Search Results (1,381)

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

Barrel tools are increasingly used in high-precision machining of free-form surfaces. However, limited studies evaluate their performance specifically on spherical geometries, where tool–surface contact characteristics differ significantly. Understanding how tool geometry and process parameters influence surface quality and cutting forces in such cases remains underexplored. This study evaluates how barrel cutter radius and varying machining parameters affect cutting forces and surface roughness when milling internal and external spherical surfaces. Machining tests were conducted on structural steel 1.1191 using two barrel cutters with different curvature radii (85 mm and 250 mm) on a 5-axis CNC machine. Feed per tooth and radial depth of cut were systematically varied. Cutting forces were measured using a dynamometer, and surface roughness was assessed using the Rz parameter, which is more sensitive to peak deviations than Ra. Novelty lies in isolating spherical surface shapes (internal vs. external) under identical path trajectories and systematically correlating tool geometry to force and surface metrics. The larger curvature tool (250 mm) consistently generated up to twice the cutting force of the smaller radius tool under equivalent conditions. External surfaces showed higher Rz values than internal ones due to less favorable contact geometry. Radial depth of the cut had a linear influence on force magnitude, while feed rate had a limited effect except at higher depths. Smaller-radius barrel tools and internal geometries are preferable for minimizing cutting forces and achieving better surface quality when machining spherical components. The aim of this paper is to determine the actual force load and surface quality when using specific cutting conditions for internal and external spherical machined surfaces. Full article

Helical milling presents a promising alternative to conventional drilling for hole production, offering superior surface quality and improved production efficiency. While this technique has been extensively applied in the aerospace industry, its potential for machining common engineering materials, such as AISI 1045 steel, remains underexplored in the literature. This study addresses this gap by systematically evaluating the influence of key process parameters—cutting speed (V_c), axial depth of cut (a_p), and tool diameter (D_t)—on hole quality attributes, including surface roughness, burr formation, and nominal diameter accuracy. A full factorial experimental design (2³) was employed, coupled with analysis of variance (ANOVA), to quantify the effects and interactions of these parameters. The results reveal that, with a higher V_c, it is possible to reduce surface roughness (R_a) by 30% to 40%, while an increased a_p leads to a 50% increase in R_a. Additionally, D_t emerged as the most critical factor for nominal diameter accuracy, reducing geometrical errors by 1% with a larger D_t. Burr formation was predominantly observed at the lower end of the hole, highlighting challenges specific to this technique. These findings provide valuable insights into optimizing helical milling for low-carbon steels, offering a foundation for broader industrial adoption and further research. Full article

In this study, the tool axis inclination angle with respect to the surface normal is used to compare the wear of spherical and toroidal milling cutters. Five different surface types were machined in this investigation using three toroidal and three spherical milling cutters at inclination angles of 15°, 35°, and 55°. Each cutter processed 45 surfaces, nine of each type. The MM1-200 microscope was used to monitor tool wear under a microscope after the surfaces had been machined. A comparison diagram and microscopic pictures of tool wear in relation to the wear surface are presented in the paper. Additionally, it contrasts the tool edge’s state at the conclusion of machining with its state following five years of storage, during which time the tool was subjected to environmental influences. Full article

In recent years, much research has been devoted to the evaluation of physical phenomena and the technological effects of precise and micromilling processes. However, the available current literature lacks synthetic work covering the current state of the art regarding cutting force–tool displacement interactions in precise and micromilling manufacturing systems. Therefore, this literature review aims to fill this research gap and focuses on the critical literature review regarding the current state of the art within the prediction methods of cutting forces and machining system’s displacements/vibrations during precise and micromilling techniques. In the first part, a currently available cutting force, as well as the static and dynamic machining system displacement models applied in precise and micromilling conditions are presented. In the next stage, a relationship between the geometrical elements of cut and generated cutting forces and tool displacements are discussed, based on the recent literature. A subsequent part concerns the formulation of the generalized analytical models for a prediction of cutting forces and vibrations during precise and micromilling conditions. In the last stage, the conclusions and outlook are formulated based on the conducted analysis of the literature. In this context, this paper constitutes a synthetic work presenting current trends in the prediction of precise milling and micromilling mechanics. Full article

The escalating global demand for fresh water, driven by urbanization and industrial growth, underscores the need for sustainable water management, particularly in the water-intensive construction sector. Although prior studies have primarily concentrated on treated wastewater, the practical viability of utilizing untreated wastewater has not been thoroughly investigated—especially in developing nations where treatment expenses frequently impede actual implementation, even for non-structural uses. While prior research has focused on treated wastewater, the potential of untreated or partially treated wastewater from diverse industrial sources remains underexplored. This study investigates the feasibility of incorporating wastewater from textile, sugar mill, service station, sewage, and fertilizer industries into concrete paver block production. The novelty lies in a dual approach, combining experimental analysis with XGBoost-based machine learning (ML) models to predict the impact of key physicochemical parameters—such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Hardness—on mechanical properties like compressive strength (CS), water absorption (WA), ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DME). The ML models showed high predictive accuracy for CS (R² = 0.92) and UPV (R² = 0.97 direct, 0.99 indirect), aligning closely with experimental data. Notably, concrete pavers produced with textile (CP-TXW) and sugar mill wastewater (CP-SUW) attained 28-day compressive strengths of 47.95 MPa and exceeding 48 MPa, respectively, conforming to ASTM C936 standards and demonstrating the potential to substitute fresh water for non-structural applications. These findings demonstrate the viability of using untreated wastewater in concrete production with minimal treatment, offering a cost-effective, sustainable solution that reduces fresh water dependency while supporting environmentally responsible construction practices aligned with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Additionally, the model serves as a practical screening tool for identifying and prioritizing viable wastewater sources in concrete production, complementing mandatory laboratory testing in industrial applications. Full article

As manufacturing demands for challenging-to-machine metallic materials continue to evolve, the performance of cutting tools has emerged as a critical limiting factor. The synergistic application of micro-texture and coating in cutting tools can improve various properties. For the processing of existing micro-texture, because of the fast cooling and heating processing method of laser, there are defects such as remelted layer stacking and micro-cracks on the surface after processing. This study introduces a preheating-assisted technology aimed at optimizing the milling performance of textured coated tools. A milling test platform was established to evaluate the performance of these tools on titanium alloys under thermally assisted conditions. The face-centered cubic response surface methodology, as part of the central composite design (CCD) experimental framework, was employed to investigate the interaction effects of micro-texture preparation parameters and thermal assistance temperature on milling performance. The findings indicate a significant correlation between thermal assistance temperature and tool milling performance, suggesting that an appropriately selected thermal assistance temperature can enhance both the milling efficiency of the tool and the surface quality of the titanium alloy. Utilizing the response surface methodology, a multi-objective optimization of the textured coating tool-preparation process was conducted, resulting in the following optimized parameters: laser power of 45 W, scanning speed of 1576 mm/s, the number of scans was 7, micro-texture spacing of 130 μm, micro-texture diameter of 30 μm, and a heat-assisted temperature of 675.15 K. Finally, the experimental platform of optimization results is built, which proves that the optimization results are accurate and reliable, and provides theoretical basis and technical support for the preparation process of textured coating tools. It is of great significance to realize high-precision and high-quality machining of difficult-to-machine materials such as titanium alloy. Full article

Successful dental implant therapy relies on accurate planning and placement, e.g., through static, computer-aided implant surgery using CAD/CAM-fabricated surgical guides. This study examined production methods’ influence on surgical guide sleeve housing geometry. A model with two edentulous spaces was digitized using intraoral scanning and CBCT, and two virtually positioned implants were planned. Ten guides per group were produced using milling (MCX5), DLP printing (ASIGA and SHERA), and SLA printing (FORM), printing with 50 µm and 100 µm layers each. Each guide (n = 70) was then digitized using an industrial scanner before and after sterilization. Superimposition of the actual guide data with the reference data allowed for evaluation of deviations at the drill sleeve housing along the x-, y-, z-, and dxyz-axes. Descriptive and statistical evaluation was performed (significance level: p ≤ 0.0125). Significant differences existed among the production methods: Milling and SLA showed higher deviations than the DLP group (p < 0.001). Milled guides post-sterilization showed the highest deviations (0.352 ± 0.08 mm), while one DLP printer at 50 μm layer thickness showed lowest deviations (0.091 ± 0.04 mm). The layer thickness was insignificant, whereas sterilization increased deviation (p < 0.001). DLP produced the most precise implant surgical guides. All 3D printers were suitable for fabricating clinically acceptable surgical guides. Full article

This paper presents a CFD-based method for the aerodynamic design of a high-pressure compressor rotor blisk, taking into account manufacturing constraints. Focus is placed on the influence of geometric deviations caused by the dynamic constraints of the milling machine. Special attention is given to the leading edge region of the blade, where high curvature results in increased sensitivity in both aerodynamic behavior and manufacturability. The generic blisk geometry on which this study is based is characterized by an elliptical leading edge. For the optimization, the leading edge is described by Bézier curves that transition smoothly to the suction and pressure sides with continuous curvature and a non-dimensional length ratio. In steady-state RANS parameter studies, the length ratio is systematically varied while the chord length is kept constant. For the aerodynamic evaluation of the design’s key performance parameters such as blade pressure distribution, total pressure loss and compressor efficiency are considered. To evaluate the machine dynamics for a given design, compliance with the nominal feed rate and the deviation between the planned and actual tool tip positions were used as evaluation parameters. Compared to the reference geometry with an elliptical leading edge, the purely aerodynamic optimization achieved an isentropic efficiency improvement of +0.24 percentage points in the aerodynamic design point and a profile deviation improvement of $-3$$\mu$m in the 99th quantile. The interdisciplinary optimization achieved an improvement of +0.20 percentage points and $-30$$\mathrm{\mu }$m, respectively. This comparative study illustrates the potential of multidisciplinary design approaches that balance aerodynamic performance goals with manufacturability via a novel approach for Design-to-Manufacture-to-Design. Full article

The use of aluminum honeycomb structures is fast expanding in advanced sectors such as the aeronautics, aerospace, marine, and automotive industries. However, processing these structures represents a major challenge for producing parts that meet the strict standards. To address this issue, an innovative manufacturing method using longitudinal ultrasonic vibration-assisted cutting, combined with a CDZ10 hybrid cutting tool, was developed to optimize the efficiency of traditional machining processes. To this end, a 3D numerical model was developed using the finite element method and Abaqus/Explicit 2017 software to simulate the complex interactions among the cutting tool and the thin walls of the structures. This model was validated by experimental tests, allowing the study of the influence of milling conditions such as feed rate, cutting angle, and vibration amplitude. The numerical results revealed that the hybrid technology significantly reduces the cutting force components, with a decrease ranging from 10% to 42%. In addition, it improves cutting quality by reducing plastic deformation and cell wall tearing, which prevents the formation of chips clumps on the tool edges, thus avoiding early wear of the tool. These outcomes offer new insights into optimizing industrial processes, particularly in fields with stringent precision and performance demands, like the aerospace sector. Full article

Next to solid wood, laminated particleboard is the most widely used wood-based material in the furniture industry. Ensuring the high quality of the laminate surface after machining is of critical importance for furniture manufacturers, particularly prior to the edge banding process, as this process significantly influences the final aesthetic and functional quality of panel elements. The objective of this review article is to gather and evaluate the current state of knowledge regarding the influence of machining process parameters and the physical and mechanical properties of laminated particleboard on machining quality. Particular emphasis is placed on the occurrence of laminate damage, commonly referred to as delamination, a prevalent defect in the furniture manufacturing sector. Both categories of influencing factors—process-related and material-related—are analyzed within the context of the three primary technological processes employed in the woodworking industry, namely drilling, cutting, and milling. The analysis revealed that a persistent research gap concerns the relationship between machining quality and material parameters, particularly in the case of milling—a process of critical importance in the furniture industry. Full article

Nickel-based superalloy Hastelloy C276 is widely used in high-performance industries due to its strength, corrosion resistance, and thermal stability. However, these same properties pose substantial challenges in machining, resulting in high tool wear, surface defects, and dimensional inaccuracies. This study investigates methods to enhance machining performance and surface quality by evaluating the tribological behavior of TiSiN/TiAlN-coated carbide inserts under six cooling and lubrication conditions: dry, MQL with coconut oil, Cryo-LN₂, Cryo-LCO₂, MQL–Cryo-LN₂, and MQL–Cryo-LCO₂. Open-slot finishing was performed at constant cutting parameters, and key indicators such as cutting zone temperature, tool wear, surface roughness, chip morphology, and microhardness were analyzed. The hybrid MQL–Cryo-LN₂ approach significantly outperformed other methods, reducing cutting zone temperature, tool wear, and surface roughness by 116.4%, 94.34%, and 76.11%, respectively, compared to dry machining. SEM and EDS analyses confirmed abrasive, oxidative, and adhesive wear as the dominant mechanisms. The MQL–Cryo-LN₂ strategy also lowered microhardness, in contrast to a 39.7% increase observed under dry conditions. These findings highlight the superior performance of hybrid MQL–Cryo-LN₂ in improving machinability, offering a promising solution for precision-driven applications. Full article

In this work, the development of Al-based metal matrix composites (MMCs) is achieved using hybrid SiC and ZrO₂ reinforcement particles for automotive applications. Powder metallurgy (PM) is employed with various combinations of important process parameters for the fabrication of MMCs. MMCs were characterized using scanning electron microscopy (SEM), X-ray diffractometry (XRD), and a microhardness study. All XRD graphs adequately exhibit Al, SiC, and ZrO₂ peaks, indicating that the hybrid MMC products were satisfactorily fabricated with appropriate mixing and sintering at all the considered fabrication conditions. Also, no impurity peaks were observed, confirming high composite purity. MMC products in all the XRD patterns, suitable for the desired applications. According to the SEM investigation, SiC and ZrO₂ reinforcement components are uniformly scattered throughout Al matrix in all produced MMC products. The occurrence of Al, Si, C, Zr, and O in EDS spectra demonstrates the effectiveness of composite ball milling and sintering under all manufacturing conditions. Moreover, an increase in interfacial bonding of fabricated composites at a higher sintering temperature indicated improved physical properties of the developed MMCs. The highest microhardness value is 86.6 HVN amid all the fabricated composites at 7% silica, 14% zirconium dioxide, 500° sintering temperature, 90 min sintering time, and 60 min milling time. An integrated Particle Swarm Optimization–Support Vector Machine (PSO-SVM) model was developed to predict microhardness based on the input parameters. The model demonstrated strong predictive performance, as evidenced by low values of various statistical metrics for both training and testing datasets, highlighting the PSO-SVM model’s robustness and generalization capability. Specifically, the model achieved a coefficient of determination of 0.995 and a root mean square error of 0.920 on the training set, while on the testing set, it attained a coefficient of determination of 0.982 and a root mean square error of 1.557. These results underscore the potential of the PSO-SVM framework, which can be effectively leveraged to optimize process parameters for achieving targeted microhardness levels for the developed Al-SiC-ZrO₂ Composites. Full article

Ball-end milling cutters are normally used for complex surface machining. During the milling process, the tool posture and cutting parameters of the ball-end milling cutters have a significant impact on chip formations and morphological changes. Based on the Cutter Workpiece Engagement (CWE) model, this study establishes a chip flow model for ball-end milling cutters with consideration of the tool posture variation. The machining experiments of Ti-6Al-4V with a 15° inclined plane and different feed directions were carried out. The influence mechanism of time-varying tool posture on chip formation was systematically investigated. The results reveal an interaction between the chip flow direction and the cutting velocity direction. The included angle between the chip flow directions at the maximum and minimum contact points in the CWE area affects the degree of chip curling, with a smaller angle leading to weaker curling. This research provides a theoretical foundation for the optimization of posture parameters of ball-end milling cutters and expounds on the influence of the chip flow angle on chip deformation. Full article

Background/Objectives: The integration of machine learning (ML) and artificial intelligence (AI) has revolutionized the pharmaceutical industry by improving drug discovery, development and manufacturing processes. Based on literature data, an ML model was developed by our group to predict the formation of binary co-amorphous systems (COAMSs) for inhalation therapy. The model’s ability to develop a dry powder formulation with the necessary properties for a predicted co-amorphous combination was evaluated. Methods: An extended experimental validation of the ML model by co-milling and X-ray diffraction analysis for 18 API-API (active pharmaceutical ingredient) combinations is presented. Additionally, one COAMS of rifampicin (RIF) and ethambutol (ETH), two first-line tuberculosis (TB) drugs are developed further for inhalation therapy. Results: The ML model has shown an accuracy of 79% in predicting suitable combinations for 35 APIs used in inhalation therapy; experimental accuracy was demonstrated to be 72%. The study confirmed the successful development of stable COAMSs of RIF-ETH either via spray-drying or co-milling. In particular, the milled COAMSs showed better aerosolization properties (higher ED and FPF with lower standard deviation). Further, RIF-ETH COAMSs show much more reproducible results in terms of drug quantity dissolved over time. Conclusions: ML has been shown to be a suitable tool to predict COAMSs that can be developed for TB treatment by inhalation to save time and cost during the experimental screening phase. Full article