Search Results (68)

Tool wear remains a critical limiting factor in machining performance, particularly in dry cutting conditions where friction and tribological interactions dominate. This study investigates the influence of a 5–8 μm PVD-deposited TiN coating on the wear behavior of high-speed steel (HSS) end mills during milling of three representative wood species (oak, beech, and fir). A spatially resolved wear evaluation methodology was employed, based on ten measurement points distributed along a 20 mm active cutting edge, enabling simultaneous assessment of mean wear and maximum localized wear (Umax). A factorial experimental design combining material type and feed rate (1500–2500 mm/min) was analyzed using two-way ANOVA with effect size quantification (η²). The results reveal a statistically significant reduction in mean wear for TiN-coated tools (F = 7.46, p = 0.0195, η² = 0.34), corresponding to an average decrease of approximately 46% compared to uncoated tools. Maximum wear was influenced by both coating (F = 14.73, p = 0.0028, η² = 0.399) and material (F = 4.37, p = 0.040, η² = 0.237). The experimental findings are interpreted through a tribological framework, indicating a transition from abrasion- and micro-chipping-dominated degradation in uncoated tools to a controlled wear regime in TiN-coated tools, characterized by reduced asperity penetration, delayed crack initiation, and limited tribochemical interactions. These results demonstrate that coating effects dominate global wear evolution, while material properties influence localized degradation. The proposed combined experimental–statistical–mechanistic approach provides a robust framework for understanding and optimizing tool performance in dry machining environments. Full article

This study systematically investigates the influence of steel fibers on the mechanical properties of cement concrete. End-hook, shear, and milling type steel fibers were selected, with comparisons made to copper-plated and corroded steel fibers. The effects of fiber type, aspect ratio (40–60), and volume content (0.5–1.5%) on the compressive, flexural, and splitting tensile properties of concrete were analyzed. A multi-objective mechanical performance prediction model was established using a combined macro- and micro-scale testing approach, integrated with response surface methodology (RSM) and I-optimal design. The results indicate that steel fibers can significantly enhance the overall mechanical properties of concrete. Among the types tested, the end-hook fiber exhibited the best performance in compressive and splitting tensile strength, and the 28-day compressive strength increased by 41% compared with plain concrete, while the milling fiber showed the greatest improvement in flexural strength, and the value reached up to 72%. Furthermore, the failure mode observations indicated that steel fiber incorporation fundamentally altered the fracture behavior of concrete, transitioning it from brittle fracture to quasi-ductile behavior with post-crack load-carrying capacity, particularly for end-hook and milling fiber types. An optimal parameter window for the fiber reinforcement effect was identified, with the best comprehensive performance achieved at an aspect ratio of 50–60 and a fiber content of 0.5–1.0%. The enhancement effect of copper-plated and corroded steel fibers was limited due to reduced interfacial bonding performance. The developed model demonstrates high prediction accuracy, providing a theoretical and experimental basis for the engineering application of fiber-reinforced concrete. Full article

Micro milling of metallic materials presents unique dynamic challenges due to highly nonlinear cutting forces and the susceptibility to self-excited vibrations (chatter). This paper presents a novel mathematical model for chatter prediction in micro milling, based on an enhanced formulation of cutting forces that includes the frictional interaction between the tool’s flank face and the machined surface. The proposed approach enables accurate simulation of the cutting process and prediction of the limiting depth of cut, beyond which chatter occurs. Experimental validation was performed using pneumatic spindle and micro end mills, with chatter detection based on surface inspection via digital microscopy. A strong correlation was observed between the simulated and experimentally determined limiting depths of cut, confirming the model’s predictive capability. This research offers a new methodology for modelling cutting forces and improves the ability to predict chatter in micro milling processes, contributing to the optimization of machining parameters across a wide range of materials. Full article

Polymethyl methacrylate (PMMA) is widely used in microfluidic device fabrication due to its chemical resistance, low cost, optical transparency, and manufacturing compatibility. However, limited research exists on wall deformations and the minimum achievable wall thickness between machined channels in PMMA via micro-milling. As microfluidic devices require tightly spaced features, identifying the minimum machinable wall thickness is essential for miniaturization and multifunctional integration, enabling rapid and reproducible biomedical testing. This study presents experimental data and finite element modeling on wall deformation characteristics—wall deviation angle, average wall thickness, and minimum machinable wall thickness—between micro-milled PMMA channels. Micro end-milling was performed with varying feed rates, wall thicknesses (50 μm, 100 μm, 150 μm), and milling strategies (direct, radial, axial depth). ANOVA was used to assess parameter influence, and finite element modeling simulated wall bending under the radial depth strategy. Results show that wall thickness, feed rate, and milling strategy significantly affect wall deviation and thickness. Experimental and simulation data revealed consistent trends: 50 μm walls showed cracking, base fractures, and geometric deviations, while 100 μm and 150 μm walls retained structural integrity. A minimum wall thickness of 150 μm is necessary to ensure reliable sealing in microfluidic devices. Full article

This study introduces a real-time acoustic emission (AE) monitoring system for the micro-milling of fluorine-doped tin oxide (FTO) thin films, a critical transparent conductive oxide (TCO) material. The system uses AE sensors to capture high-frequency elastic waves generated during the micro-milling process. We combine experimental and theoretical analyses to investigate how various milling parameters influence the AE signals. To address the crucial challenge of ensuring ductile mode cutting in brittle materials like FTO, we employed a convolutional neural network (CNN) to identify the transition between ductile and brittle machining modes. A CNN was trained on energy-based features extracted from the AE signals, achieving a classification accuracy of 97.37%. This high accuracy demonstrates the effectiveness of integrating AE sensing with deep learning for interpreting complex micro-machining data. The results confirm that this combined approach offers a powerful, non-destructive, and intelligent monitoring solution for improving process control and understanding in the micro-milling of fragile conductive thin films. Full article

The resistance to erosion of stainless steel-based plasma transferred arc (PTA)-cladded hardfacings reinforced with ex-situ-synthesized TiC is compared to those reinforced using in-situ-synthesized TiC (formed from TiO₂ and graphite). The PTA cladding was performed under an optimized torch linear velocity of 0.7 m/s and cladding current of 115 A. The microstructure of the cladded overlay was analyzed using scanning electron microscopy (SEM), and the phase composition was determined using X-ray diffraction (XRD). Vickers macrohardness measurements were made at representative areas at the surface of the overlays. An erosive wear test was conducted with impact angles of 30° and 90° and impact velocities of 20, 50, and 80 m/s. The formation of TiC from TiO₂ and graphite started during ball milling and ended during the cladding stage. The final TiC content in the hardfacings was below nominal, which is likely due to carbide segregation occurring during the cladding process. The highest hardness was 2.4 times that of stainless steel, which was observed in the deposit containing 60 vol.% ex-situ-synthesized TiC. Both ex-situ and in-situ TiC reinforcement improved resistance to erosion, providing up to 1.5 times better resistance under the 30° impact angle and up to 6.3 times under the 90° impact angle than that of stainless steel. However, ex-situ TiC showed a slightly larger improvement. At the 30° impact angle, the primary wear mechanism is micro-ploughing, but at the 90° impact angle it is surface fatigue. Both mechanisms appeared at both angles under 80 m/s impact velocity. Full article

The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Marquardt Back Propagation Algorithm (LMBP). The efficacy of these models was evaluated using RMSE, revealing that the LMBP model yielded the lowest RMSE for surface roughness (Ra), nose radius wear, and resultant cutting force, hence demonstrating superior predictive capability within the trained dataset. Additionally, a Genetic Algorithm (GA) was employed to ascertain the optimal machining settings, revealing that the ideal parameters include a cutting speed of 85 m/min, a feed rate of 0.07 mm/tooth, and a rounding radius of 7 mm. Moreover, the detachment of the coating layer resulted in alterations to the tooltip cutting edge on the machined surface as the circular loop distance increased. The initial arc radius fluctuated by 33.82% owing to tooltip defects that alter the edge micro-geometry of machining. The measured and expected values of the surface roughness, resultant cutting force, and nose radius wear exhibited discrepancies of 6.49%, 4.26%, and 4.1%, respectively. The morphologies of the machined surfaces exhibited scratches along with laces, and side flow markings. The back surface of the chip structure appears rough and jagged due to the shearing action. Full article

Micro- and nanoplastic pollutants are among the major environmental challenges, and are exacerbated by the continuous degradation of growing amounts of plastic debris in the aquatic environment. The purpose of this study was to investigate the morphology of micro/nanoplastics (M/NPs) formed from polyethylene terephthalate (PET) by mechanical degradation in an aquatic environment, which mimics the processes in the natural environment well, and to determine the impact of these particles on model aquatic organisms. To this end, M/NPs were obtained by ball milling in an aqueous medium and the effect of milling length on particle size and shape was investigated. The particles obtained in an environment simulating natural conditions were irregularly shaped, and those of nanometric size tended to form aggregates of various shapes. The ingestion and toxicity of PET M/NPs to freshwater zooplankton were then assessed. Daphnia magna and Thamnocephalus platyurus were used in a series of acute ecotoxicity tests, by exposure to M/NP dispersions at environmentally realistic concentrations (0.01–1.0 mg/L), as well as at very high concentrations (100–1000 mg/L). A significant uptake of PET particles by both types of invertebrates was observed, and the M/NPs were mainly concentrated in the digestive tracts of the crustaceans. However, they did not cause acute toxicity to the tested organisms or a reduction in their swimming activity, even at concentrations as high as 1000 mg/L. Full article

The selection of the right tool path trajectory and the corresponding machining parameters for end milling is a challenge in mold and die industries. Subsequently, the selection of appropriate tool path parameters can reduce overall machining time, improve the surface finish of the workpiece, extend tool life, reduce overall cost, and improve productivity. This work aims to establish the performance of end milling process parameters and the impact of trochoidal toolpath parameters on the surface finish of AISI D3 steel. It especially focuses on the effect of the tool tip nose radius deviation on the surface quality using precision measurement techniques. The experimental design was carried out in a systematic manner using a face-centered central composite design (FCCD) within the framework of response surface methodology (RSM). Twenty different experiment trials were conducted by changing the independent variables, such as cutting speed, feed rate, and trochoidal pitch distance. The main effects and the interactions of these parameters were determined using analysis of variance (ANOVA). The optimal conditions were identified using a multiple objective optimization method based on desirability function analysis (DFA). The developed empirical models showed statistical significance with the best process parameters, which include a feed rate of 0.05 m/tooth, a trochoidal pitch distance of 1.8 mm, and a cutting speed of 78 m/min. Further, as the trochoidal pitch distance increased, variations in the tool tip cutting edge were observed on the machined surface due to peeling off of the coating layer. The flaws on the tool tip, which alter the edge micro-geometry after machining, resulted in up to 33.83% variation in the initial nose radius. Deviations of 4.25% and 5.31% were noted between actual and predicted values of surface roughness and the nose radius, respectively. Full article

As a fundamental machining tool, the ball end milling cutter plays a crucial role in manufacturing. Due to its low thermal conductivity, the heat generated during the cutting process of titanium alloy materials is not dissipated efficiently, resulting in a substantial cutting heat. This heat leads to chip adhesion and exacerbates the wear of the ball end milling cutter, ultimately affecting its service life. Therefore, studying the residual life of the tool during the cutting process is essential to prevent significant impacts on the product’s surface quality due to tool damage and passivation. Most research on micro-texture cutters is based on experiments that analyze the wear patterns of cutters under various lubrication conditions and their influence on the cutting process. Different neural network prediction models are employed to enhance the accuracy and stability of tool life prediction models. However, the exploration of other superior models for predicting the life of micro-texture cutters remains ongoing. This paper is based on an experiment involving the milling of titanium alloy using a micro-pit-structured ball end milling cutter. It was found that the cutting force of the tool is higher during the initial and later wear stages. During the stable wear stage, the unevenness of the defective layer on the tool surface is reduced, increasing the contact area and reducing the surface pressure, thereby decreasing the cutting force. This study analyzes the influence of micro-pit structural parameters on the wear and milling force of the ball end milling cutter. By evaluating the wear value of the ball end milling cutter after each cut, the wear mechanism of the micro-texture cutter is identified. A deep-learning-based bidirectional long short-term memory (BiLSTM) neural network model for tool life prediction is developed. Through training and validation, the model’s accuracy and stability are continuously improved. A comparative analysis with different predictive models is conducted to determine whether the proposed model offers advantages over existing models, which is crucial for maximizing tool utilization and reducing manufacturing costs. Full article

Titanium alloys are widely used in various fields, but milling titanium alloy materials often leads to problems such as high milling forces, increased milling temperatures, and chip adhesion. Thus, the machinability of titanium alloys faces challenges. To improve the milling performance of titanium alloy materials, this study analyzes the effective working area on the surface of the milling cutter through mathematical calculations. We design micro-grooves in this area to utilize their friction-reducing and wear-resisting properties to alleviate the aforementioned issues. The effective working area of the ball end milling cutter’s cutting edge is calculated based on the amount of milling and the installation position between the milling cutter and the workpiece. By observing the surface structure of seashells, micro-grooves are proposed and designed to be applied in the working area of the milling cutter surface. The impact of the micro-groove area on the milling cutter surface and spindle speed on milling performance is discussed based on milling simulation and experimental tests. Experimental results show that the cutting force, milling temperature, and chip resistance to adhesion produced by micro-groove milling cutters are superior to conventional milling cutters. Milling cutters with three micro-grooves perform best at different spindle speeds. This is because the presence of micro-grooves on the surface of the milling cutter improves the friction state, promoting a reduction in milling force, while the micro-grooves also serve as storage containers for chips, alleviating the phenomenon of chip softening and adhesion to the cutter. When conducting cutting tests with a milling cutter that has three micro-grooves, the milling force is reduced by 10% to 30%, the milling temperature drops by 10% to 20%, and the surface roughness decreases by 8% to 12%. Full article

In order to further reduce the height of burrs on the surface of the workpiece when milling titanium alloy with ball-end milling cutters and optimize the quality of the workpiece, this article takes the mesoscopic geometric feature of ball-end milling cutters as the research object and establishes the theoretical relationship between the mesoscopic geometric feature parameters and the height of titanium alloy burrs during milling. A milling test platform was built to explore the influence of micro-texture, blunt edge single factor, and their interaction with cutting parameters on the force-thermal characteristics and workpiece burr in the milling process. The influence mechanism was revealed, and the prediction model was established. The results show that the ball-end milling cutter with mesoscopic geometric features was able to suppress burrs, and the burr height was reduced by 21% compared with the non-textured milling cutter. The micro-texture reduced the contact area and improved the heat dissipation conditions, thereby reducing the force-thermal characteristics and thus inhibiting the formation of some burrs. The blunt edge can disperse the stress, diffuse the local heat in the tool–chip contact area, and reduce the burr height. In the interaction test considering cutting parameters, the interaction between R and ap was significant. The optimized parameters based on the simulated annealing algorithm were as follows: the radius of the blunt edge was 33.242 μm, the distance between the texture and the edge was 114.621 μm, the texture diameter was 59.820 μm, the texture spacing L₁ was 131.410 μm, the cutting depth a_p was 0.310 mm, the cutting speed V was 140.019 mm/min, and the feed f was 60 μm/z. This provides a basis for the study of strengthening the tool to suppress burr size. Full article

Titanium alloy material has physical properties such as low thermal conductivity, high hardness, and surface resilience, which are prone to problems such as large milling force, low machining efficiency, and poor surface quality in processed products during dry milling. This document details our process of isolating micro-textures from biological structures, applying them to cutting tool surfaces to create micro-texture milling cutters, and employing this micro-texture technique to reduce friction and prevent wear on these cutters. According to the milling dosage and the installation position between the tool and the workpiece, the effective working area of the cutting edge of the ball-end milling cutter is calculated. At the same time, a self-lubricating cutter was constructed by using a laser to process micro-textures and filling solid lubricant inside the micro-textures. An analysis was conducted to compare the milling efficiency of bionic microtextured cutters in both dry and micro-lubricated environments. It was found that the self-lubricating tool promoted a 3% to 5% decrease in milling force, a reduction in the coefficient of friction, a high surface finish of the machined workpiece, and an alleviation of chip sticking at the edge area. Full article

Precision edge preparation techniques for cemented carbides enable optimization of the geometry of tools’ cutting edges. These techniques are frequently used in high-stress environments, resulting in substantial improvements in tools’ cutting performance. This investigation examined the impact and evolution of cutting edge parameters and resulting surface finishes as a function of dry-electropolishing time on an end-mill. Findings demonstrate enlargement of the cutting edge radius, a decrease in surface roughness, and the mitigation of defects induced during previous manufacturing stages (i.e., smashed ceramic particles, burrs, chipping, etc.). Additionally, a direct correlation between dry-electropolishing time and primary cutting edges’ micro-geometry parameters has been established. Full article

Rotary Ultrasonic Machining (RUM) stands as a crucial method for machining hard and brittle materials. However, for machining hard-to-machine metal, it continues to face many challenges due to the complex vibration of the milling tool. Flank milling is an efficient method for machining complex parts, such as blisks and impellers, which have been widely used in aerospace field. However, current research is more focused on rotary ultrasonic end milling. In this context, we will study the surface integrity of rotary ultrasonic flank milling 40Cr steel using a self-developed RUM system. We delve into exploring the impacts of tool vibration on surface morphology, residual stress, and micro-hardness of the workpiece under various process parameters. The experimental findings reveal that rotary ultrasonic flank milling, in contrast to traditional flank milling techniques, significantly diminishes the surface roughness by about 40%. The reasons for the reduction of surface roughness are analyzed from the point of view of the cutting force. The surface roughness appears to be notably linked to both the average cutting force and the frequency domain characteristics. In addition, the experimental results indicate that rotary ultrasonic flank milling demonstrates the capacity to elevate the micro-hardness of the machined surface. Full article