To enhance the sustainability of manufacturing, various clean cutting technologies have been developed, yet their sustainability assessment faces challenges in balancing multiple conflicting objectives and stakeholder interests. This paper proposes a game theory-based evaluation framework that treats environmental, technical, economic, and social dimensions as cooperative players. The Nash equilibrium model is employed to dynamically reconcile subjective weights from the analytic hierarchy process and objective weights from the entropy method, thus achieving optimal weight allocation. Experimental studies on Ti-6Al-4V titanium alloy milling compared dry milling, minimum quantity lubrication, and cryogenic minimum quantity lubrication (CMQL) under different parameters. Results demonstrate that the game-theoretic model effectively integrates preferences and achieves Nash equilibrium. CMQL showed superior performance, increasing tool life by approximately 40% and reducing surface roughness by about 25% compared to dry milling. Coated inserts reduced carbon emissions by nearly 30% versus end mills. The Nash equilibrium analysis demonstrates that dry milling with coated inserts attains the highest level of processing sustainability under high-speed conditions due to synergistic environmental and economic advantages, while simultaneously revealing practical trade-offs among competing objectives. This study confirms that the proposed framework enables scientific weight coordination and provides a quantifiable, interpretable decision-making system for sustainable process selection.

Tungsten carbide, owing to its high hardness, compressive strength and wear resistance, is widely used in precision molds and wear-resistant components; however, these properties also make its machining highly challenging. Polycrystalline diamond (PCD), as a superhard tool material, possesses excellent mechanical properties and shows great potential for machining such difficult-to-cut materials. Among the factors influencing the performance of PCD tools, diamond grain size is particularly critical, yet its effect on tool wear mechanisms in tungsten carbide machining has been insufficiently studied. In this study, milling experiments on tungsten carbide were carried out using PCD tools of three grain sizes to examine their effect on tool wear. The results indicated that the wear rate decreased as the diamond grain size increased. Tool wear was mainly characterized by large-scale spalling on the rake face, as well as adhesive and abrasive wear on the flank face. It was further inferred that the large-area spalling of the rake face was associated with the cobalt-catalyzed phase transformation of diamond grains at elevated temperatures and the scraping of the chips. In addition, abrasive wear was strongly dependent on the diamond grain size: tools with finer grains were dominated by intergranular fracture, whereas those with coarser grains tended to transgranular fracture. This behavior was primarily attributed to the direct mechanical impacts between the diamond grains of the tool and the WC grains of the workpiece during machining.

WC-Co tungsten carbide is widely used in precision molds and wear-resistant components owing to its high hardness and wear resistance; however, its inherent brittleness poses significant challenges for the efficient precision machining of complex geometries. In ball-end milling of WC-Co, the continuously varying toolworkpiece contact conditions give rise to pronounced three-dimensional dynamic cutting characteristics, causing ductile deformation and brittle fracture to occur alternately within a single process. To overcome the low efficiency of conventional ductile-regime machining, a cross-mode milling strategy is proposed, in which a ductile-quality surface is achieved through the coordinated action of ductile and controlled brittle removal via appropriate parameter selection. Milling experiments were conducted on WC-15Co using polycrystalline diamond (PCD) ball-end mills to investigate the evolution of cutting forces and surface generation under varying feed per tooth and axial depth of cut. The critical parameter range for cross-mode milling and its influence on surface quality were systematically evaluated. The results show that the cutting forces increase with feed per tooth, and that in subsequent passes, the reduced chip thickness shifts the removal mode toward a combination of ploughing and micro-cutting. The critical feed per tooth for cross-mode milling was identified to be approximately 15.5 mu m. Even when operating below this threshold, an excessive axial depth of cut can still induce WC grain fracture, leading to grain pull-out and micro-fractured surfaces. Consequently, the surface roughness first decreases and then increases with axial depth of cut. These findings provide practical guidance for the high-efficiency, high-quality cross-mode milling of WC-Co components.

The inherently low dynamic stiffness of industrial robots presents a fundamental obstacle to their full potential in high-precision milling applications, such as those in the aerospace sector. The resulting vibrations severely compromise machining quality and limit production efficiency. To address this problem, a high-bandwidth piezoelectric active damping toolholder is developed as a local micro-actuation unit for robotic milling. Distinct from conventional end-effector designs, the proposed toolholder adopts a local dynamic design strategy to reduce interference between the toolholder dynamics and the pose-dependent structural modes of the robot. Furthermore, a model-independent multi-harmonic adaptive feedforward control strategy is introduced to suppress spindle-related vibration harmonics without requiring real-time identification of the robot system. To evaluate the effectiveness and robustness of the proposed approach, milling experiments were conducted on two distinct industrial robot platforms under multiple validation scenarios. Initial validation on an ESTUN ER220 robot, machining a 5A06 aluminum alloy aerospace panel prototype, demonstrates maximum reductions in the root-mean-square values of vibration acceleration at the toolholder in the X, Y, and Z directions of 45.10%, 49.74%, and 50.61%, respectively, together with a surface roughness Sa reduction of up to 64.1%. Subsequent experiments on a KUKA KR500 heavy-duty robot further confirmed the effectiveness of the proposed toolholder under representative, posture-varying, chatter-prone, and variable-condition milling scenarios. The results support the robustness and cross-platform applicability of the proposed local active damping approach for improving vibration behavior and surface quality in robotic milling.

Developing an accurate and reliable prediction model for machine tool energy consumption is crucial for effective energy management and process optimization in machining processes. However, such models typically require large amounts of energy consumption data and additional sensor signals for training due to the complexity of machining parameters, which substantially increases the time and economic costs associated with developing and deploying energy prediction models. To address these challenges, this study proposes a Fully Connected-Radial Basis Function (FC-RBF) model and a Machining Processes-based Virtual Sample Generation (MP-VSG) method for predicting machine tool energy consumption from small and cost-effective datasets. The FC-RBF model achieves high-precision predictions under limited training data, while the MP-VSG method effectively generates representative virtual samples to mitigate information scarcity in small datasets. Experimental results based on machine tool energy consumption data demonstrate the effectiveness of the proposed approach. The integrated FC-RBF and MP-VSG framework achieves a Mean Absolute Percentage Error (MAPE) of 6.67

Tool-axis orientation is a critical factor affecting cutting mechanics and surface integrity in ball-end milling of tungsten carbide. However, its influence on material removal behavior and subsurface microstructural evolution remains unclear. Therefore, dry ball-end milling experiments were conducted on WC-15Co tungsten carbide using a PCD cutter under different lead and tilt angle conditions. The results show that the lead angle and tilt angle influence machined surface-layer formation through different mechanisms. As the lead angle increases, the effective cutting region gradually shifts away from the tool tip, increasing the minimum local cutting velocity and promoting the transition from ploughing-dominated deformation to stable shearing. Consequently, Fx and Fy decrease, and machining-induced plastic deformation is suppressed within an appropriate lead angle range. In contrast, the tilt angle primarily changes the direction of the cutting velocity and the tool-workpiece contact condition. An increase in the tilt angle causes the local cutting velocity in the finally generated surface region to deviate toward the workpiece normal direction, thereby strengthening the normal contact interaction between the cutting edge and the workpiece. Consequently, the Fx and Fz force components increase significantly, producing a stronger three-dimensional compressive stress state within the machined surface layer. Under large tilt angle conditions, the intensified normal compressive effect promotes local orientation fragmentation, grain breakage, and crack initiation, thereby aggravating subsurface damage.

Tungsten carbide (WC) exhibits high hot hardness and excellent abrasion resistance, making it widely employed in mold manufacturing, aerospace, and precision component production. However, its extreme hardness leads to severe tool wear during cutting, which limits both machining efficiency and surface quality. Therefore, understanding the mechanisms of cutting tool wear and their evolution during tungsten carbide turning is critical for improving machining accuracy and tool life. In this study, dry orthogonal turning experiments were conducted on WC-15Co using a polycrystalline diamond (PCD) tool to investigate the effects of machining parameters and material removal volume on tool wear from a thermo-mechanical coupling perspective. The results revealed that cutting temperature was more sensitive to cutting speed than to feed. Cutting forces were influenced by both material thermal softening and tool wear, resulting in a decreasing trend in the tangential force with increasing cutting speed, while the feed force initially decreased and subsequently increased. At low material removal volumes, the pronounced thermal softening combined with minimal initial tool wear yielded optimal surface quality at a cutting speed of 250 m/min. However, in terms of tool life, a cutting speed of 100 m/min provided a better thermo-mechanical balance, resulting in minimal wear and stable surface quality. Under high-speed cutting conditions (>= 350 m/min), chips transformed from discontinuous to continuous due to thermal softening and the extrusion effect induced by tool wear, which adversely affected surface quality. The wear behavior of PCD tools was strongly dependent on cutting speed: at lower speeds, adhesion and abrasive wear were dominant, whereas at higher speeds, rapid tool failure occurred due to diamond graphitization, severe oxidation, and spalling of large adhered layers. This study elucidates the mechanisms by which thermo-mechanical interactions influence tool wear under varying machining parameters, providing theoretical insights and guidance for parameter optimization to achieve efficient and stable turning of tungsten carbide, with significant engineering implications.

Ti6Al4V is a highly desirable material due to its mechanical properties, but its machinability remains a challenge. This work presents and assesses a novel hybrid cooling/lubrication method that combines cryogenic CO₂ with minimal quantity lubrication (MQL) and sprays it from a single nozzle, as an efficient and sustainable way to improve TC4 machining performance. The novelty of this work lies in the comprehensive investigation of the atomization behavior of the cryo-MQL mixed spray and its direct correlation with machining outcomes. A multiphase flow model based on the discrete phase model (DPM) was developed using ANSYS to simulate the complex interactions between liquid lubricant and cryogenic gas within the mixing chamber and nozzle. The k-ε turbulence model was employed to capture the turbulent nature of the two-phase spray. Mesh generation was performed using GAMBIT. Experimental characterization of droplet size distribution was conducted using the spray footprint technique, revealing that the finest droplets (12.06 μm) were achieved at a spray pressure of 0.5 MPa and flow rate of 60 ml/h, which wields the best results during the machining experiments. Milling experiments were then carried out under consistent cutting conditions while varying spray parameters. Cutting force, surface roughness, surface hardness, and tool wear were thoroughly analyzed. Cryo-MQL outperformed MQL, cryogenic CO₂, and flood cooling, by lowering cutting force, improving surface finish, increasing surface hardness, and lowering tool wear. These findings show cryo-MQL’s synergistic effect of simultaneous cooling and lubrication, establishing it as a potential green machining approach for hard-to-cut alloys such as TC4.

Titanium alloy Ti6Al4V is extensively utilized in aerospace, biomedical, and high-performance engineering applications owing to its high strength-to-weight ratio, corrosion resistance, and structural reliability. However, its low thermal conductivity, high hardness, and strong chemical reactivity create substantial challenges during drilling, particularly concerning tool wear, surface integrity, and dimensional accuracy. Polycrystalline diamond (PCD) tools are particularly suitable for Ti6Al4V due to their exceptional hardness, thermal stability, and resistance to adhesive and abrasive wear, enabling improved tool performance under demanding machining conditions. This study evaluates the performance of PCD tools in drilling Ti6Al4V under different cutting conditions; dry, minimum quantity lubrication (MQL), and flood cooling at varying spindle speeds (1000, 1500, 2000 rev/min). A full factorial design was employed to assess the influence of cutting conditions on tool wear (TW), surface roughness (Ra), cutting temperature (CT), and diametric error (DE). Detailed characterization using scanning electron microscopy (SEM), 3D surfaces topography, and micrographic analyses were conducted to elucidate wear mechanisms, chip formation behavior and hole surface integrity. Results revealed that cooling strategies significantly influence machinability in PCD drilling of Ti6Al4V in terms of tool wear, surface integrity, thermal behavior, dimensional accuracy, and chip morphology. Flood cooling delivered the best overall performance, reducing TW by up to 52.80

The machining processes must achieve sustainability due to growing ecological concerns and energy crises. As an effective tool, sustainability assessment guides the implementation of sustainable strategies in machining processes. However, the complex resource consumption across machining processes and the coupling effects among machining parameters have greatly hindered its implementation. To address this challenge, this study proposes a sustainability assessment method based on progressive analysis of critical sources, enabling the identification and evaluation of key factors influencing sustainability. First, critical sources are identified through quantification of contribution degrees and sensitivity analysis. Progressive analysis is employed to focus resources on in-depth research into the fundamental characteristics and operational mechanisms of critical sources, establishing specialized indicators such as specific embodied energy and specific carbon emissions for cutters. Subsequently, a sustainable soft sensor is developed to enable efficient and cost-effective sustainability assessment. Finally, a milling case study incorporating various tool types and cooling-lubrication strategies demonstrates the method’s effectiveness in comprehensively capturing the coupling effects inherent in machining processes. The results confirm the method’s reliability and clearly validate its capability to evaluate sustainability performance in machining. This study not only provides technical support for sustainability assessments but also delivers actionable insights to facilitate the implementation of sustainable machining strategies.

WC-Co tungsten carbide possesses excellent properties, including high hardness, wear resistance, strength, heat resistance and corrosion resistance, which has led to its extensive utilisation in material forming and metal processing. During the tungsten carbide cutting process, serrated chips often appear, and they can affect cutting force, temperature and machining quality. Therefore, understanding the characteristics of serrated chips and controlling their formation as a means of enhancing the machining quality of WC–Co tungsten carbides is essential. In this research investigation, orthogonal cutting experiments and finite element simulations were undertaken in order to acquire the cutting force and chip morphology. Polycrystalline diamond (PCD) cutting tools were used for conducting cutting experiments on WC-15Co tungsten carbide. The cutting speeds were between 70 and 140 m/min, and the feed rates were between 5 and 25 µm/r. The focus of the investigation was on the influence of cutting parameters on chip formation. As the cutting speed increases, both the cutting force and the sawtooth frequency also increase; conversely, an increase in the feed rate results in a decrease in the sawtooth frequency. The mechanism underlying the formation of serrated chips in tungsten carbide has been investigated. The results showed the serrated chip formation of tungsten carbide to be primarily caused by local thermoplastic instability induced by crack initiation in the main shear zone. Chip formation occurred as a result of the combined effect of adiabatic shear and periodic fracture.

In robotic milling for aerospace large and complex cabin structural components, chatter can significantly impair the surface quality of workpieces, and in severe cases even result in tool damage. To mitigate chatter-related issues, the selection of appropriate process parameters using Stability Lobe Diagrams (SLDs) is a widely employed and effective strategy. Nevertheless, most prior investigations have primarily focused on regenerative chatter in specific robotic configurations, often disregarding the impact of low-frequency chatter originating from the inherent structural modes of the robot and its configuration-dependent dynamics. A novel approach for predicting the multi-modal stability of milling robots across their entire workspace is presented in this study. By integrating multibody dynamics model and regenerative chatter theory, this approach comprehensively accounts for the vibrations of the robotic milling system, which encompasses both the robotic structure and the tool, as well as the influence of multi-order modal variations. Moreover, a new representation method for the stability cloud map is suggested. Utilizing the multibody system transfer matrix method, a dynamics model is formulated to accommodate alterations in configurations. Additionally, a grid-based dynamic parameter identification method is proposed to predict frequency response functions under different robotic configurations. The chatter stability prediction model is integrated with the dynamics model to establish a multi-modal driven SLD. Finally, the correctness of the proposed robotic dynamics model and the effectiveness of the multi-modal SLDs with variable configurations are validated through modal and milling experiments conducted on an industrial robot.

Sustainable manufacturing has become a global and societal goal. Although its execution is much slower, the manufacturing sector is also slowing down the unsustainable practices. One of the difficulties in the implementation of sustainability is to re-engineer its evaluation for specific fields. This study brings sustainable manufacturing underpinned an overall performance index (OPI) by integrating machinability metrics, sustainability metrics, and their comprehensive sustainability evaluation. It involves quantitative and qualitative factors such as the operator’s health, shop floor environment, air quality, chip removal, and surface quality of the product in this assessment process. In this attempt, the application of the framework is presented for the assessment of sustainability through a case study with different green and hybrid lubri-cooling technologies in machining processes. These technologies have the potential to contribute to cleaner production by reducing the consumption of cutting fluid, cutting tools, energy, and carbon emissions. Considering these state-of-the-art cooling technologies, this research aims to explore their advantages and promote their application in engineering fields, thereby opening new possibilities for sustainable manufacturing practices.

The low-temperature properties of the titanium alloy Ti17 affect the milling process. In order to better understand the various physical phenomena in the cryogenic cutting process, cryogenic impact tests and tensile tests of titanium alloy Ti17 were undertaken in this study. Based on the cryogenic performance of the material, experiments on dry milling and cryogenic milling with a liquid nitrogen jet were conducted. It was found that the strength was increased, and the toughness was decreased under cryogenic conditions. The milling force shows an increasing trend with the increase of cutting speed and feed rate under both cooling conditions. The milling forces of cryogenic conditions were higher than that of dry cutting, and the surface roughness under cryogenic conditions was also improved compared to dry cutting. This study highlights how cryogenic milling of Titanium Alloy Ti17 can improve surface roughness and mechanical strength, leading to extended tool life and reduced material waste, which contributes to sustainable manufacturing. Additionally, using cryogenic cooling minimizes the need for conventional cutting fluids, reducing environmental impact and enhancing process sustainability. (c) 2025 The Authors. Published by ELSEVIER Ltd. This is an open-access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer review under the responsibility of the scientific committee of the NAMRI/SME.

The metal processing industry faces significant environmental and energy consumption challenges, which are increasingly important in global sustainability initiatives. Traditional metal-cutting methods, which involve substantial quantities of cutting fluids and generate considerable waste and emissions, are now considered unsustainable. The sustainability assessment and environmental impacts of cooling-lubricating assisted machining of Ti-6Al-4V alloys have not been studied holistically. This research aims to develop a new empirical model for the comprehensive understanding of the energy consumption and carbon emissions associated with clean-cutting processes. To achieve this, a series of milling tests were conducted using different clean-cutting techniques, including green wet cutting (pouring), Minimum Quantity Lubrication (MQL), Cryogenic CO2 cooling and hybrid CO2 mixed with minimum quantity lubrication ( CO2-MQL ). The CO2-MQL technique stood out among the tested methods, showing a remarkable improvement in energy efficiency and carbon emissions reduction. Specifically, this method demonstrated up to 30

Chatter is a deciding factor in milling operations regarding surface integrity and production efficiency. The purpose of this experimental research is to systematically investigate the influence of milling stability on machined surface integrity and fatigue performance of Ti-6Al-4V alloy samples. The surface integrity parameters like roughness, plastic deformation, microhardness, and residual stress were used to evaluate the machined surface quality. The fatigue tests were performed at five tensile stress levels with samples machined under stable, critical stable and unstable (chatter) cutting conditions. The experimental findings show that in comparison with samples produced by stable milling, the machined surface integrity parameters get worse with the increase of chatter amplitude and chatter marks, while fatigue test results show that the average fatigue life of these samples also decreases by about 20 %. The fatigue fracture morphology underscored that multiple fatigue cracks are initiated from the chatter marks on the machined surface. The experimental results provide a quantitative relation between the occurrence of chatter in milling operations and the life expectancy of the corresponding machined parts.

Energy forecasting models are essential for energy monitoring and optimization in industrial production. However, the training and testing of predictive models often require a large amount of data, but the experimental data collected not only contains noise but also is time-consuming and costly. In this article, a deep neural network model and a virtual sample generation method based on Monte Carlo-Particle Swarm Optimization are developed. A novel deep neural network architecture is designed to extract machine tool energy consumption characteristics from sample sets. The output of virtual samples generated based on probability distribution is sampled via Monte Carlo, whereas the input of virtual samples is determined using the particle swarm optimization, and the virtual samples are ultimately generated. The proposed method effectively bridges the information gap in limited-sample datasets, thereby enhancing the performance of the model. To verify the effectiveness of the method, the impacts of normal, uniform and exponential distributions on the quality of virtual samples were analyzed. Four unbalanced experimental datasets were designed for comparative analysis. The findings demonstrate that the proposed method is well-suited for small sample datasets and can enhance the prediction accuracy of trained models by over 20% with the inclusion of 100 virtual samples.

Thin-walled parts are important parts in determining the performance of aircraft. In order to solve the problem of easy deformation and generalized clamping, this paper proposes a vibration suppression method for thin-walled parts machining by combining magnetorheological damping module and flexible fixture. Firstly, the dynamic response characteristics of large thin-walled parts and flexible fixture under machining excitation are experimentally investigated. Then an intelligent vibration damping fixture is designed for the vibration characteristics of the parts, and the response surface optimization method is adopted to optimize the structural parameters of the fixture with the goal of reducing the deformation of thin-walled parts under machining excitation, so as to improve the vibration suppression effect of the intelligent vibration damping fixture.

Industrial robots (IRs) have become powerful alternatives for milling large and complex components to computerized numerical control machine tools due to their low cost, wide adaptability, and large workspace. However, the significant low stiffness of IRs makes them more prone to vibration under machining forces, especially in milling operations with large cutting volumes, restricting the development of IRs in high-quality and high-efficiency machining fields. Robot configuration and process parameters are the direct and typical factors affecting the vibration in robotic milling. Reasonable selection of these parameters can significantly reduce the vibration in robotic milling, and then the machining quality and efficiency can thus be improved. In this paper, a multibody dynamics model of milling IRs is established and then validated through experimental modal analysis, hammer impact test, and milling experiments. Based on this foundation, the effects of machining parameters including robot configuration, spindle speed, feed speed, depth of cut, and width of cut on vibration in robotic milling are investigated, which provides the guidance for the vibration suppression and the improvement of efficiency for robotic milling.

Although tungsten carbide (WC) exhibits performance, its poor machinability limits its applications. With advancements in cutting tool technology, turning and milling processes characterized by high efficiency and geometric versatility have become feasible methods for machining tungsten carbide. To systematically examine machining parameters' effect on surface quality, this study used polycrystalline diamond (PCD) cutting tools for turning experiments on WC-15Co. The results showed that increasing the feed led to higher cutting forces in all directions, as well as elevated cutting temperature, surface hardness, and surface roughness. Specific cutting energy (SCE) in brittle cutting was lower than in ductile cutting, mainly because brittle cutting involves rapid stress release through crack propagation and fracture, consuming less energy. As the cutting speed increased, cutting forces, surface hardness, and surface roughness decreased, while temperature tended to rise. Critical uncut chip thickness also increased with cutting speed, primarily due to localized material softening and the alleviation of stress concentration at higher speeds. Furthermore, higher feeds led to greater chip serration and wider segment spacing. Additionally, high-temperature softening and the squeezing effect during cutting caused cobalt enrichment on the chip bottom surfaces.