. Full article

This study presents a probabilistic analysis of how thermal conductivity and hardness affect surface integrity in CNC turning, focusing on five engineering materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 Annealed, and Stainless Steel 304 Annealed. Surface roughness and waviness parameters were measured across five zones per material under controlled conditions. Results revealed a strong inverse correlation between thermal conductivity and surface irregularities. Aluminum Alloy 6061 (167 W/m·K, 95 HBW) exhibited the best surface finish (R_a_avg ≈ 1.7664 µm; W_a_avg ≈ 0.8242 µm), while Carbon Steel 1020 Annealed (51 W/m·K, 170 HBW) demonstrated the poorest finish (R_a_avg ≈ 4.0780 µm; W_a_avg ≈ 3.8384 µm). Intermediate materials followed consistent trends: Brass C26000 (R_a_avg ≈ 2.5398 µm; W_a_avg ≈ 1.5364 µm), Bronze C51000 (R_a_avg ≈ 1.8862 µm; W_a_avg ≈ 0.9299 µm), and Stainless Steel 304 Annealed (R_a_avg ≈ 0.9031 µm; W_a_avg ≈ 0.5914 µm). High thermal conductivity materials exhibited narrower parameter distributions (σ_R_a ≤ 0.40 µm), while harder materials showed increased variability (σ_R_a ≥ 0.60 µm). Probabilistic models, validated with high confidence (R² > 0.95), offer predictive insights for optimizing machining strategies. These findings support material-specific process adjustments to enhance surface quality, tool life, and functional reliability in aerospace, automotive, and biomedical applications. Full article

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to $1\times {10}^{10}$ cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article

Ti-6Al-4V is a titanium-based alloy that is widely used in a diverse range of applications, especially in industries such as biomedical and aerospace. Several lubricooling techniques have been introduced to enhance the machinability of these materials. Among them, environmentally friendly strategies are gaining in importance, with sustainability trends rising in manufacturing. The present research investigates the effect of two eco-friendly lubricooling techniques (minimum quantity lubrication and cryogenic cooling), along with other cutting parameters (cutting speed and feed per tooth), on the surface roughness and microhardness of the machined surfaces, which are identified as one of the most frequently implemented indicators of surface integrity in the ball-end milling of the Ti-6Al-4V alloy. In addition, the total electrical energy consumption of the machine tools under different cooling/lubrication conditions was also analyzed. The results obtained showed that cryogenic cooling enhanced milling performance as compared to MQL. Moreover, a multi-objective parameter optimization model integrating the machining responses (surface roughness, microhardness, energy consumption, and productivity) and sustainability metrics (environmental impact, operator’s health and safety, and waste management) was introduced. It was found that cryogenic cooling outperformed the MQL method in terms of both machining performance and environmental impact. An analysis of variance (ANOVA) was carried out to evaluate the significance of each process parameter on the multiple performance index. The results indicate that feed per tooth, cooling method, and cutting speed were significant, with respective contributions of 39.4%, 36.8%, and 22.9%. Finally, the optimal parameter setting was verified through a confirmation test and the results reveal that an improvement was observed in the machining responses and multiple performance index. Full article

The machining of titanium alloys, particularly Ti6Al4V, presents a significant challenge in manufacturing engineering. Its high strength, low thermal conductivity and high chemical reactivity make Ti6Al4V a hard-to-machine material. However, the machining process is critical for aerospace and biomedical industries. The rapid wear and short lifetime of cutting tools are the main limitations in Ti6Al4V machining, leading to a large increase in manufacturing costs and compromising the surface quality of machined components. Faced with this problem, the texturing of cutting tools, especially through laser-based techniques, has gained considerable attention in the last decade due to improvement of the tribological properties of textured surfaces. Laser Surface Texturing (LST) has emerged as a promising technique to improve the tribological performance of cutting tools by enabling the creation of precise surface structures. Building on prior research, this review provides a comprehensive overview of the most recent research on this topic, summarizing key findings and outcomes from various investigations. Full article

As the demand for high-performance dissimilar material joining continues to increase in fields such as aerospace, biomedical engineering, and electronics, the welding technology of dissimilar materials has become a focus of research. However, due to the differences in material properties, particularly in the welding between metals and non-metals, numerous challenges arise. The formation and quality of the weld seam are strongly influenced by laser process parameters. In this study, successful welding of high-borosilicate glass to a TC4 titanium alloy, which was treated with high-temperature oxidation, was achieved using a millisecond pulsed laser. A series of process parameter comparison experiments were designed, and the laser welding behavior of the titanium alloy and glass under different process parameters was investigated using scanning electron microscopy (SEM) and a universal testing machine as the primary analysis and testing equipment. The results revealed that changes in process parameters significantly affect the energy input and accumulation during the welding process. The maximum joint strength of 60.67 N was obtained at a laser power of 180 W, a welding speed of 3 mm/s, a defocus distance of 0 mm, and a frequency of 10 Hz. Under the action of the laser, the two materials mixed and penetrated into the molten pool, thus achieving a connection. A phase, Ti₅Si₃, was detected at the fracture site, indicating that both mechanical bonding and chemical bonding reactions occurred between the high-borosilicate glass and the TC4 titanium alloy during the laser welding process. Full article