In view of the deformation generated during the casing end milling process, the casing parts are meshed and nodaled through Hypermesh, and the finite element simulation software ABAQUS is developed for secondary development by Python, so as to realize the simulation process of quasi static outer ring belt end milling. Combined with the simulation results, the deformation law in the process of casing end milling is analyzed, and the maximum deformation is near the machining position, and the deformation gradually decreases as it moves away from the machining position; the local geometric characteristics of the casing will lead to significant inconsistencies in the milling deformation of the ring belt end. The average errors predicted by the simulation model in the local height and feed direction are 13.66% and 12.17%, respectively, and the average error and maximum error of the overall prediction of the model are 19.75% and 14.28%, respectively, which are less than 20%, and the prediction accuracy is within the acceptable range, which shows the reliability of the simulation model. Using the simulation model, the factors affecting the milling deformation of the casing end are studied, which provides guidance for the future research of the end milling deformation compensation technology.

The methods of finishing surfaces of products by selective laser melting (SLM) are considered. The experience of experimental studies performed for manufacturing cutting tool housings (cutters, cutters, drills) using SLM is presented. The products considered have a complex spatial shape of the surfaces, which cannot be manufactured using traditional methods. The advantages of additive technologies for manufacturing such products are widely described, but this method has quite a few disadvantages. This includes high surface roughness and insufficiently high accuracy of the location of these surfaces. Thus, with a surface roughness requirement of Ra2.5 microns, additive technologies can only produce Ra12.5 microns (Rz40). The highest accuracy of products obtained by SLM is 0.1 mm with a requirement of 0.03-0.05 mm. Thus, the additive manufacturing method of complex products can be considered as a preparatory stage, and the finishing of the base and working surfaces has to be performed at later stages of production. The experience of final processing of these products is analyzed using the example of manufacturing turning tool housings, as well as end and end mills.

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.

Influence of fiber reinforcement and machining direction on end milling of carbon, Kevlar, and hybrid thermoplastic composites

Mechatronic design techniques to predict the best clamping conditions in slender ball end milling of a flexible workpiece

Abstract Miniaturization is the future of the industrial revolution, and technologies like micromilling are crucial in ensuring that products meet market quality standards. Therefore, micromilling must be efficient and free from anomalies such as tool failures, vibrations, and chatter. The avoidance and detection of the anomalies can be achieved via force prediction due to their correlations. This work investigates the applicability of machine learning (ML) ensemble regressors for force prediction in micromilling. A systematic series of experiments was performed on hardened AISI H13 (50 ± 1 HRC), with force signals recorded, processed, and used to develop the regressors. Models including Random Forest (RF), stacked generalization, extreme gradient boosting (XGBoost), voting, adaptive boosting (AdaBoost), and gradient boosting (GB) were developed, evaluated, and compared. XGBoost achieved the best force prediction performance, with average RMSE , MAPE , and R 2 values of 0.41, 7.83%, and 0.98, respectively, on validation data. The XGBoost model was then utilized to develop the XGBoost-Grey Wolf Optimization algorithm, which optimized cutting parameters and forces. The optimization results indicate that a feed in the range [2.68–3.75 (µm/rev)] combined with an axial depth of cut in the range [25-47.97 (µm)] will yield optimal cutting ( F c = 1.55 N) and axial ( F z = 1.74 N) forces during micromilling of hardened steels using a TiAlN-coated carbide end mill. It is also found that the axial depth of cut is the most significant parameter in generating cutting forces, whereas feed has the highest impact in generating axial forces. These findings can enhance the precision of micromilling of hardened steels.

The fourfold increase in the number of publications devoted to the milling of thin-walled structures over the past ten years indicates, on the one hand, their increasingly widespread use in high-tech products and, on the other hand, the challenges faced by the industry in their manufacture, including the vibrations that accompany the milling process. Chatter leads to the formation of macrorelief on the machined surface in the form of waviness, which negatively affects the efficiency of products containing thin-walled structures. Various studies of the mechanism of waviness formation agree on the decisive role of regenerative chatter in this process. However, their occurrence requires the presence of initial waviness. To the reasons and principles behind its formation, the existing research does not provide an answer. Analysis of vibration oscillograms for both up-cut and down-cut milling leads to the conclusion that during the contact time between the tool and the part, a specific type of vibration occurs—attending free vibrations (AFV)—which have a distinct first wave and serve as a trigger for waviness formation. The study of the AFV influence on the formation of the machined surface macrorelief is the novelty of this work. Statistical analysis of the results obtained shows the relationship between the parameters of the AFV and those of the waviness. This article continues a series of publications on the results of research into the influence of technological parameters on those of AFV and waviness. It is devoted to the study of the impact of the radial cutting depth during up-cut and down-cut end milling of thin-walled structures. The obtained results demonstrate that the height of the waviness increases with the growth of the radial depth of the cut, both during up-cut and down-cut milling, while its step decreases during up-cut milling and increases during down-cut milling.

Milling force modeling and tooth shape optimization of the wave-flute end mill based on the semi-analytical method

Symmetry and asymmetry jointly govern the dynamics and surface integrity of thin-wall AA7075 end milling. In this work, a symmetry-aware simulation and experimental framework is developed to connect process parameters with milling forces, dynamic response, surface quality, and through-thickness residual stress. A mechanistic milling-force model is first established for multi-tooth end milling, where the periodically repeated tooth-order excitation provides a nominally symmetric load pattern along the tool path. The predicted forces are then used as input for finite-element modal and harmonic-response analysis of a thin-walled component, revealing how symmetric and anti-symmetric mode shapes interact with the tooth-order excitation to generate locally amplified, asymmetric vibration of the compliant wall. Orthogonal and single-factor milling experiments on AA7075 thin-wall specimens are performed to calibrate and validate the force model, and to quantify the influence of feed per tooth, axial depth of cut, spindle speed, and radial width of cut on deformation, surface roughness, and geometric accuracy. Finally, a thermo-mechanically coupled finite-element model is employed to evaluate the residual-stress field, showing a characteristic pattern in which an initially symmetric thermal–mechanical loading produces depth-wise symmetry breaking between tensile surface layers and compressive subsurface zones. The proposed symmetry-aware framework, which combines milling-force theory, finite-element simulation, and systematic experiments, provides practical guidance for selecting parameter windows that suppress vibration, control residual stress, and improve the machining quality of thin-wall AA7075 components.

The paper deals with the development of a system for integrated assessment of machinability based on monitoring tool displacements and cutting temperatures. This system is characterised by its simplicity and low cost. The proposed approach to evaluating material machinability combines static and dynamic components of cutter displacements, as well as cutting temperature, each assigned specific weighting coefficients. The material machinability evaluation is differentiated for roughing and finishing operations through weighting coefficients that take into account the specific influence of each parameter on the machining results. This differentiated approach enables assessment of material machinability both from the perspective of resistance to deformation and fracture and considering specific cutting process characteristics. The dynamic displacement component serves as a diagnostic characteristic for the chip formation process (elemental, segmented, or continuous), surface roughness quality, and, when combined with temperature, for cutting tool life. Experimental results from end milling of 45 and 09G2S steels, along with VT6 and VT8M-1 titanium alloys with varying grain sizes, demonstrate the practical application of this methodology. Standardised experiments provided displacement and temperature data used to evaluate the machinability of tested materials. The results confirm the feasibility of using the proposed comprehensive indicator for assessing material machinability in cutting processes. This approach forms the basis for the development of a new comprehensive machinability assessment method that considers individual parameters and their combinations to determine technological constraints, thereby enabling process optimisation and production cost reduction.

Machining is one of the methods to produce components and products fromraw material. Many factors influence the outcome of the machining processand the life of the components there after. Researchers have tried tounderstand the underlying principles of machining using finite elementanalysis since many years. In the present work the authors have made anattempt to study few behaviour namely, stress distribution, force variation andmachining induced residual stresses while machining hard to machineTi6Al4V alloy using finite element analysis. The model that is presented inthis work is an improvisation of some of the existing models overcomingsome of the shortcomings of the existing model. The work presented here usesthe time tested Johnson-Cook model, but unlike the many other workssacrificial layers is not being used rather, Johnson-Cook damage model isbeing used. In addition, the authors have considered opted for oblique cuttingin spite of high computational time due to the want of more accurate results.

Trochoidal milling is a machining technique that enables high-efficiency machining of particularly hard-to-cut materials using a specific tool path at high feed and cutting speeds. This research focused on the impacts of dry and MQL milling on surface roughness (Ra) in trochoidal milling groove opening on AISI 304 stainless steel. Experiments were conducted with a TiAlN-coated carbide end mill using parameters such as variable cutting speed, axial depth of cut (ap), and constant feed. Analysis of variance was used for evaluating the effects of the parameters on surface roughness. While surface roughness increased in dry cutting with increasing ap, it decreased in MQL environment. This result was ascribed to the stable cutting process resulting from the MQL application's reduction of friction along the milling cutter edge and the prevention of BUE formation. Based on the results, the minimum Ra value of 0.386 μm was acquired in the MQL cutting environment at a cutting speed of 150 m/min and a ap of 8 mm. According to Ra measurements from four groove surfaces, the surface roughness obtained in the MQL environment was on average 17.5% lower than in dry cutting. According to the ANOVA results, cutting speed was the most effective parameter in trochoidal milling under dry and MQL environments. These results prove the positive effect of the MQL application in trochoidal milling operations on increasing machining efficiency.

The article discusses the effect of riveting on the required shape accuracy (flatness) of thin long parts made of 08H15N5D2T steel during sequential machining. Flatness is a parameter of shape accuracy, which is characterized by the degree of deviation of the surface from a perfectly flat state relative to an ideal body (an ideal body means a body that has a certain degree of shape accuracy, confirmed by special techniques). It is affected by mechanical processing and the ability of the metal to store the received energy in the form of stresses. The machining modes, if selected incorrectly, introduce greater hardness into the product layers compared to the hardness obtained after heat treatment. This phenomenon is called riveting (or over-riveting). Heat treatment also plays an important role in obtaining the required accuracy values. With a rationally selected heat treatment mode, the amount of riveting decreases with the same machining modes, as well as the likelihood of over-riveting. The article conducts experiments on 08H15N5D2T steel samples and presents the cutting modes used in production for end milling and flat pendulum grinding of manufactured parts, as well as their effect on flatness under various pre-heat treatment modes. Conclusions are drawn based on the results obtained.

This study focuses on the prediction and analysis of temperature distribution during end milling of AISI 4340 steel. The influence of cutting parameters—cutting speed, feed per tooth, and depth of cut—on temperature generation in the cutting zone was investigated using a CCD experimental plan. Temperature was measured with a thermal imaging camera, while the milling process was simulated using Third Wave AdvantEdge 7.1 FEM software. The obtained temperatures ranged from 74 °C to 200 °C, depending on the cutting conditions. A second-order regression model with three factors was developed and showed an average prediction error of 8.62%, while the alternative fitted model had an average error of 10.91%. FEM simulations using AdvantEdge 7.1 demonstrated a somewhat higher deviation, with an average error of 14.75% relative to experiments. The highest deviations for all approaches occurred at extreme cutting parameters (very low or very high depth of cut). The study demonstrates that FEM simulations are an effective tool for predicting thermal behavior in milling and optimizing cutting parameters. Accurate prediction of cutting zone temperatures can improve tool life, enhance process efficiency, and support the selection of optimal machining conditions, which is very important from an industry point of view.

Optimizing end mill geometry is critical for improving performance and reducing costs in the high-volume manufacturing of tools, dies and molds. This study demonstrates a successful optimization framework for solid end mills machining 1.2379 cold-work tool steel, integrating Finite Element Analysis (FEA), Artificial Neural Networks (ANN), and Genetic Algorithms (GA). The optimized tool geometry, derived from four key design parameters, delivered substantial performance gains over an industrial reference (parent) tool. Our ANN-GA model achieved a remarkable predictive accuracy (R = 0.75–0.98) over the RSM model (R = 0.17–0.63) and identified an optimal design that reduced the resultant cutting force by approximately 11% (to 142.8 N) and improved surface roughness by 21% (to 0.1637 µm) compared to experimental baselines. Crucially, the new geometry halved the tool breakage rate from 50% to ~25%. Parameter analysis revealed the width of the land as the most influential geometric factor. This work provides a validated, high-performance tool design and a powerful modeling framework for advancing machining efficiency in tool, mold and die manufacturing.

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.

Introduction. Wire arc additive manufacturing (WAAM), due to its “design as manufacturing” characteristic, is gradually becoming one of the most promising technologies. However, at present, there are no comprehensive comparative studies on the microstructure and mechanical properties of deposited samples made from austenitic stainless steel at different locations of the sample. In addition, their machinability remains insufficiently investigated. The purpose of this study is to compare the microstructure and mechanical properties of samples made of austenitic stainless steel ER321 (analogues – AISI 321, 0.08% C-18% Cr-10% Ni-Ti) obtained by the WAAM method at different locations within the sample and to assess their machinability by the magnitude of the components of the cutting force during end milling and the roughness of the machined surface. The properties and microstructure of samples obtained by wire-arc additive technology are investigated, and milling forces are investigated. The effect of the feed on the components of the cutting force and the roughness of the machined surfaces during conventional milling of ER321 steel workpieces using 12 mm diameter cemented carbide end mills with a wear-resistant AlTiN coating applied by physical vapor deposition (PVD) is determined. Research methods. The content of elements and the solidification pattern in various parts of the workpieces were determined using X-ray microanalysis. The microstructure of the samples was studied by a metallographic method. Stress-strain diagrams were obtained by tensile tests, and the microhardness of the samples was also measured. In comparison with the pattern of conventional milling of rolled workpieces, a pattern of changes in cutting forces and surface roughness was established depending on the feed rate during milling of deposited workpieces. Results and discussion. During deposition, ferrite with a vermicular morphology is primarily formed in the lower region of the sample, whereas austenite with a dendritic ferrite structure is observed in other regions. The microhardness values of the deposited and rolled samples are close, averaging around 230 HV0.1. The ultimate tensile strength of the rolled samples is 666 MPa, which is approximately 40 MPa higher than that of the deposited samples. During milling of the deposited workpieces, the lateral cutting force acting perpendicular to the feed direction is greater, and the surface quality is poorer. During milling of deposited workpieces, the lateral cutting force acting perpendicular to the feed direction is greater, and the surface quality is poorer. During milling of deposited workpieces, the feed force acting in the feed direction is greater under high feed rates.

Identification of the appropriate model parameters in the cutting process is essential to achieve highly accurate cutting simulations that are effective for the process optimization. During end milling, tool damage deteriorates not only the side cutting edge geometry but also the corner cutting edge as the cutting distance increases. The conventional model does not consider the effect of local corner edge damage on the cutting force. Therefore, we proposed a model and its parameter identification method considering the corner edge damage. The tool life test verified that the corner damage modelling significantly improves the force estimation accuracy.

This study investigates vibration signals generated during end milling of thin-walled EN AW-7075 aluminum alloy components using a set of 24 tools with distinct cutting edge microgeometries. Five characteristic parameters describing the dynamic response of the process, including both energy-related and statistical indicators, were extracted and analyzed. The results clearly demonstrate the critical influence of tool microgeometry on process dynamics. In particular, the introduction of an additional zero-clearance flank land at the cutting edge proved decisive in suppressing vibrations. For the most favorable geometries, the root mean square (RMS) value of vibration was reduced by more than 50%, while the spectral power density (PSD) decreased by up to 70–75% compared with the least favorable configurations. Simultaneously, both time- and frequency-domain responses exhibited complex and irregular patterns, highlighting the limitations of intuitive interpretation and the need for multi-parameter evaluation. To enable a synthetic comparison of tools, the Vibration Severity Index (VSI), which integrates RMS and kurtosis into a single composite metric, was introduced. VSI-based ranking allowed the clear identification of the most dynamically stable geometry. For the selected tool, additional analysis was conducted to evaluate the influence of cutting parameters, namely feed per tooth and radial depth of cut. The results showed that the most favorable dynamic behavior was achieved at a feed of 0.08 mm/tooth and a radial depth of cut of 1.0 mm, whereas boundary conditions resulted in higher kurtosis and a more impulsive signal structure. Overall, the findings confirm that properly engineered cutting-edge microgeometry, especially the formation of additional zero-clearance flank land significantly enhances the dynamic of thin-wall milling, demonstrating its potential as an effective strategy for vibration suppression and process optimization in precision machining of lightweight structural materials.

The inhomogeneity and droplet related defects of cathodic arc deposition and lower ion density of magnetron sputtering-based tool coatings are the challenges that lowers coating quality. The present study carries two types of tool coatings in which one set of cutting inserts are coated with single layer conventional cathodic arc evaporation while the other set is coated with bimodal coating (arc and sputtering). The bimodal coating utilises an arc current of 80 A for the adhesive base layer and sputtering current of 500 mA for the surface refinement layer. SEM micrographs and atomic force microscopy confirmed that the bimodal coating significantly reduced the droplet density as well as improved the surface uniformity of arc deposited coating (84.9% reduction in R t value). XRD plots reveal the formation of the nanocrystalline cubic structure of TiN. The crystallite sizes were analysed by Scherrer equation which were 12.4 and 14.3 nm for single layer and bimodal coating respectively. The bimodal coating showed a 7.3% enhancement in nano-hardness confirmed its superior performance. During the end milling of Nimonic 90 under constant machining parameters of 75 m/min cutting speed, 100 mm/min feed, and 0.05 mm axial depth of cut under a 5 bar compressed air cutting environment, the bimodal coated tool reduced the cutting forces, surface roughness, and cutting temperature by 45.26%, 34.24%, and 25.93% respectively compared with cathodic arc coating. These findings highlight that combining arc evaporation with sputtering effectively enhances coating quality for advanced machining applications.