Search Results (1,414)

During machining, most of the mechanical energy is converted into heat. A substantial part of this heat is transferred to the cutting tool, causing a rapid rise in tool temperature. Excessive thermal loads accelerate tool wear and lead to displacement of the tool center point, reducing machining accuracy and workpiece quality. This challenge is particularly pronounced when machining titanium alloys. Due to their low thermal conductivity, titanium alloys impose significantly higher thermal loads on the cutting tool compared to conventional carbon steels, making the process more difficult. To reduce temperatures in the cutting zone, cutting fluids are widely employed in titanium machining. They have been shown to significantly extend tool life. Cutting fluids are broadly categorized into cutting oils and water-based cutting fluids. Owing to their distinct thermophysical properties, these fluids exhibit notably different cooling and lubrication performance. However, current research lacks comprehensive cross-comparative studies of different cutting fluid types, which hinders the selection of optimal cutting fluids for process optimization. This study examines the influence of three cutting fluids—emulsion, cutting oil, and synthetic oil-free fluid—on tool wear, temperature, surface quality, and energy consumption during flood-cooled end milling of Ti-6Al-4V. A novel experimental setup incorporating embedded thermocouples enabled real-time temperature measurement near the cutting edge. Tool wear, torque, and surface roughness were recorded over defined feed lengths. Among the tested fluids, emulsion achieved the best balance of cooling and lubrication, resulting in the longest tool life with a feed travel path of 12.21 m. This corresponds to an increase of approximately 200 % compared to cutting oil and oil-free fluid. Cutting oil offered superior lubrication but limited cooling capacity, resulting in localized thermal damage and edge chipping. Water-based cutting fluids reduced tool temperatures by over 300 ${}^{\circ }$C compared to dry cutting but, in some cases, increased notch wear due to higher mechanical stress at the entry point. Power consumption analysis revealed that the cutting fluid supply system accounted for 60–70 % of total energy use, particularly with high-viscosity fluids like cutting oil. Complementary thermal and CFD simulations were used to quantify heat partitioning and convective cooling efficiency. The results showed that water-based fluids achieved heat transfer coefficients up to 175 kW/m²· K, more than ten times higher than those of cutting oil. These findings emphasize the importance of selecting suitable cutting fluids and optimizing their supply to enhance tool performance and energy efficiency in Ti-6Al-4V machining. Full article

Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path accuracy. This study proposed a dynamic robot posture programming method to enhance the stiffness for aluminum alloy milling task. Firstly, a milling force prediction model is established and validated under multiple postures and various milling parameters, confirming its stability and reliability. Secondly, a robot stiffness model is developed by combining system stiffness and milling forces within the milling coordinate system to formulate an optimization index representing stiffness performance in the actual load direction. Finally, considering the constraints of joint limit, singular position and joint motion smoothness and so on, the robot posture in the milling trajectory is dynamically programmed, and the joint angle sequence with the optimal average stiffness from any cutter location (CL) point to the end of the trajectory is obtained. Under the assumption that positioning errors were effectively compensated, the experimental results demonstrated that the proposed method can control both axial and radial machining errors within 0.1 mm at discrete points. For the specific milling trajectory, compared to the single-step optimization algorithm starting from the initial optimal posture, the proposed method reduced the axial error by 12.23% and the radial error by 8.61%. Full article

Chatter vibration in machining operations has been identified as one of the major obstacles to improving surface quality and productivity. Therefore, efficiently and accurately predicting stable cutting regions is becoming increasingly important, especially in high-speed milling processes. In this study, on the basis of a predictor–corrector scheme, the following three error correction methods are developed for milling stability analysis: the Correction Hamming–Milne-based method (CHM), the Correction Adams–Milne-based method (CAM) and the Predictor–Corrector Hamming–Adams–Milne-based method (PCHAM). Firstly, we employ the periodic delay differential equations (DDEs), which are usually adopted to describe mathematical models of milling dynamics, and the time period of the coefficient matrix is divided into two unequal subintervals based on an analysis of the vibration modes. Then, the Hamming method and the fourth-order implicit Adams–Moulton method are separately utilized to predict the state term, and the Milne method is adopted to correct the state term. Based on local truncation error, combining the Hamming and Milne methods creates a CHM that can more precisely approximate the state term. Similarly, combining the fourth-order implicit Adams–Moulton method and the Milne method creates a CAM that can more accurately approximate the state term. More importantly, the CHM and the CAM are employed together to acquire the state transition matrix. Thereafter, the effectiveness and applicability of the three error correction methods are verified by comparing them with three existing methods. The results demonstrate that the three error correction methods achieve higher prediction accuracy without sacrificing computational efficiency. Compared with the 2nd SDM, the calculation times of the CHM, CAM and PCHAM are reduced by around 56%, 56% and 58%, respectively. Finally, verification experiments are carried out using a CNC machine (EMV650) to further validate the reliability of the proposed methods, where ten groups of cutting tests illustrate that the stability lobes predicted by the three error correction methods exhibit better agreement with the experimental results. Full article

This research investigates the multi-objective optimization of micro-milling processes for the titanium alloy Ti-3Al-2.5V (grade 9) through the application of grey relational analysis. The incorporation of nanometer-sized particles in hybrid machining lubricants plays a crucial role in improving heat transfer during machining. The approach aims to increase the efficiency and effectiveness of micro-milling by addressing various performance metrics simultaneously, leading to better machining results for this titanium alloy. Additionally, the integration of nanoparticles into the machining lubricant significantly improves the lubrication properties, reducing friction during the machining process. The study analyzed four machining parameters: machining speed, rate of feed, axial depth of cut, and the weight percentage concentration of hybrid machining lubricants Multi-wall Carbon Nano Tube and Alumina Oxide (MWCNT and Al₂O₃). The machining nanolubricant was formulated by adding 1% and 2% volume concentrations of MWCNT and Al₂O₃ nanoparticles to the industrial machining fluid. In this machining context, the friction between the machining tool and the Ti-3Al-2.5V work piece is a vital factor influencing the output quality. The results demonstrate that the chosen machining parameters and machining lubricants have a direct impact on the coefficient of friction and surface roughness. The study concludes that utilizing machining nanolubrication for machining Ti-3Al-2.5V (grade 9) significantly enhances the quality compared with traditional machining lubricants. Full article

Corner radius end mills (CREMs) are widely used in machining due to their unique tool geometry, which improves surface quality. Variations in cutting force during machining significantly impact machining quality. Therefore, precisely predicting cutting forces is critical for controlling machining chatter and enhancing accuracy. Traditional element force models have complex formulas and high computational demands when considering tool runout. This paper proposes a hybrid prediction model for CREMs that integrates the separate-edge-forecast method and the BP neural network. The integration approach incorporates runout effects into cutting force coefficients and addresses nonlinear effects from runout. The accuracy of the cutting force prediction model was validated through side milling on 7075 aluminum alloy. The results indicate that the maximum error between the predicted and measured forces is 9.43%, demonstrating that this model ensures high prediction accuracy while reducing computation cost. Full article

In flank milling of thin-walled workpieces, machining deformation is a key issue affecting workpiece accuracy and process stability. Although the traditional finite element method (FEM) offers high accuracy, its low computational efficiency makes it difficult to meet the requirements for rapid prediction in engineering practice. For this purpose, this paper proposes an efficient method for predicting workpiece deformation based on the voxel octree model. First, based on the analysis of the contact position between the cutting tool and the workpiece, the thin-walled workpiece is divided into six levels of voxel units, using a voxel octree model. Then, the stiffness matrix and update model of the voxel units are established. Finally, the deformation prediction is completed by calculating the micro-milling force and the voxel stiffness matrix. The experimental results show that the workpiece deformation predicted by the proposed method is highly consistent with the actual machining measurement. At the same time, compared with traditional FEM and voxel model methods, the calculation time is reduced by 90% and 13.2%, respectively. This method can provide rapid decision support for the optimization of thin-walled workpiece machining processes and effectively improve the efficiency of preliminary research in actual machining. Full article

Bio-based surfactants have demonstrated significant potential as economically viable and environmentally sustainable alternatives to petroleum-derived surfactants, with the global biosurfactant market expanding from USD 4.41 billion in 2023 to a projected USD 6.71 billion by 2032, representing a compound annual growth rate of 5.4%. While conventional surfactants such as alkyl aryl sulfates and alkyl benzene sulfonates exhibit extremely high aquatic toxicity and impose substantial ecological costs, biosurfactants including lipopeptides (surfactin, iturin, fengycin, lichenysin) produced by Bacillus species and glycolipids (rhamnolipids, sophorolipids, trehalose lipids, mannosylerythritol lipids) from Pseudomonas demonstrate superior biodegradability. However, current biosurfactant production costs, ranging from 5 to20 USD/kg, cannot compete effectively with synthetic surfactants, averaging approximately 2 USD/kg, necessitating comprehensive process improvements to achieve commercial viability. The utilization of renewable agricultural feedstocks containing 65–70% carbohydrates, including corn stover, sugarcane bagasse, rice bran, and palm oil mill effluent, has achieved production costs as low as 3.8 USD/kg through advanced optimized pretreatment technologies, enzyme catalysis, simultaneous saccharification and fermentation (SSF), and downstream processes, resulting in cost reductions compared to conventional methods. The implementation of artificial intelligence and machine learning algorithms for bioprocess optimization enables simultaneous optimization of genetic engineering, metabolic pathways, and fermentation parameters, achieving yield improvements and cost reductions, with projections indicating production costs below 2.50 USD/kg being needed in the next decade to achieve cost parity with synthetic surfactants, maintaining economic viability. Full article

Material extrusion (MEX) is an additive manufacturing process used for 3D printing thermoplastic-based polymers, including single polymers, blends, and reinforced polymer composites (RPCs). RPCs are highly valued in various industries for their exceptional properties. The surface finish of RPC MEX-printed parts is high due to the process-related layering nature and the materials’ properties. This study explores RPC development for MEX printing and the potential of dry milling post-processing to enhance the MEX-printed part’s surface quality. RPC MEX filaments were developed by incorporating graphene nanoplatelets (GNPs) and/or recycled-carbon fibers (rCFs) into a polylactic acid (PLA) matrix. The filaments, including pure PLA and various GNPs-PLA composites, rCF-PLA, and rCF-GNPs-PLA, were developed through ball mill mixing and melt extrusion. Tensile tests were performed to assess the mechanical properties of the developed materials. Dry milling post-processing was carried out to assess the machinability, with the aim of enhancing the MEX-printed part’s surface quality. The results revealed that adding GNPs into PLA showed no considerable enhancements in the tensile properties of the fabricated RPCs, which is contrary to several existing studies. Dry milling showed an enhanced surface quality of MEX-printed parts in terms of surface roughness (Sa and Sz) and the absence of defects such as delamination and layer lines. Adding GNPs into PLA facilitated the dry machining of PLA, resulting in reduced surface asperities compared to pure PLA. Also, there was no observation of pulled-out, realigned, or naked rCFs, which indicates good machinability. Adding GNPs also suppressed the formation of voids around the rCFs during the dry milling. This study provides insights into machining 3D-printed polymer composites to enhance their surface quality. Full article

The process of penetration of lubricants into the frictional interface in steel machining such as turning and milling is not well revealed, which has thus compromised their machining performance. In this paper, the penetration characteristics of deionized water (Di-water) containing different electro-osmosis additives were investigated using a steel-on-steel friction pair. The worn surface lubricated with water solutions were examined using advanced surface analysis techniques. The results indicate that water lubricants were electrically driven to the frictional interface for lubrication. The addition of positive electro-osmosis additives helped promote the penetration of water solutions, thus resulting in the formation of a thick lubricating film of iron oxide at the sliding surface. This reduced abrasion damage significantly, therefore producing a lower coefficient of friction (COF) and wear loss in comparison with pure water. Full article

The aim of this study was to investigate microstructure and the mechanical and operational characteristics of thick and thin walls 3D-built by electron beam additive manufacturing (EBAM). In addition, the milling parameters (rotation speed, feed, and cutting width) were optimized based on simultaneous assessments of Ra roughness on the machined surfaces and material removing rate values. The wall dimensions did not exert a noticeable effect on their chemical compositions, as compared with the original wires used for 3D printing. In comparison, the strength characteristics of the wrought steel (cold-rolled plate) were higher due to finer grains, with both ferrite content and dislocation density being greater as well. In the 3D building process, multiple thermal cycles gave rise to the formation of elongated columnar grains, reducing the strength characteristics. The corrosion rate of the wrought steel was almost twice those of the 3D-printed blanks because of the higher content of both ferrite and twins. By assessing the machinability of the EBAM-built blanks using the stationary milling machine, the cutting forces were comparable due to similar mechanical properties (including microhardness). To improve the removing rate values and reduce the cutting forces, it is recommended to enhance the cutting speeds while not increasing the feeds. For the semi-industrial milling machine, both linear multiple regression and nonlinear neural network models were applied. An integrated approach was proposed that rationally determined both additive manufacturing and post-processing parameters based on a combination of express assessment and analysis of the mechanical, operational, and technological characteristics of built products within a single laboratory complex. Full article

Regenerative chatter is an unfavorable phenomenon that severely affects machining efficiency and surface finish in milling operations. The prediction of chatter stability is an important way to obtain the stable cutting zone. Based on implicit multistep schemes, this paper presents the third-order and fourth-order implicit exponentially fitted methods (3rd IEM and 4th IEM) for milling stability prediction. To begin with, the delay differential equations (DDEs) with time-periodic coefficients are employed to describe the milling dynamics models, and the principal period of the coefficient matrix is firstly decomposed into two different subintervals according to the cutting state. Subsequently, the fourth-step and fifth-step implicit exponential fitting schemes are applied to more accurately estimate the state term. Two benchmark milling models are utilized to illustrate the effectiveness and advantages of the high-order implicit exponentially fitted methods by making comparisons with the three typical existing methods. Under different radial immersion conditions, the numerical results demonstrate that the 3rd IEM and the 4th IEM exhibit both faster convergence rates and higher prediction accuracy than the other three existing prediction methods, without much loss of computational efficiency. Finally, in order to verify the feasibility of the 3rd IEM and the 4th IEM, a series of experimental verifications are conducted using a computer numerical control machining center. It is clearly visible that the stability boundaries predicted by the 3rd IEM and the 4th IEM are mostly consistent with the cutting test results, which indicates that the proposed high-order exponentially fitted methods achieve significantly better prediction performance for actual milling processes. Full article

An in vitro study evaluated the fracture resistance of four CAD/CAM restorative materials: lithium disilicate ceramic (IPS e.max CAD, EM), hybrid ceramic (Vita Enamic, VE), a polymer-based composite (Cerasmart, CS), and a novel 3D-printed resin (Ceramic Crown, CC) fabricated using digital press stereolithography (DPS) technology. Standardized full-coverage crowns were designed and manufactured for each material. All specimens underwent thermocycling and fracture testing using a universal testing machine. EM exhibited the highest fracture resistance (mean: 440.49 N), while VE showed the lowest (173.82 N). CS (265.49 N) and CC (306.76 N) presented intermediate values without statistically significant differences between them. Stereomicroscopic analysis revealed differences in fracture patterns, with IPS e.max CAD showing smooth, brittle fractures, while hybrid and polymer-based materials exhibited tortuous fracture surfaces. These results suggest that DPS technology achieves mechanical performance for Ceramic Crown comparable to that of milled polymer-based composites, while offering production advantages in terms of time efficiency. As one of the first studies to evaluate Ceramic Crown and DPS technology, these findings provide initial insights into their mechanical behavior. However, further studies are required to validate their clinical performance before widespread use can be recommended. Full article

Jet electrochemical milling (JECM) offers significant advantages for fabricating fine grooves and slits in thin-walled, low-rigidity, and heat-sensitive metallic materials, such as amorphous alloys, owing to its operational flexibility, lack of material constraints, and superior surface quality. Nevertheless, conventional JECM techniques for groove machining encounter limitations including excessive overcut, restricted ability to produce microstructures with high depth-to-width ratios, and reduced machining accuracy. To address these issues, this study proposes an innovative approach termed the periodically thinning jet electrochemical milling (PT-JECM) method. This method involves initially generating a shallow microgroove through a single pass using the original nozzle diameter, followed by successive milling passes with progressively smaller nozzle diameters based on the preformed groove. Comparative analysis with traditional JECM methods reveals that this strategy significantly improves the etching factor from 1.896 to 4.318, corresponding to a 128% enhancement. Furthermore, it markedly decreases the slot width increase from 275 μm to 1 μm and improves the aspect ratio from 0.51 to 0.83, representing a 63% increase, enabling the precision machining of large aspect ratio holes and slot structures. Full article

While the machining of Inconel 718 has been widely studied, its cast counterpart Inconel 713LC remains underexplored, despite its relevance in high-temperature aerospace and energy components. This work presents a comprehensive investigation of dry milling behavior in Inconel 713LC, focusing on the interplay between tool wear, cutting forces, surface integrity, and chip formation across a broad range of cutting parameters. A stable process window was identified: 30–50 m/min cutting speed and 0.045–0.07 mm/tooth feed, where surface roughness remained below Ra 0.6 µm and tool life exceeded 10 min. Outside this window, rapid thermal and mechanical degradation occurred, leading to flank wear beyond the 550 µm limit and unstable chip morphology. The observed trends align with those in Inconel 718, allowing the cautious transfer of established strategies to cast alloys. By quantifying key process–performance relationships and validating predictive models for tool life and cutting forces, this study provides a foundation for optimizing the dry machining of cast superalloys. The results advance sustainable manufacturing practices by reducing reliance on cutting fluids while maintaining surface and dimensional integrity in demanding applications. Full article

As the core component of high-performance mechanical transmission devices, semi-open impellers are widely used in aerospace, automobiles, and high-end industrial manufacturing. Their complex structures and variable blade shapes have extremely high requirements for machining accuracy and surface quality. However, at present, traditional manual programming for machining generally has problems of relatively low machining efficiency and machining accuracy. Therefore, we explored the application of the Siemens NX (UG NX12.0) software platform in modeling and process programming for machining semi-open impellers and designed an efficient machining strategy by combining rough machining, semi-finishing machining, and finishing machining in an optimized manner. By using the advanced functions of UG NX, the whole process from three-dimensional modeling and process planning to tool path generation was formulated, and machining verification was carried out on the Mikron Mill E500U five-axis machining center. The process plan significantly improved machining efficiency and optimized surface quality. After verification, the machining efficiency increased by more than 30%, and the machining accuracy was significantly improved, fully verifying the superiority and practicability of the UG NX platform in the machining of complex curved surface parts. Full article