Microscale fused deposition modeling (µ-FDM) requires thermoplastic materials with stable thermal behavior, controlled melt flow, and reliable interlayer mechanical performance. This study investigates the thermal, structural, morphological, and flexural behavior of µ-FDM-printed acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC), and a 50:50 ABS-PC blend to establish a process-structure-property relationship under microscale deposition conditions. Thermogravimetric analysis (TGA) revealed a progressive increase in degradation temperature from ABS (T max ≈ 416 °C) to the ABS-PC blend (≈ 438 °C) and PC (≈ 520 °C), indicating enhanced thermal stability of the blend. Differential scanning calorimetry indicates partial miscibility between the ABS and PC phases. Fourier-transform infrared and X-ray diffraction analyses confirmed intermolecular interactions and predominantly amorphous structures without new phase formation. Flexural testing demonstrated an increase in ultimate flexural strength from ABS (~38 MPa) to the blend (~44 MPa) and PC (~53 MPa), while the ABS-PC blend exhibited the highest flexural modulus. Fracture and morphological analyses revealed a transition from brittle crack propagation in ABS to ductile deformation in PC, whereas the blend exhibited mixed-mode fracture behavior with improved interlayer load transfer. The results demonstrate that ABS-PC blending provides a balanced combination of thermal stability, stiffness, and mechanical performance, making it a promising material system for µ-FDM-based micromanufacturing applications.
The study emphasizes the importance of understanding the biomechanical properties of skin to maintain skin health and enable early disease detection. The research utilized piezo-based and optical position sensitive detector sensing methods to measure the skin’s natural frequency in vivo and non-invasively. A 3D-printed band with a piezo sensor was used to collect data, while COMSOL Multiphysics was employed for numerical analysis. Results showed piezo-sensing frequencies at 262.17 ± 2 Hz and optical-sensing frequencies at approximately 258.75 ± 4 Hz, with numerical analysis indicating a frequency of 269 Hz. The maximum deviation between experimental and numerical frequencies was within an acceptable error margin of 3.81%. The findings provide insights into skin properties, potentially enhancing the development of noninvasive diagnostic tools for early detection of skin diseases.
Microelectromechanical Systems (MEMS)-based acoustic metamaterials (AMMs) enable precise manipulation of acoustic waves at the microscale through engineered compact, lightweight, subwavelength structures. Unlike most macroscale AMMs fabricated by additive manufacturing, MEMS-based AMMs are realized using microfabrication processes that ensure high precision, tight dimensional control, wafer-level integration, and compatibility with electronic microsystems. This review covers the main design types of MEMS-based AMMs: Helmholtz resonators (single and array), ultra-micro-perforated panels (UMPPs), membrane-cavity structures, phononic bandgap designs, and impedance-controlled systems. It focuses on how fabrication methods affect acoustic performance, especially thermo-viscous losses. Core MEMS fabrication techniques-such as photolithography, deep reactive-ion etching (DRIE), wafer bonding, soft lithography and polydimethylsiloxane (PDMS) casting, and Lithographie, Galvanoformung, Abformung (LIGA)-are examined for their structural fidelity, dimensional accuracy, and integration capability. UMPPs with sub-100 µm apertures suppress higher-order band narrowing and frequency shifting. Flexible UMPPs, fabricated by casting PDMS onto MEMS silicon molds, broaden the absorption bandwidth through structural flexibility, though with a slight reduction in peak absorption due to coupled structural-acoustic vibration effects. Applications include MEMS vibration isolation, phononic metaplates, implantable sensors, surface acoustic wave control, noise absorption, and RF filtering, covering kilohertz to gigahertz frequencies. This review links acoustic design with MEMS fabrication and system integration, providing a framework for developing microscale AMM devices.
The development of scalable and flexible energy harvesting systems is crucial for powering next-generation wearable electronics. In this study, a 3D printed triboelectric nanogenerator (TENG) based on an MXene–silicone composite was fabricated using extrusion-based direct ink writing. Ti3C2Tx MXene nanosheets were synthesized via selective etching of Ti3AlC2 and incorporated into a silicone elastomer matrix at low concentrations (0–2 wt%) to improve dielectric and mechanical performance. The composite inks exhibited pronounced shear-thinning behavior, ensuring excellent printability and structural stability. Mechanical characterization showed enhanced tensile strength and Young’s modulus up to 1 wt% MXene while preserving the flexibility required for repeated contact–separation operation. The dielectric constant increased from 2.7 for pristine silicone to 5.5 at 1.5 wt% due to interfacial polarization, leading to improved surface charge generation. Optimization of the printed lattice thickness revealed that the 1 wt% MXene sample with a thickness of 0.8 mm produced the highest open-circuit voltage of approximately 58 V. The improved electrical output arises from the combined effects of dielectric enhancement, mechanical compliance, and micro-architected structural design. This work demonstrates a scalable micromanufacturing approach for developing tunable and high-performance flexible triboelectric energy harvesting devices.
Polymer-based nanocomposites with engineered electrical conductivity have attracted considerable attention for precision micro-manufacturing applications. However, the micromachining behavior of hybrid conductive polymer nanocomposites, particularly under nontraditional processes such as micro-electrical discharge machining (micro-EDM), remains largely unexplored. To address this research gap, a novel hybrid nanocomposite comprising polymethyl methacrylate (PMMA) reinforced with 10 wt.% multi-walled carbon nanotubes (MWCNTs) and 2.5 wt.% silver nanoparticles was synthesized and evaluated for micro-EDM machinability, with potential applications in biomedical and precision engineering sectors. Using a solution-mixing technique, the nanocomposite was created, resulting in the fillers being evenly distributed throughout the PMMA matrix. Structural and electrical characterization were performed using X-ray diffraction over a defined 2θ range, scanning electron microscopy (SEM) for morphological assessment, and dielectric spectroscopy across a broad frequency spectrum to evaluate conductivity enhancement and interfacial polarization behavior. Dielectric spectroscopy revealed enhanced electrical conductivity and interfacial polarization within the nanocomposite matrix, indicating improved charge transport pathways necessary for stable spark initiation during micro-EDM. The composite was successfully micromachined using micro-EDM using brass (0.5 mm), tungsten (0.5 mm), and copper (0.4 mm) wire electrodes in a hydrocarbon-based electrical discharge machining oil dielectric with varying input voltages (100–150 V) and pulse on-time ratios (10%–50%). The maximum material removal rate achieved was 0.0063 mm³/s using a tungsten electrode. SEM revealed hole diameters ranging from 450 to 555 µm, with tungsten electrodes showing the lowest roundness error (~40 µm), corresponding to approximately 8% deviation relative to the 0.5 mm nominal electrode diameter. Tool wear analysis showed minimal degradation for tungsten electrodes compared to copper and brass. In this study, a PMMA/MWCNT/Ag hybrid nanocomposite was synthesized, characterized, and systematically evaluated for micro-EDM machinability under varying discharge parameters.
Additive manufacturing (AM) techniques are increasingly applied with precision and are recognized as valuable tools across various stages of product development and production. The demand for innovative and customized products tailored to individual users is growing rapidly, and AM enables the fast creation of such items, facilitating timely market launches. Among the existing AM technologies, powder bed fusion (PBF) has gained considerable attention due to its ability to produce high-quality parts automatically. This is attributed to its compatibility with a wide range of materials and the superior quality of the final components. Despite its advantages, PBF faces challenges that must be addressed to establish it as a reliable manufacturing method. Various issues, such as poor dimensional accuracy, variations in mechanical properties, defects, residual stresses, surface irregularities, etc., limit its application in high-value, mission-critical products. The primary factors affecting the quality of parts produced by PBF are the processing parameters. Because process parameters are directly related to microstructure development and process-induced defects, optimizing parameter settings and preventing defects such as melt pool geometry is key to ensuring the production of high-quality AM components. Hence, in this work, the role of process parameters and other factors affecting the part quality is discussed in detail. Moreover, the manuscript discusses the role of advanced tools, such as machine learning, in situ monitoring, simulations, etc., to enhance the part quality during PBF-based AM.
TiAlSiN–coated tools are recommended for machining gummy and strain-hardenable austenitic stainless steels for their superior wear resistance and thermal stability. However, sustenance of such coatings is reportedly more challenging on micro-tools due to the complex material removal mechanics in micro-drilling. The current study attempts to investigate the impact of carbide substrate microstructure on the sustenance of this coating on micro-drills when deposited by high power impulse magnetron sputtering (HiPIMS) and explore its subsequent influence on the tool’s performance in machining of SS316 steel, comparing with uncoated counterparts. It was assessed by thrust force (Fz), torque, acoustic emission (AE) signals, micro-hole quality, and micro-tool condition. Carbide tools with finer carbide grain exhibited unimodal distribution, resulting in higher hardness. They consistently outperformed those with coarser carbide grains in multimodal distributions with lower hardness. Coated tools were expectedly superior to uncoated ones, but coatings experienced earlier and more severe delamination on substrates having softer and more heterogeneous microstructure. Machining results indicated a 40% reduction in peak Fz, 33% reduction in torque, and 20% reduction in acoustic energy with coated tools having finer grains in unimodal distribution. Higher substrate hardness and superior microstructural homogeneity played a key role in the enhanced performance of coated micro-tools.
Technological developments in dentistry have increased diagnosis precision, facilitated treatment administration, and shortened chair times, enabling dentists to handle patients more successfully. A dentist may now see, precisely measure, gather data, and fabricate prototypes of both soft and hard tissue thanks to 3D printing. It has become feasible by using rapid prototyping techniques, including filament, resin, and metal-based 3D printing. Every technology has unique benefits when it comes to producing a certain kind of product. The most often utilised technologies in dentistry are Vat polymerisation-based systems. Using three-dimensional printing in different methods of treatment might be beneficial. The applications in the dental sector comprise crowns, bridges, occlusal splints, retainers, implants, drill guides, and other oral restorations. This study offers a structured and sustainability-focused framework for comprehending the current status and future prospects of additive manufacturing (AM) in dentistry, facilitating its transition towards trustworthy and environmentally responsible clinical application. Statement of novelty and statement of industrial relevance This review presents an updated analysis of dental AM based on recent literature, with a specific focus on sustainability-driven evaluation. A critical comparison of major techniques (SLA, DLP, FDM and SLM) is provided in terms of accuracy, surface quality, biocompatibility, mechanical reliability, clinical acceptance, and environmental impact. There is a discussion of new trends like micro-scale fabrication, nano-reinforced dental resins, chairside manufacturing, and fully digital workflows. From an industrial point of view, the review gives useful information about dental applications like crowns, bridges, occlusal splints, retainers, implants, drill guides, and other oral restorations.
The present work shows the graphene nano powder-assisted micro-electrical discharge process for deposition of high-entropy alloy (HEA) coatings. Results show remarkable improvements in corrosion resistance behavior for MgAz31b workpiece designed for miniaturized system and implant applications. In this work, HEA coatings were prepared at different pulse-on durations and thoroughly examined with respect to their surface morphology, corrosion characteristics, and tribological behavior. Findings reveal that the graphene powder-mixed AlCoCrFeNi HEA coating in electrical discharge processing significantly enhances both corrosion and wear resistance. Among the studied compositions, AlCoCrFeNi exhibited the best performance, achieving a corrosion penetration rate (CPR) of 9.6 mm/year as determined by electrochemical testing. Furthermore, the addition of graphene nano powder during the coating process provided an additional boost to the tribological performance. Graphene enhances plasma channel stability and promotes uniform molten material deposition, resulting in a denser and more defect-free coating compared to conventional electrical discharge machining (EDM) processing.
This study focuses on the welding of stainless steel using automatic metal inert gas (MIG) welding, which plays a vital role in achieving high-quality and durable joints. The work investigates how various welding parameters influence the mechanical properties and metallurgical characteristics of 202L stainless steel welds. Experiments were conducted by varying parameters such as wire speed, gas flow rate, current, and voltage. Tensile and impact tests (as per ASTM standards), along with microstructural examination, revealed that wire speed and gas flow significantly influence both the microstructure and overall joint strength. Impact toughness was found to increase with higher welding current, while the ultimate tensile strength initially rose and then decreased beyond an optimal level. The findings provide useful guidance for selecting suitable MIG welding parameters to produce strong and reliable stainless steel components for industrial applications.
Nickel–Titanium (NiTi) shape memory alloy is widely used in aerospace, biomedical, and micro-actuation applications due to its unique shape memory and superelastic properties; however, its poor machinability poses significant challenges for conventional machining processes. Electrochemical drilling (ECD) emerges as a promising non-traditional machining technique for producing high-quality micro-holes in NiTi without inducing tool wear and thermal damage. The present work focuses on determining the optimal parametric data set during ECD of NiTi shape memory alloy. The rate of material removal (RMR), overcut (OC), taper angle (TA), and circularity error (CE) are considered as performance measuring indices. Experiments are conducted using a central composite design (CCD) based RSM considering input parameters such as current (I), voltage (V), and inter-electrode gap (IEG). The accepted models are analyzed using an artificial neural network (ANN) to find out the error between the predicted Value and the Model data. To optimize the experiments, a metaheuristic flower pollination algorithm (FPA) has been implemented, both as a single-objective and multi-objective optimization technique to determine the best parameter setting (I = 22 Amp, V = 6 volts, IEG = 0.5 mm). The parametric optimal results obtained using the FPA algorithm are RMR (mm 3 /min) 0.48893, OC (mm) 0.054187, TA (degree) 0.33221, and circularity error (CE) (mm) 0.010429 with an objective function value of 0.64394. The experimental results found that an appropriate combination of V, IEG, and I can effectively minimize defects while maximizing RMR, thereby improving the overall quality and efficiency of the ECD process.
The standard method of EDM (electrical discharge machining) has many benefits through the new application of conductive powders into the dielectric fluid (or dielectric medium), in a hybrid type of machining called PMEDM (powder mixed EDM), which is an emerging hybrid cutting process. PMEDM provides the biggest picture of the characteristics, principles, other indicators of performance, and recent improvements. In addition, a significant number of powder types affect how well a PMEDM process will perform, including aluminum, copper, graphite, and silicon carbide. There are also many interrelated fundamental variables involved in producing electrical discharges that produce the results desired from machining. They include how much powder is mixed with the fluid; what current is used to discharge the fluid; how long the electrical discharge takes to recharge the electrodes; and how far apart the two positive electrodes are when they create their current. The results of these interactions can be seen in the wear on tools, surface finishing, and accuracy of machining. Thus, this article provides an overview of both the fundamental processes and limitations associated with the PMEDM process and gives a thorough overview of the latest technological advancements within this area of research. Ultimately, the PMEDM process has grown to become very important to the future of advanced machining technologies, and this will continue to support other forms of advanced machining technologies that could change the future of precision manufacturing.
Hybridizing non-traditional machining (NTM) processes, such as micro-ECDM, is a great challenge in measurement and microfabrication. To find out the solution to this problem, in the present research, the overall model is demonstrated in three steps, during the first stage, ANN is used to construct the linear model of width of cut (WOC), metal removal rate (MRR), and surface roughness (SR) from experimental data consisting of process parameters, that is, voltage (V) pulse frequency (PF), electrolyte concentration (EC), and duty ratio (DR). In the second phase, to get the best-fitted model, we applied both Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and a hybrid of these two algorithms for cross-validation and validation on the train and test datasets. Based upon root mean square error, accuracy, and computational time, the proposed algorithm is more efficient than PSO and GA. Furthermore, in the final phase, the ANN model was optimized using hybrid GAPSO, which helped to determine the optimal process parameters responsible for maximum MRR, minimum WOC, and SR formation. The result shows that maximum MRR, minimum WOC, and SR were formed for the optimized value of voltage 45 volts, electrolytic concentration 30 wt%, DR 0.45, and PF 75 Hz. Moreover, hybrid GAPSO-ANN shows better convergence, accuracy, and computational time (seconds) for micro-machining characteristics analysis.
Photochemical machining (PCM) is a machining process in which a mask or a chemical protective layer is employed along with a method of creating an image, while a chemical is used to dissolve undesired parts of a workpiece. In this research, the main goal is to employ the PCM method to create a groove on spherical 8 mm and 4 mm diameter workpieces made of 100Cr6 steel. Experiments were conducted using Positiv 20 as the photoresist agent, a tilted spray coating method to cover the workpiece with photoresist, a device used to bake photoresist onto 3D surfaces, a profile projector for measuring the dimensions, and an ML200 light projection device. The workpieces are exposed to light for 20 seconds, followed by development in a 10 g/L sodium hydroxide (NaOH) solution for 43 seconds. After the initial experiments, a problem, called “imperfect development,” arose, which was resolved by changing the exposure method of the pattern on the spherical workpiece. After the experiments were done and the “imperfect development” problem was resolved, a groove was formed on the surface of the workpiece which measured 3,072 µm in length, 636 µm in width and 52 µm in depth on an 8 mm diameter spherical workpiece, and a groove measured 3,084 µm in length, 648 µm in width and 51 µm in depth on a 4 mm diameter spherical workpiece.
Materials machined using electro discharge machining (EDM) generally require longer processing times because of slower metal removal. Improving the material removal rate (MRR) often depends on modifying the dielectric medium or employing hybrid EDM techniques. In this study, standard EDM oil and graphite powder-mixed EDM (PMEDM) oil were compared to enhance the machining performance of high-strength EN24 alloy steel. EN24, composed of nickel, chromium, and molybdenum, is widely used for shafts, axles, and heavy-duty offshore components due to its high ductility and tensile strength. Graphite-mixed dielectrics are reported to improve MRR because graphite particles increase spark conductivity and thermal efficiency in the discharge gap. Two sets of experiments were performed: one using standard EDM oil and the other using EDM oil mixed with 2 g/L of graphite powder (0.25 µm size). Machining trials were carried out to evaluate key performance indicators such as MRR, tool wear rate (TWR), overcut, and surface roughness (SR). Comparative analysis revealed that the graphite-mixed dielectric enhanced MRR, TWR, and overcut by 43.14%, 12.27%, and 23.13%, respectively, while SR improved by 7.9%. Overall, the results demonstrate that graphite-PMEDM oil significantly improves the machining efficiency and surface characteristics during EDM of EN24 steel.
Microelectromechanical systems (MEMS) have remained an innovative field since their introduction. Its applications have been observed in most general-use areas of human life, and for this reason, various advancements are continuously proposed for it. MEMS started with fabricating individual sensors to measure a particular response of the system, gradually shifted to integrating multiple sensors to record multiple responses, and has now moved to multiple devices with integrated sensors connected at a common point, the Internet of Things, where the devices can act smart. One additional requirement from the present perspective is to propose self-sustainable devices, which can generate power for themselves and are well capable of sustaining their lifetime and performance. Hence, artificial intelligence (AI) integrated smart and self-sustained sensors are the present interest. In this context, this review briefly discusses the basics of MEMS and machine learning (ML) and their integration to observe different applications of AI/ML in MEMS sensors in different fields. The various sensitivity and sustainability requirements in MEMS are primarily being catered to through proposing innovation in fabrication techniques, materials, detection schemes, and related hardware/software, which have also been briefed in the review.
Electrochemical discharge machining (ECDM) is a novel non-conventional processing method that entails high-temperature melting and enhanced chemical etching facilitated by substantial electrical energy discharge. The present research article presents experimental findings on the effects of tool rotation and tool–workpiece (T–W) gap on the geometric characteristics of drill holes formed in a zirconia workpiece during the ECDM process. In addition, the influence of a one-micron-thick platinum-plated tool is also used to analyze the surface texture of the micro-hole formed in the ceramic. The effect of various process parameters is explored, including applied voltage range (90–110 V), electrolyte concentration (25%–35%), lower pulse frequencies (10–30 kHz), tool rotation speeds (10–50 rpm), and T–W gap (0–30 µm). The discharge-affected zone on the workpiece, due to sparking, and the surface topography of the machined zone have been studied using a scanning electron microscope. The difference in shape of the heat flux projected on the machining zone and the temperature distribution on the workpiece, with and without tool rotation, has been generated by the thermo-gun. The topographical study of the machined surface and the temperature distribution reveals the potential use of this indigenously designed and developed ECDM setup for machining on ceramics in the micron regime. Furthermore, the statistical analysis using the Taguchi L27 design, S/N ratio, analysis of variance, and regression modeling revealed that applied voltage is the most dominant parameter, contributing nearly 48% to material removal rate and 28% to radial overcut, with significant effects at a 95% confidence level.
Micro-Electrical Discharge Machining (Micro-EDM) has emerged as a crucial technique for fabricating intricate micro-features in advanced alloys such as Ti-6Al-4V. The present study investigates the effects of gap voltage, peak current, pulse-on time, and flushing pressure on material removal rate (MRR), tool wear rate (TWR), taper, and overcut during micro-hole drilling. A total of 31 experiments were conducted using a central composite design, and the experimental data were used to develop predictive models based on multivariate regression analysis, fuzzy logic, artificial neural networks, and an adaptive neuro-fuzzy inference system (ANFIS). The models were evaluated using five statistical indices: mean absolute percentage error (MAPE), root mean squared log error, root mean squared percentage error, root relative squared error, and correlation coefficient (R). Results show that ANFIS provides the most accurate predictions, with MAPE values below 5% for MRR and TWR and R > 0.95 across all responses, outperforming other approaches. The novelty of this work lies in its comparative framework, which highlights the superiority of ANFIS in capturing nonlinear input-output relationships in micro-EDM. The study concludes that ANFIS can be effectively applied for optimizing micro-EDM parameters, and future research should extend this methodology to other difficult-to-machine materials and explore advanced ANFIS variants for improved machining performance.
Nano finishing of small surfaces has become a challenging task in various industries such as automotive, machine tool, aerospace, and others. These miniature surfaces require a specially designed tool for their precise surface finishing. In the existing work, the magnetorheological (MR) finishing process replaces grinding, lapping, or honing processes to provide fine surface finishing through the use of gel-like smart fluid. The fluid contains a suspension of abrasive and iron particles along with grease and paraffin oil. With the use of Maxwell Ansoft software, a magnetic simulation was performed to verify the dispersion of the magnetic field. The safety and effectiveness of the tool design were well demonstrated by this simulation. Thereafter, the optimization study revealed with workpiece rotations of 300 rpm, tool linear speed of 70 cm/min, and completion time of 50 minutes, the maximum percentage change in surface roughness (Ra) value was found as 70%.
Additive manufacturing (AM) enables the precise and customized fabrication of complex geometries. Among its applications, 3D clay printing is gaining attention in construction, art, and architecture; however, it faces significant challenges related to structural stability, rheological behavior, and process parameter optimization. Key factors such as printing layer height, printing speed, and material properties critically influence the quality of printed parts, often resulting in defects like cracking and warping. This study uses a design of experiments (DoE) methodology to evaluate the effects of the parameters on the quality of 3D-printed clay objects. The analysis identifies optimal parameter settings that reduce dimensional defects, particularly in final thickness ratios. The optimal parameters for printing were set to layer height (A), printing speed (B), nozzle diameter (C), and material preparation time (D), being 0.3 mm, 25 mm/s, 13 mm, and 1 hour, respectively. Results show that optimized configurations enhance both dimensional accuracy and mechanical stability. These findings contribute to the standardization of small-scale 3D clay printing, offering valuable insights for academic research and industrial implementation.
