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Ball end mills have complex geometry. The precision CNC grinding of the cutting edge on the spherical surface is a big challenge for the tool manufacturers. Regarding the design cutting edge, we propose a cutting-edge design model in which the cutting edge can be offset along the spherical surface. To improve the accuracy of this model, the twist drill chisel-edge was also introduced into this design model of cutting edge. Then, referring to the toolpath calculation method of complex surface machining, we established a method for grinding the rake face of the ball end mill using a 1A1 standard wheel. For the clearance of the ball end mill, we developed a method to grind the clearance of the ball end mill using an 11V9 standard wheel. Finally, the design model of cutting edge, grinding models of rake face, and clearance were verified by simulations and experiments, respectively. The results show the proposed cutting-edge model has a better profile tolerance. The intersection line between the rake face and clearance coincides with the design's cutting edge. These prove the effectiveness of methods. [ABSTRACT FROM AUTHOR]

There are inequable force-induced deformation, obvious cutting vibration problems during side milling of low-rigidity frame and beam parts, resulting in unsatisfactory dimensional accuracy and surface quality. Most of these parts are side-milled by end mill, which currently in use do not take into account the workpiece stiffness imparity. Therefore, the concept of gradient helix edge (GHE) is proposed in this research, and its advantages are analyzed. The general mathematical model is given. Furthermore, the parabola is taken as an example for the GHE space equation derived, of which accuracy is verified by numerical simulation. Finally, the comparison experiment with the invariant helix edge end mill was completed. The results showed that the milling force perpendicular to the milling surface of the GHE end mill was smaller and with less fluctuation, which could obtain less force-induced deformation theoretically. The milling process was smoother, and better surface quality was obtained. The excellent performance of the GHE end mill was further verified by milling different features. [ABSTRACT FROM AUTHOR]

508-III steel has become one of the most viable materials for heavy nuclear reactor pressure vessels due to its excellent strength and fracture toughness. However, the poor machinability of 508-III steel requires the tool to withstand extremely high mechanical stress, thermal stress, and shock during the cutting process, thereby accelerating tool failure and affecting machining efficiency. High-feed milling (HFM) is a novel rough machining technology that ensures machining efficiency and prolongs tool life. This paper aims to reveal tool failure mechanisms during HFM of 508-III steel and to indirectly evaluate the cutting performance of the HFM cutter in the context of cutting vibration and chip morphology. Dry milling experiments of 508-III steel were carried out using an HFM cutter with arc-shaped bottom edges. Experimental results show adhesion, oxidation, and diffusion are the primary tool wear mechanisms. The wear levels between each insert present significant differences, which can be explained by cutting-edge position deviations and unexpected tool movements. With the continuous accumulation of tool wear, the insert with the weakest cutting edge strength is broken, which can be attributed to the crack propagation near the cutting edge, hard spots, and the shedding of adhesive materials. When the feed per tooth is 0.8~1.0 mm, the whole milling process shows good stability, especially in the spindle direction. The chip morphology and color variations are closely related to tool wear, which can provide an essential basis for tool failure prediction. [ABSTRACT FROM AUTHOR]

Tool wear detection based on machine vision is the core to realize intelligent and automated cutting processing. It is essential to solve the problem of low-efficiency and low-quality acquisition of tool wear image acquisition in the machine detection process. In this paper, the image acquisition method by using a 6-DOF (degree of freedom) serial robot with an industrial camera is developed. The digital twin unit of the detection system is set up, the UR10 robot kinematics model and acquisition trajectory planning method are proposed, and Unity3D software is used to arrange the digital twin environment of the detection system. The synchronous mapping function of the robot digital model and physical entity is given through Socket communication to realize complex tool edges image acquisition trajectory planning, accurate teaching of virtual scenes, digital monitoring of the detection process, and optimization of system models. The stability and efficiency of robot kinematics, trajectory planning, and interactive communication in the tool wear image detection environment are verified. The research results show that the proposed method can provide support for the digital monitoring and control technology of the tool wear on-machine detection process. [ABSTRACT FROM AUTHOR]

The flute plays an important role in the design process for tools. The complexity of the cutting edge is increasing continuously. Thus, it is difficult to grind flute precisely and efficiently. This paper presents a practical grinding method for the flute of tools. It solves the issue of how to grind the flute with the complex edge by standard wheels. It is summarized as follows: the curve of cutting edge and core are used as the designed parameters of the flute; we propose the fundamental conditions that the wheel does not interfere with designed parameters; we can find the posture and position of wheel in the curve frame easily, thus obtaining wheel trajectory. Finally, using a taper end mill as an example, the results show a good agreement between the CAM data and the model data. This proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]

In the milling process, it is easy to produce chatter due to the low rigidity of the thin-walled structure, which leads to the deterioration of workpiece surface quality and reduces the service life of cutting tools and machine tools. Therefore, a new chatter detection method for thin-walled parts based on optimal variational mode decomposition (OVMD) and refined composite multi-scale dispersion entropy (RCMDE) is proposed in this paper. Firstly, to solve the problem that the decomposition effect of the variational mode decomposition (VMD) algorithm is greatly affected by its parameter, a genetic algorithm (GA) is used to iteratively optimize the parameter of the VMD algorithm, and a new index, square envelope spectral correlated kurtosis (SE-SCK), is introduced as the fitness function of the genetic algorithm. Then, the energy ratio of the decomposed signal is calculated as the principle of selecting sub-components, and the sub-components with rich chatter information are selected for signal reconstruction. To solve the problem that the multi-scale dispersion entropy (MDE) will miss some information in the multi-scale process, RCMDE is introduced to detect milling chatter. Finally, the experiment of the variable cutting depth in side milling of titanium alloy thin-walled parts is carried out. The experimental results show that the OVMD algorithm proposed can solve the problem of difficult separation of chatter frequency bands caused by mode aliasing and lay a foundation for subsequent chatter feature extraction. RCMDE is more conducive to chatter detection than the single-scale DE when the scale factor is 4. The distinguishing effect of RCMDE on the machining state is more than 50% higher than that of MDE when the scale factor is 4. [ABSTRACT FROM AUTHOR]

Tool Condition Monitoring (TCM) technology in machining is crucial for maintaining safety and optimizing costs. However, its practical application faces two significant challenges: difficulties in data collection and a decline in generalization performance across different monitoring tasks. To this end, a hybrid feature boundary-enhanced meta-learning network with adaptive gradients (HFBEAML) is proposed. This method combines cutting process parameters with time-series signal features, employing a multimodal one-dimensional Convolutional Neural Network (1D-CNN) based on the DenseNet architecture and a multi-head self-attention mechanism (MHSA) to mine multi-dimensional features sensitive to tool wear. To further enhance the model's feature discrimination capability, a multi-loss joint optimization strategy is introduced, combining task-level loss with an improved triplet loss. This approach incorporates strategies for adaptive boundaries and handling the hardest positive and negative sample sets, thereby enhancing the robustness of feature metrics. Additionally, this research innovatively proposes an adaptive meta-level gradient update mechanism, dynamically adjusting gradient weights according to the characteristics of multiple tasks, aiming to improve the model's generalization ability in multi-task learning environments. The effectiveness of the proposed method is demonstrated through experiments in two different scenarios, comparing its results with five other models showcasing its significant advantages in multi-task, small-sample data monitoring environments. [ABSTRACT FROM AUTHOR]

Saving resources and reducing the energy consumption of CNC machine tools has become one of the main research directions of modern green manufacturing. In this paper, the influence of curvature characteristics on energy consumption is studied based on surface milling. Firstly, the loss of energy consumption in the machining process of the NC machine tool is analyzed, and the milling energy consumption model per unit volume is established; secondly, based on the unit volume milling energy consumption model, a unit volume milling energy consumption model considering blade curvature characteristics is established; finally, based on the milling energy consumption model, the "specific energy" is introduced to establish the energy consumption field model of blade milling path. The distribution of the energy consumption field of four milling paths is analyzed in turn, and the distribution of the energy consumption field of 24 tool sites in the milling path is analyzed. The experimental results show that the energy consumption field model can show the energy consumption distribution of the NC machine tool machining process, and the accuracy is more than 90%, which provides a reliable basis for the subsequent energy consumption prediction of the impeller machining process. [ABSTRACT FROM AUTHOR]

Due to its good cutting performance in titanium alloy machining, integral end mills are more and more used in machining aero-engine impeller blades. The tool spiral groove plays the role of chip acceptor and chip removal, and the accuracy of its parameters has an important effect on the cutting performance. In the grinding process of the spiral groove, the grinding wheel's external grinding is mainly involved in the grinding task. The grinding wheel's wear degree is related to the grinding time and grinding times of the grinding wheel, and the wear of the grinding wheel will lead to the change of the parameters of the spiral groove. To achieve the accurate solution of the grinding wheel surface wear profile, image processing technology was used to extract the spiral groove end section contour coordinates of the grinding wheel and fit them. The worn sand profile was solved based on the contact line principle, and the grinding wheel wear amount was obtained. The traditional reconstruction method was used to verify the algorithm. The results show that the accuracy of the reverse algorithm for the wear profile of the grinding wheel is relatively high. [ABSTRACT FROM AUTHOR]

Generally, off-line methods are used for surface roughness prediction of titanium alloy milling. However, studies show that these methods have poor prediction accuracy. In order to resolve this shortcoming, a prediction method based on Cloudera's Distribution including Apache Hadoop (CDH) platform is proposed in the present study. In this regard, data analysis and process platform are designed based on the CDH, which can upload, calculate, and store data in real time. Then this platform is combined with the Harris hawk optimization (HHO) algorithm and pattern search strategy, and an improved Harris hawk optimization optimization (IHHO) method is proposed accordingly. Then this method is applied to optimize the support vector machine (SVM) algorithm and predict the surface roughness in the CDH platform. The obtained results show that the prediction accuracy of IHHO method reaches 95%, which is higher than the conventional methods of SVM, BAT-SVM, gray wolf optimizer (GWO-SVM), and whale optimization algorithm (WOA-SVM). [ABSTRACT FROM AUTHOR]

Ball-end cutters are widely used in industries of dies, molds, and aerospace, which have the problem of poor machined surface quality due to the low cutting speed near the tool-tip. With the increase in the complexity of parts, it will become more and more difficult to avoid the tool-tip participating in the cutting. In this paper, the velocity effect sensitivity of the ball-end cutter is analyzed, and several key positions, including the intersection points of the CWE boundaries, are selected to describe the cutting speed in three dimensions. The relationships between the cutting speed of the critical points and important variables such as the machining inclination angle and the feed direction were investigated. The optimal range of feed direction is obtained when the tool-tip engages in the contact circle. The core aim of the feed direction selection is to make the tool engagement area in a high position by changing the feed direction, to avoid surface damage and improve the quality of the machined surface. Finally, an experimental study was carried out, and the results corroborate the effectiveness of the selection method. In the experiment, it was also found that cutting-out from the cutter contact position can improve the surface quality in the directions of non-optimal range, and the milling force and chips shape will vary with the change of the feed direction. [ABSTRACT FROM AUTHOR]

In the milling process, chatter is easy to occur and has a very adverse impact on the quality of the workpiece and the production efficiency. A chatter feature extraction method based on optimized variational mode decomposition (OVMD) and multi-scale permutation entropy (MPE) was proposed to solve the problem that it is difficult to detect the machining chatter state during milling. The methodology presented in this article allows the occurrence of machining chatter to be effectively identified through real-time digital signal processing and analysis. First, in order to solve the problem of variational mode decomposition (VMD) parameter selection, an automatic selection method based on particle swarm optimization (PSO) and the maximum crest factor of the envelope spectrum (CE) was proposed. Then, the decomposed signal was reconstructed based on the energy ratio. In order to solve the problem that the single-scale permutation entropy (PE) cannot detect milling chatter well, the MPE was introduced to detect milling chatter. Finally, experimental verification was carried out, and the MPE of the reconstructed signals at different scales was extracted and analyzed. The results show that using the OVMD algorithm to process the signals can significantly improve the discrimination of MPE. With the increase of the scale factor, the MPE of the milling signals tends to decrease. At the same time, MPE is better than single-scale PE in chatter detection, and the MPE at scale factor of 4 is more conducive to chatter detection. [ABSTRACT FROM AUTHOR]

Solid end mill cutting tools are widely used in machining of curved surface parts in many industrial sectors, e.g., aerospace, automotive, and energy. Grinding is one of the most important processes in the manufacturing of the tools, and the movement of the grinding wheel closely influences the key parameters of helical groove cross-section, i.e., rake angle γo, inner core radius Rc, outer core radius Rcb, and edge width Ew, as well as chip removal capacity. However, a "closed" flute of a tool, which is one style of flute, may cause many problems if grinding it by one-pass, e.g., grinding wheel dressing to adapt to the change of flute parameters. To solve the problem in "closed" flute grinding, this paper proposes a two-pass flute grinding based on the iteration method. Within the context, some parameters and rules are identified in modeling of the two-pass grinding to control grinding wheel width and to smoothen grinding marks between the two passes of grinding. Finally, the method is implemented and validated by a set of numeric simulations and experiments. The results show that the errors of core radius, rake angle, edge width, and big core radius of the two ground tools are 1.1%, 2.0%, 3.4%, and 2.2%, respectively, which are within the designed tolerances. [ABSTRACT FROM AUTHOR]

Multi-atlas parcellation (MAP) is carried out on a brain image by propagating and fusing labelled regions from brain atlases. Typical nonlinear registration-based label propagation is time-consuming and sensitive to inter-subject differences. Recently, deep learning parcellation (DLP) has been proposed to avoid nonlinear registration for better efficiency and robustness than MAP. However, most existing DLP methods neglect using brain atlases, which contain high-level information (e.g., manually labelled brain regions), to provide auxiliary features for improving the parcellation accuracy. In this paper, we propose a novel multi-atlas DLP method for brain parcellation. Our method is based on fully convolutional networks (FCN) and squeeze-and-excitation (SE) modules. It can automatically and adaptively select features from the most relevant brain atlases to guide parcellation. Moreover, our method is trained via a generative adversarial network (GAN), where a convolutional neural network (CNN) with multi-scale $l_{1}$ loss is used as the discriminator. Benefiting from brain atlases, our method outperforms MAP and state-of-the-art DLP methods on two public image datasets (LPBA40 and NIREP-NA0). [ABSTRACT FROM AUTHOR]

The quality of a workpiece depends on the time-varying characteristics of the tool performance. Tool wear is an important factor that affects the serviceability of a tool. However, most existing models do not fully contain the tool design and processing parameters and do not focus on the time-varying characteristics of wear. In this paper, the wear distribution characteristics of a ball-end mill were investigated by analysing the position function in the milling life model, and the geometric model of the wear volume was proposed based on the principle of calculus. By geometric and physical modelling, the tool design and machining parameters were completely introduced in the model, and the time variable is introduced when solving the wear rate. After the models and assumptions are validated, the factors affecting wear are cross-analysed and discussed through simulation. Wear models including time variables, tool design, and milling parameters are obtained, which can be used directly for tool design and analysis. This research provides a basis for the tool analytical design. [ABSTRACT FROM AUTHOR]

Pyrolysis experiments of hazelnut shells were conducted by tube furnace pyrolysis reactor at 400–1,000°C. The results indicated that the effect of pyrolysis temperature on yield and characteristics of bio-char was significant. With the deepening of carbonization, bio-char yields decreased, approximately 50 wt% of VM were released, and the concentration of FC increased by 47 wt%. The optimum for HHV (33.3 MJ/kg) and combustion reactivity was at 900°C and 700°C, respectively; the adsorption capacity of bio-char was optimal and was determined by BET surface area and iodine absorption value (respectively 197.3 m2/g and 595.4 mg/g) at 800°C. The results provided a strong support for bio-char application from discarded hazelnut shell pyrolysis. [ABSTRACT FROM AUTHOR]

Solid cutting tool is more and more widely used, such as aerospace, automobile and 3C. Helical groove is an important feature which influences the performance of solid cutting tool. Furthermore, groove grinding is the longest and largest material removal process in whole grinding of solid tool. Therefore, the most wear of grinding wheel occurs in its grinding. The worn wheel would result in groove grinding error without a proper compensation. The traditional compensation relies on the experience of machine operator, and generally, it is performed on site. In this paper, a compensation algorithm of worn wheel is proposed by analysing the boundary contact condition which is influenced by wheel wear. Furthermore, the algorithm is implemented by using C# and validated by a set of and experiments. The experimental results show that the ground tool flutes under compensation algorithm are controlled within designed tolerances; i.e., core radius errors are 1.7% and 0.7%, rake angle errors are 1.5% and 0.6%, and edge width errors are 4.3% and 5%. [ABSTRACT FROM AUTHOR]

An increasing number of cutting tools with different geometric shapes are needed owing to the increasing complexity of the workpiece surfaces in aerospace and automotive molding. A new kind of milling cutter is designed, which is named as revolving lemniscate milling cutter (RLMC for short). The parametric mathematical models of RLMC are proposed. Mathematical models of cutting edge curves are proposed to provide the theoretical basis for RLMC precision manufacturing. Equivalent helix angle, curvature radius of tool rotation surface, effective cutting linear velocity, and scallop height are studied. Corresponding numerical simulation results are also provided as graphical representations. Three RLMC with different tool parameters are manufactured, and contrast experiments are conducted to prove that using RLMC in end milling can achieve better machining quality. The parametric design and application research provide a new method and theoretical basis for other new types of milling cutters. [ABSTRACT FROM AUTHOR]

Tetrastigma Hemsleyanum Diels et Gilg (SYQ) is a typical She ethnomedicine that has been used in anti-tumor treatment in Chinese folklore. The polysaccharide of SYQ (SYQ-PA) has been reported to have antioxidant and anti-inflammatory effects, but the effect and mechanism on antitumor is still unclear. To investigate the activity and mechanism of SYQ-PA against breast cancer in vitro and in vivo. In this study, different stages of MMTV-PYMT mice, which at 4-week-old and 8-week-old representative the transition from hyperplasia to late carcinoma, were used to investigate the potential effect of SYQ-PA of breast cancer development in vivo. The mechanism was explored with IL4/13-induced peritoneal macrophages model. Flow cytometry assay was employed to analysis the change of tumor microenvironment and the macrophages typing. The inhibition of the condition medium from macrophages on breast cancer cells was detected with xCELLigence system detection. The inflammation factors were tested with cytometric bead array. Co-culture system was used to detect the cell migration and invasion. In addition, the underlying mechanism was investigated using RNAseq analysis, Q-PCR and Western blot, and the PPARγ inhibitor was used to verify the mechanism. SYQ-PA significantly attenuated the process of breast primary tumor growth and reduced the infiltration of TAMs accompanied promoting the polarization of M1 phenotype in MMTV-PyMT mice. Then in vitro studies showed that SYQ-PA promoted macrophages polarization form IL4/13 induced M2 toward to the anti-tumor M1 phenotypes, and the conditioned medium (CM) from the induced macrophages inhibited the proliferation of breast cancer cells. At the same time, SYQ-PA treated macrophages inhibited the migration and invasion of 4T1 in the co-culture system. Further results indicated that SYQ-PA suppressed the release of anti-inflammatory factors and promoted the production of inflammatory cytokines which may induce M1 macrophage polarization and inhibit breast cancer cell proliferation. Subsequently, the underlying mechanism analysis based on RNAseq and molecular assays indicated that SYQ-PA inhibited PPARγ expression and regulated downstream NF-κB in macrophages. After treated with PPARγ inhibitor, T0070907, the effect of SYQ-PA was decreased, or even disappeared. As the downstream, the expression of β-catenin was also inhibited obviously, those above all contribute the process of SYQ-PA induced M1 macrophages polarization. Collectively, SYQ-PA was observed inhibited breast cancer, at least in part, via PPARγ activation- and β-catenin-mediated M2 macrophages polarization. These data expound the antitumor effect and mechanism of SYQ-PA, and provide a possible that SYQ-PA can be used as an adjuvant drug for macrophage tumor immunotherapy in breast cancer. [Display omitted] • SYQ-PA treatment significantly attenuated growth and lung metastasis of breast primary tumor in MMTV-PyMT. • SYQ-PA alleviates the immunosuppressive effect of TME by polarizing TAMs toward M1 macrophages. • SYQ-PA reverses macrophage M2 polarization into the M1 phenotype by inhibiting PPARγ and β-catenin expression. [ABSTRACT FROM AUTHOR]

The denoising of mechanical system is always an indispensable process in sensor signal analysis. It directly affects the result of subsequent tool state monitoring and identification. Therefore, a denoising framework is proposed to solve this problem. Bayesian nonparametric estimation instead of the Gaussian fitting distribution of CycleGAN can ensure the quality of denoising data to the greatest extent. The experiment of milling 42CrMo steel was carried out, and the proposed method was verified. Compared with the wavelet packet threshold, the signal-to-noise ratio (SNR) obtained by the propose model is increased by 4.71 dB on average, and RMSE ranges from 0.0210 to 0.0642. UKF-CycleGAN model has better denoising effect than other methods. The model proposed in this paper improves the accuracy of tool wear identification. At the same time, the process of selecting the parameters for denoising model by manual experience can be reduced. This provides the possibility for online denoising of sensor signals in milling process, which has certain guiding significance for tool state monitoring in machinery industry. [ABSTRACT FROM AUTHOR]

In recent years, nanotechnology has been developing continuously. Due to their advantageous huge specific surface areas, microinterface characteristics, remediation ability and potential environmental risks, nanomaterials have become a hot topic in the field of environmental research. With the mass production and use of nanomaterials, they will inevitably be discharged or leaked into the water environment. In this paper, we will describe some typical nanomaterials, such as nanoscale zero valent iron (nZVI), graphene nanomaterials (GNMs), TiO 2 nanoparticles (NPs), ZnO NPs, Fe 3 O 4 NPs, carbon nanotubes (CNTs), Ag NPs, and other nanomaterials in water environments, focusing on the positive and negative effects of some nanomaterials in water environments. The remediation function and the impact of nanomaterials in water environments, including behavior of nanomaterials and their toxicity to aquatic organisms will be discussed. This will be of great significance for our subsequent research on nanomaterials. • The behavior of nanomaterials in the water environment (lakes, rivers, oceans and wetlands) was summarized. • The remediation effects and mechanisms of nanomaterials for the water environmental problems were reviewed. • Some nanomaterials can produce toxic effects on planktons and mocrobes in the water environment. [ABSTRACT FROM AUTHOR]

In deep hole boring process, long and flexible boring bars are often used. Due to the large length-to-diameter ratio, the stiffness of the boring bars is inevitably reduced, where the boring bars’ vibration effects will occur. The influences of vibration will significantly degrade the accuracy and the surface quality, or even lead failure of the production. Therefore, it is of significant importance to develop techniques to reduce vibration in deep hole boring. In this paper, a new boring bar with a variable stiffness dynamic vibration absorber (VSDVA) is presented, where the basic parameters of the proposed boring bar are measured. Based on the proposed dynamic model, the vibration characteristics of the proposed boring bar are analyzed, and the change laws of the vibration reduction performance are obtained under different excitation frequencies. A new vibration reduction method is proposed, where best vibration reduction performance can be achieved by adjusting the stiffness of the VSDVA. Finally, the vibration reduction performance of the proposed boring bar is validated and evaluated by boring experiments. These works could provide guidance for designing new types of boring bars, selecting cutting parameters, and adjusting the vibration reduction performance of the proposed boring bar, and as a result, it provides a new design idea for the design of boring bar. [ABSTRACT FROM AUTHOR]

The cutting force signal distortion is caused by the dynamic characteristics of cutting force testing system. In order to handle this issue, we propose two improvements in the traditional inverse filtering technology. Firstly, we use three-spline interpolation method instead of the curve fitting method to fit the frequency response function of the test system which basically improves the accuracy of fitting. Secondly, the low-pass filter is added before the inverse filter to eliminate the influence of the high-frequency noise signal on the cutting force signal. We choose the cavity-free surface of outer covering parts of mold of automobile as research objects. The inverse filter dynamic compensation technology has been used to remove the influence of the dynamic characteristics of the test system and the high-frequency noise on the cutting force signal. The effectiveness of the proposed method is verified by relative milling experiments. Based on the experimentally measured forces after dynamic compensation, the modified cutting force coefficients are obtained using the average milling force method. The variation law of the cutting force coefficients with the axial depth, the radial width, and the feed rate is examined. Based on the modified variable cutting force coefficients, the 3D stability of the ball end milling cutter surface has been obtained using full-discretization approach. Combining the results from the cutting experiment and the nonlinear method, the stability prediction based on the modified variable cutting force coefficient can improve the prediction accuracy. The results provide theoretical support for the optimization of the machining process of the cavity-free surface of outer covering parts of mold of automobile. [ABSTRACT FROM AUTHOR]

The axial thermal error of five-axis CNC machine tools is a significant factor affecting the machining accuracy. To predict the axial thermal error during the machining process, a method is proposed to model axial thermal error in the machining space of five-axis machine tools with a double swing table. The comprehensive thermal error model of the machining space is first established, which includes the axial thermal error of the spindle, worktable, and other components. The spindle is then simplified as a rod, and an analytical model for the axial thermal error of the spindle is developed based on the heat transfer governing equations. This model enables the determination of the time-varying behavior of the axial thermal error at different speeds. Furthermore, the worktable is simplified as a circular plate, and the analytical model for the axial thermal error of the worktable is established based on the thermal bending differential equation of the small deflection circular plate. This model allows for the determination of the time-space variation of the axial thermal error at different radii of the worktable. Finally, based on the axial comprehensive thermal error field of the five-axis CNC machine tool processing space, the distribution of the axial thermal error in the machine tool processing space under thermal equilibrium conditions is revealed. Experimental verification of the proposed model is conducted on the VMC-C50 double swing five-axis CNC machine tool. The experimental results show that the error between the measured axial thermal error value and the axial comprehensive thermal error model value is within 26.6%, thus confirming the accuracy of the proposed model. [ABSTRACT FROM AUTHOR]

In the milling process of thin-walled parts, chatter is very easy to occur, which has a very adverse impact on the surface quality and machining efficiency of the workpiece. In order to solve the problem of low accuracy of milling state identification caused by few initial samples and dynamic changes in the milling process, a hybrid online evolutionary chatter identification model combining unsupervised learning and supervised learning was proposed. First of all, aiming at the problem that traditional K-means algorithm is difficult to adapt to online dynamic clustering of milling chatter, an online incremental-sparse K-means algorithm (ISK-Means) was proposed, and the dynamic incremental-sparse strategy of K-means was designed. Second, aiming at the problem that the online sequential extreme learning machine (OS-ELM) algorithm directly adds its predicted samples to the training sample set during the incremental learning process, and the pseudo-samples in the training sample set would lead to the degradation of the OS-ELM model, a hybrid online evolutionary chatter identification model combining the ISK-means and the OS-ELM was proposed, and the online identification and evolution strategy was designed. Finally, the experimental results show that the ISK-means algorithm can greatly improve the clustering efficiency and is suitable for milling chatter online dynamic clustering. Meanwhile, compared with the existing model, the recognition accuracy of the hybrid online evolutionary chatter recognition model combined with ISK-means algorithm and OS-ELM algorithm is improved by 1.31%. This is of great significance for the online control of subsequent chatter. [ABSTRACT FROM AUTHOR]

In this study, the zero-valent iron Fenton reagent (ZVI Fenton-like) system was combined with the chemical precipitation method for the deep treatment of desulfurization wastewater from coal-fired power plants, and the chemical oxygen demand (COD) was used as the evaluation criterion for organic matter in the desulfurization wastewater. The effects of reaction time, H2O2 dosage, zero-valent iron dosage, pH, and reaction temperature were also investigated. The results showed that the COD concentration of the effluent was the lowest when the running time of the ZVI Fenton-like reagent system was 1 h, the dosage of H2O2 was 33.3 mg·L−1, the dosage of iron was 0.075 g·L−1, the pH was 4.5~6.5, the reaction temperature was 35 °C, the COD concentration of the wastewater was the lowest and its operating conditions were the best, and the internal reaction mechanism was finally deduced. In summary, the zero-valent iron Fenton reagent system provides a new idea for the treatment of desulfurization wastewater from coal-fired power plants. [ABSTRACT FROM AUTHOR]

This paper presents a new damping boring bar with a variable stiffness dynamic vibration absorber (VSDVA) based on dynamic vibration absorber theory. The new designed VSDVA is installed in a cavity which is in the front of the damping boring bar. A new vibration reduction method is proposed by adjusting the stiffness of the VSDVA to adapt the different cutting parameters. The dynamic model of the damping boring bar system is established. The influences of the excitation frequency and the overhang length of the VSDVA on the amplitude ratio are discussed, and the optimal curves are obtained. The damping zone and non-damping zone are obtained as well. These analyses provide a theoretical basis for the design of the damping boring bar and the choice of cutting parameters. The proposed damping boring bar is tested with excitation experiments, impact tests, and boring experiments. The results show that the vibration is reduced, which verifies the feasibility of the proposed method. The accuracy of the dynamic model is substantiated by comparing the experimental data with the theoretical data. [ABSTRACT FROM AUTHOR]

This paper proposes a kind of milling chatter stability prediction method used for the stability of milling free-form surface based on the time-domain. Firstly, a dynamic equation is established by considering the influence of mold surface curvature and cutting tool lead angle on dynamic chip thickness without deformation. Then, the multi-delay milling system vibration displacement, which is given by the ratio of dynamic chip thickness and the static chip thickness as the threshold, was calculated based on the numerical method. Finally, the chatter stability domain based on the full-discretization method of milling chatter stability domain is compared to analyze the influence of the characteristics of free surface curvature on the chatter stability domain. The results of the experiment show that the time-domain simulation method can reveal the influence of different processing areas of free-form surface mold on the instability mechanism of the system. The change trend of milling chatter stability domain was found to be consistent with the experimental results. [ABSTRACT FROM AUTHOR]

The premature failure of cemented carbide cutting tools is one of the main technical challenges in interrupted cutting. During the interrupted cutting process, the cutting edges of cemented carbide cutting tools are more susceptible to early fracture and failure than in the continuous process. This paper presents the results of an experimental study on the cutting performances of cemented carbide cutting tools without coatings, in interrupted cutting. Analysis is performed on the relevant destabilizing features and the fracture formation mechanism. Special workpieces were carried out in the cutting experiments, and relevant working states were monitored by a high-speed camera. The 3D force analyzer and thermal imaging instruments were combined, and the mechanical and thermal shock loads were collected, showing the evolution process of tool failure behaviors and further revealing the regularity of the destabilizing behaviors of the cemented carbide cutting tools. Laser confocal scanning microscopy and scanning electron microscopy were performed to observe tool cracks and micro-morphologies of the fracture surface of the cutting tools, which further demonstrated the failure mechanisms at different cutting stages. The cutting speeds and feed rates corresponding to different failure modes are regionally divided using statistical analysis, and the parameter area for safe cutting for cemented carbide tools is obtained. This study provides methods, data, and theoretical reference for the design of cemented carbide tools and the choice of cutting parameters. [ABSTRACT FROM AUTHOR]

The post-processor is an important interface that transforms cutter location data into numerical control (NC) data. The data compensation in a five-axis machine is quite complex, because of a variety of the machine centers and the computerized numerical control (CNC) system. Since most work on the five-axis post-processor method has dealt primarily with the generation of NC code, this study breaks with tradition and introduces a post-processor with optimization tool radius compensation and a general machine configuration. Furthermore, a practical method for optimizing the NC code is presented that is based on further study of tool compensation and tool wear. The proposed post-processor is validated for various five-axis machine centers using a generalized kinematic model and various cutting tool models. The results of the verification tests showed that proposed post-processor approach can be used to accurately convert the cutter location into NC codes, and the optimized NC code generated by the optimization tool radius compensation method demonstrates the practical value of the proposed approach for improving processing quality and reducing the total machining time and cost. [ABSTRACT FROM AUTHOR]

It has been demonstrated that chatter has a negative effect on machined surface quality during milling process. The identification of chatter is crucial for reducing its influence on machining integrity, especially for the milling process of curved surface. Change of cutter position will cause chip thickness to vary in milling process. Therefore, different contact characteristic between milling cutter and workpiece will be brought out. Compared with the traditional predictive model, a model for predicting chatter stability considering this contact characteristic is proposed in this study. Stability lobe diagrams at different cutter positions are proposed to predict milling stability by employing full-discrete method. A series of experiments are carried out to calibrate and verify the stable lobe diagram. The predicting result is in good agreement with experimental investigation for the stability characterization of curved surface milling process. The model proposed in this study could be used to improve machined quality for curved surface. [ABSTRACT FROM AUTHOR]

This paper presents models related to the manufacturing of ball-nosed end mills of solid carbide (BEMSC) with a chamfered cutting edge (CCE). A parallel grinding wheel (PGW) is selected, and the relationship between CCE face and PGW working face is determined. Based on the geometry models of BEMSC established in our previous work, the centre and axis vectors of PGW are calculated for the grinding of CCE face on bath the ball-nosed end and the cylinder, which is validated through a numerical simulation. In order to produce the tool, a grinding machine, SAACKE UMIF, is chosen. Targeting the grinding data of BEMSC, the transformations are carried out between the coordinate systems of workpiece and the NC programme according to the structural features of the machine. An algorithm is derived for dispersing grinding paths. As a result, the centre data and axis vector are generated with respect to the grinding machine. The BEMSC with CCE is machined using the selected machine, which demonstrates the correctness of the established models. Finally, the performance of the machined cutting tool is validated in comparison with a common BEMSC without CCE in the milling of a mould of a multi-hardness joint structure. [ABSTRACT FROM AUTHOR]

This paper presents a new kind of cutting tool named as ellipsoid end mill to raise machining efficiency in numerical control (NC) milling. Two types of ellipsoid end mills are developed, i.e. cylindrical ellipsoid end mill and tapered ellipsoid end mill. Three models of cutting edges (the cutting edge with constant helix angle, the orthogonal spiral cutting edge and a novel cutting edge) are established, and the corresponding numerical simulation results are also provided in graphical representation, respectively. Manufacturing mathematical models in 2-axis NC machining are established in detail for ellipsoidal end mill. The axial feed speed and the radial feed speed for 2-axis NC machining are determined accurately. Mathematical model of grinding wheel's profile in 2-axis NC machining is provided, as well as correlative computer simulation results. Computer simulation results demonstrate the veracity of these mathematical models. An ellipsoid end mill is manufactured to verify the feasibility of ellipsoid end mill. Finally, a contrast experiment is provided to verify the applicability of ellipsoid end mill (EEM) and the advantage of EEM compared with ball end mill. [ABSTRACT FROM AUTHOR]

The longevity of hard alloy indexable cutting inserts used to cut metal parts is significantly related to their proper geometric design. Such a relationship is outstanding in an intermittent cutting scenario. One solution is to use a strengthened cutting insert with a negative chamfered edge. This method has proven to be effective in avoiding chipping and brittle fractures. However, it does have other effects, like a larger cutting force and a higher cutting heat. In the present research, a set of intermittent cutting experiments were conducted through combinations of two grades of hard alloy indexable inserts and multiple geometric parameters of cutting edges. A three-dimensional dynamometer and a high-speed camera were used to monitor and collect data on the impact and cutting force during the process. A study over the strengthening mechanism of the negative chamfer to the cutting insert was conducted using statistical analysis, which revealed the influence of those geometric parameters of cutting edges on the insert's lifetime. Further, it gave a quantified relationship between the negative chamfer parameter and the feed. This conclusion serves as both data and technical support for the design enhancement of hard alloy indexable inserts. [ABSTRACT FROM AUTHOR]

In this paper, the observer-based H ∞ control problem for networked control systems is investigated. Moreover, network-induced time-varying delays are considered to model the real world applications. The Luenberger-type observer is first designed for state estimation, then the observer-based controller is further developed. By applying the Lyapunov–Krasovskii functional approach, delay-dependent conditions are established to ensure that the prescribed H ∞ performance can be achieved. Finally, the effectiveness and usefulness of our obtained theoretical results are demonstrated by two examples. [ABSTRACT FROM AUTHOR]

High quality and high efficiency are very important factors in the mold industry and must be considered in machining technology development. Corners are one of the typical features in a cavity mold that can cause low accuracy, tool wear, cutter defection, and vibration. Therefore, off-line feedrate optimization in a corner-milling process has been proposed as a means for avoiding these drawbacks. The corner-milling process could be divided into five stages according to the contact conditions between the cutter and workpiece. An improved chip thickness model based on the actual trajectory of the cutter was presented to predict the movement of the cutter along a curved path. Various feedrate optimizations were presented with varying control parameters. In this paper, three feedrate optimizations were implemented using three control parameters, relative volume, milling force, and cutter deflection, based on the differential element method (DEM). A corner-milling force experiment indicated that the simulation results using a modified milling force model agreed well with actual experimental milling results. Following this, a feedrate optimization with multiple constraints was built, according to a combination of three feedrate optimizations and then consider the acceleration and deceleration limitation. The simulation by using the method in this paper demonstrated that the machining time of rough milling, semi-finish milling, and finish milling could be reduced. In addition, it was found that the resulting milling force varies within a narrow range. [ABSTRACT FROM AUTHOR]

Focusing on the feed rate limitation and high cutting force of regular torus milling cutter in machining dies, a double-circular-arc torus milling cutter is designed and fabricated in this work. The geometric characteristics, cutting force, and milling process of the milling cutter are analyzed and presented. The mathematical models of the cutting edge and radius of the main-circular-arc cutting edge are built. To ensure the cutting performance of the milling cutter, a grinding method combining theory, simulation, fabrication, and test is proposed for the fabrication of the milling cutter. The grinding model is built through coordinate transformation. The evaluation of grinding accuracy is proposed. The milling cutter is shaped on a five-axis NC grinding machine, and its accuracy is tested by a tool test center. The test results show the feasibility of the grinding process and the grinding accuracy is ensured. To verify the cutting performance of the proposed double-circular-arc torus milling cutter, milling experiments are conducted. The cutting force and surface roughness of ball-end milling cutter, regular torus milling cutter, and two double-circular-arc torus milling cutters are measured and compared. It is shown that the double-circular-arc torus milling cutters can generate low cutting force and low surface roughness. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyze the variation of temperature field, pressure field and deformation of hydrostatic thrust bearing under different working conditions, so as to provide a theoretical basis for improving accuracy and reliability. Design/methodology/approach: In this study, the double rectangular hydrostatic bearing of type Q1-224 was selected as the research object, and the simulation was carried out according to different working conditions, and the obtained data were summarized regularly. Findings: It is found that the overall temperature of hydrostatic bearing increases with the increase of speed and load, and the increase in load will result in a larger pressure distribution which first increases and then decreases with the speed. The deformation trend of the deformation field is found, and it is found that the force deformation is larger than the thermal deformation at low rotational speed, and the thermal deformation is larger than the force deformation at high rotational speed. Originality/value: In this study, the fluid-structure coupling method of conjugate heat transfer is applied to study the whole hydrostatic bearing. Most of the previous studies only studied the oil film and considered the influence of the convective heat transfer between the hydrostatic bearing and the air in heat transfer, which is rarely seen in the previous research literature. [ABSTRACT FROM AUTHOR]

Corner is one of the typical characteristics of aerospace monolithic components. In order to achieve a stable processing environment and reduce the mutation of milling force in the process of machining, the manufacturer has been searching a method to control the milling force of corner machining. Based on the cutting contact relationship and the instantaneous chip thickness, the milling force prediction model of corner machining is established. By analyzing different types of corner contact relationship, the influence of cutting contact angle and cutting arc length on milling force is established. The corner feed rate iterative calculation method is used to improve the corner milling force mutation. The corner milling experiment proves that the proposed method can not only effectively control the corner milling force, but also improve the surface quality of the bottom surface of the workpiece. The proposed method is suitable for automatic programming of multi-corner pocket machining and provides theoretical support for stable machining of frame part. [ABSTRACT FROM AUTHOR]

Conical end milling cutter is used more and more widely in NC machining. The flute is an important feature of the milling cutter, which directly affects the cutting performance of the milling cutter. However, the conical flute belongs to a complex spatial helix surface. In the machining process, the relative motion of the wheel and milling cutter is complex. To solve this problem, this paper presents a trajectory calculation method for grinding the flute of conical milling cutters. Firstly, the conical cutting edge line is deduced, and the position of the wheel in the whole grinding process is calculated under the design requirements of the rake angle and the core radius. Secondly, a method for calculating the flute profile of complex flutes is given. Finally, the effectiveness of this method is verified by numerical simulation, and it can also be extended to more complex flute grinding in future research. [ABSTRACT FROM AUTHOR]

Tool wear has an important impact on the machining process. During machining, except for the wear accumulation in time, the tool path and posture directly influence the wear spatial distribution characters. In this study, the influence of tool path and posture on tool wear distribution is introduced by describing the shape and posture of the undeformed cutting unit under different tool path and posture. Three dimensional wear matrix ergodic method is proposed to describe the spatial distribution characteristics of tool wear under arbitrary tool path, posture and machined surface. Then, the wear spatial distribution model of ball end milling tool is built based on work theory and verified by ruled surface milling of semi-open impeller. Finally, related simulation and discussion are made based on the model. • The spatial distribution law of tool wear under the comprehensive influence of tool path and machined surface is revealed. • The arbitrary representation of tool path and machined surface is proposed. • The discrete representation method of tool wear and its traversal method based on machining path are proposed and applied. • The characteristics of tool wear in typical tool paths are verified and discussed. [ABSTRACT FROM AUTHOR]

Tool condition monitoring (TCM) in machining operations is crucial to maximise the useful tool life while reducing the risks associated with tool breakage. Unlike progressive tool wear, tool breakage occurs randomly, with more severe implications for workpiece quality, machining system stiffness, and even operator safety. Existing literature reviews on TCM focus on tool wear monitoring, including wear state recognition and remaining useful life prediction. However, a comprehensive review of tool breakage monitoring (TBM) techniques is lacking. Generic signal processing and intelligent decision-making methods cannot fully satisfy the practical requirements of the TBM. In addition, developing and evaluating TBM models using imbalanced data is more challenging. Herein, we present the first systematic review on TBM to bridge these limitations, and provide adequate guidance for avoiding catastrophic tool failures during cutting processes. Signal acquisition, feature extraction, and decision-making methodologies for the TBM are outlined and compared with related techniques for tool wear monitoring. The effects of data imbalance on TBM models are considered, and feasible solutions are provided at the data and algorithm levels. Finally, the challenges faced by the TBM are discussed, and potential research directions are suggested. The research and application of TBM techniques will certainly better empower various machining operations in response to intelligent manufacturing demands. [Display omitted] • Tool breakage monitoring techniques in machining operations are systematically reviewed. • The difference between tool wear and breakage monitoring are compared. • TBM data imbalance problems are discussed at the data and algorithm levels. • The performance metrics commonly used to evaluate TBM models are highlighted. • The specific research route of tool life cycle management is proposed. [ABSTRACT FROM AUTHOR]

Titanium alloys are widely used in aerospace industrial components characterised by high material removal rate, of which the machining efficiency is a big issue. Targeting the problem, this paper presents the experimental findings of milling of titanium alloy TC11 using polycrystalline diamond (PCD) cutting tool at high feed rate. First, in order to verify the capability of PCD in finish milling of titanium alloys at high feed rate, the surface roughness R is investigated under different PCD tool geometries (radial rake angle, axial rake angle and insert sharp radius), and the results indicate that its range is from 0.821 to 1.562 μm, which is suitable to titanium components. Also, the main tool failure patterns, cutting edge fracture and flank face wear, are observed and classified. Based on the tool failure patterns, the relationship between tool life and tool geometries is established. In order to explain the reasons of tool failures, the relationships between cutting forces and the tool geometries are made clear. Finally, the processes of flank face wear and rake face wear of PCD insert are proposed to show its wear evaluations. [ABSTRACT FROM AUTHOR]

Chatter is a kind of self-excited vibration that often occurs in the milling process of thin-walled parts, which has become the main factor restricting production efficiency and quality. Due to the occurrence of chatter, the signal becomes more complex and unstable. In order to realize milling chatter detection of thin-walled parts, the method of multi-sensor signal fusion is used. A chatter detection method based on variational mode decomposition (VMD) and nonlinear dimensionless index is proposed by analyzing the characteristics of signals in time–frequency domain. Firstly, a series of intrinsic mode function (IMF) components are obtained by decomposing force and acceleration signals with VMD. When chatter occurs, the energy is transferred to the chatter frequency band. Each IMF signal's nonlinear energy entropy (EE) is extracted to construct the feature vector. A support vector machine chatter identification model based on multi-sensor signal fusion is established. To solve the problem of model incremental updating, supervised learning and unsupervised learning are combined to provide a method for chatter detection. [ABSTRACT FROM AUTHOR]

• A data-driven tool wear recognition and prediction approach is proposed. • A radar map feature fusion method is proposed to obtain tool health indicator. • The Adaboost-DT is developed for tool wear state recognition. • The SBiLSTM enables quantitative prediction of tool wear with limited data input. Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A data-driven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes. [ABSTRACT FROM AUTHOR]

Ca-calmodulin (CaM) dependent protein kinase II (CaMKII) is an important intracellular signal transduction pathway. CaMKII is rich in the primary sensory neurons and specifically presents in the small- and medium-sized neurons. It remains unclear about the modulation on the excitability of primary sensory neurons by Ca-CaM-CaMKII pathway. By current clamp recording, we found that the excitability of capsaicin-sensitive small and medium trigeminal ganglion (TG) neurons was significantly reduced by a CaM specific antagonist (W-7) and a CaMKII antagonist (KN-93). The inhibition is represented as the reduction of numbers of action potential (AP), decrease of the amplitude of AP, increase of threshold, and prolongation of duration of AP. Consistently, by voltage clamp recording, we found that both voltage-gated sodium channels (VGSCs) and voltage-gated potassium channels (VGPCs) were inhibited by W-7 and KN-93 in the order of total sodium (Na) current (INa-T) > sustained potassium (K) current (IK) > A-type K current (IA). In addition, AIP (a selective CaMKII peptide inhibitor) and KN-93 caused a similar inhibition of INa-T and IK. Those evidences show that the excitability of capsaicin sensitive small and medium TG neurons can be regulated by Ca-CaM-CaMKII pathway through modulating VGSCs and VGPCs. Considering the specific distribution of CaMKII and its susceptibility to many analgesic stimuli, Ca-CaM-CaMKII pathway may play an important role in the peripheral sensory transduction, especially in nociception. [ABSTRACT FROM AUTHOR]

In the present study, a new method is proposed to separate machining errors of a 5-axis machine tool with a double turntable. Moreover, a high-precision compensation model is established to calculate machining errors of thin-walled parts in a 5-axis machine tool. In this regard, a comprehensive error model of the S-shaped sample part considering geometric, thermal, and machining errors is established. Then based on the homogeneous transformation matrix, the geometric error models of translational axes and the A and C axes of the rotation axis are established. Accordingly, geometric errors of each motion axes are identified and measured. Meanwhile, the thermal error of the spindle is measured, and the thermal error model of the machine tool along X/Y/Z directions is established using the multiple linear regression method. Finally, the S-shaped sample is processed and the on-machine measurement of the sample surface is carried out. Based on the obtained machining error from the separation method, the distribution of the total surface error at different curvatures of the S-shaped sample is analyzed, and the distributions of geometric and thermal errors of the machine tool are obtained. It is found that when the geometric and thermal errors of the machine tool are compensated, the measured machining error of each point of the S-shaped sample can be reduced by 10–15 µm compared with that before compensation. [ABSTRACT FROM AUTHOR]

Abstract: A synchronous fluorescence spectroscopy (SFS) method was developed for determination of pyrene in the gills of exposed fish. The wavelength differences (Δλ) of 50nm was maintained between excitation and emission wavelengths and it was found to be suitable for the effective determination of pyrene in fish gills; the peak were observed at λ ex 334.5nm. Linear relationships between SFS intensity and the concentration of pyrene in n-hexane solution were established. It was demonstrated that the SFS method was effective, simple, and less expensive, providing an attractive alternative for the rapid analysis of pyrene in fish gills. [Copyright &y& Elsevier]

Abstract: A series of single β-phase nano-Ag1−x Cu x I (x =0–0.5) solid solutions powders were synthesized by wet-chemical–chelating reaction processing and citric acid used as complexing agent. The Ag1−x Cu x I powders were determined by X-ray diffraction and transmission electron microscopy. It was demonstrated that the crystalline size and lattice parameter of the Ag1−x Cu x I powders decrease with an increase in the amount of CuI substitution. The copper in the lattice of the Ag1−x Cu x I can effectively prevent the crystalline growth of the Ag1−x Cu x I powders and citrate used in the Ag1−x Cu x I powders synthesized process can accelerate single β-phase crystalline structure formation. [Copyright &y& Elsevier]