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Phosphorus (P) possesses the highest theoretical specific capacity (865 mA h g−1) among all the elements for potassium‐ion battery (PIB) anodes. Although Red P (RP) has intrinsic advantages over its allotropes, including low cost and nontoxicity, and simpler preparation, it is yet unknown to effectively activate it into a high‐performance PIB anode. Here, high‐performance RP PIB anodes are reported. Two important factors are found to facilitate RP react with K‐ions reversibly: i) nanoscale RP particles are dispersed evenly in a conductive carbon matrix composed of multiwall carbon nanotubes and Ketjen black that provide an efficient electrical pathway and a tough scaffold. ii) The results of X‐ray photoelectron spectroscopy spectrum and the electrochemical performance perhaps show that no PC bond formation is beneficial to allow K‐ions to react with RP effectively. As a result, the RP/C electrodes deliver a reversible specific capacity of ≈750 mA h g−1 and exhibit a high‐rate capability (≈300 mA h g−1 at 1000 mA g−1). RP/C full cells using potassium manganese hexacyanoferrate as cathode show a long cycling life (680 cycles) at a current density of 1000 mA g−1, in addition, a pouch‐type battery is built to demonstrate practical applications. Red phosphorus (RP) is activated for potassium‐ion battery anodes via a facile wet‐ball milling process. Supported by the conductive network composed of multiwall carbon nanotubes and Ketjen black, full cells comprising an RP/C anode and a potassium manganese hexacyanoferrate cathode show a high specific energy density (193 Wh kg−1) that is a high value for K‐ion full cells.

In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise, which can severely limit the accuracy of predictions regarding tool condition. The present work addresses this issue by proposing a novel TCM method that employs only a few appropriate feature parameters of acoustic sensor signals in conjunction with a two-layer angle kernel extreme learning machine. The two-layer network structure is applied to enhance the learning of features associated with complex nonlinear data, and two angle kernel functions without hyperparameters are employed to avoid the complications associated with the use of preset hyperparameters in conventional kernel functions. The proposed TCM method is experimentally demonstrated to achieve superior TCM performance relative to other state-of-the-art methods based on sound sensor data.

Accurate calibration of the milling force coefficients is essential for predicting the milling forces and characterizing the milling state. Cutter run-out is an unavoidable phenomenon in end milling process, which affects the working state of the cutter and the machining accuracy of the work-piece. This paper proposes an optimized method based on the average force method to calibrate milling force coefficients and cutter run-out parameters in the end milling process. The entire calibration procedure is divided into three steps. First, the milling force coefficients are initially calibrated through the utilization of the average force method. Then, further optimization is carried out based on the coefficients calibrated by the average force method; average milling force equations are used, and a calibration procedure is presented by defining an objective function which is utilized to limit the average force error ratios between the calculated results and the measured results. Finally, the milling force coefficients obtained in the second step are used to calibrate the cutter run-out parameters; the instantaneous milling force equations are used, and a calibration procedure is also presented by defining a new objective function with the minimum sum of squared errors between the instantaneous prediction results and the instantaneous measurement results. In comparison to the experimentally measured average milling forces and instantaneous milling force curves, the proposed method exhibits significantly smaller mean force error ratios than those of the average force method, while demonstrating improved agreement with the simulated milling force curves.

Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. However, while considerable research has been conducted in industrial and academic settings, the complexity of milling processes continues to complicate the implementation of TCM. This paper presents a review of the state-of-the-art methods employed for conducting TCM in milling processes. The review includes three key components: (1) sensors, (2) feature extraction, and (3) monitoring models for the categorization of cutting tool states in the decision-making process. In addition, the primary strengths and weaknesses of current practices are presented for these three components. Finally, this paper concludes with a list of recommendations for future research.

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes. _ Graphical Abstract A dynamic surface roughness monitoring system in milling operations of Al 7075 and St 52 is developed based on the ANNs using cutting conditions, vibrations in X and Z directions, and cutting fluid as inputs and surface roughness as output.

The milling process has proven to be a versatile process for the manufacturing of complex components at the macro- and micro-scales. Development of the cutting force prediction model in milling is important for process planning and optimization, as well as the controlling of machining accuracy. The well-established milling force prediction models include analytical, empirical, and numerical models, among which the analytical models are the most useful for characterizing the milling process and enhancing the understanding of the mechanics of the milling process. This paper makes a detailed review on the analytical models for the force predictions in macro- and micro-milling processes by analyzing the calculation modeling of the instantaneous uncut chip thickness (IUCT) and the determination methods of milling force coefficients. The development of the IUCT model starts from the studying of the effects of circular and trochoidal tool tip locus, later tool and workpiece deflections, tool runout, and tool wear, then the effects of cutting tool edge radius and workpiece material properties in the micro-milling process. The methods for cutting force coefficients are summarized and divided into three subgroups: the first is in constant form and obtained from the experiment, the second is expressed as the functions of the depth of cut, while the third is represented as the polynomial form under different influencing factors including machining conditions, tool geometries, and IUCT effect. The modeling laws and the key challenges for milling forces are also discussed for future research.

Numerous studies have focused on determining the optimal machining parameters for various steels, aiming to reduce both energy consumption and the average surface roughness (Ra) of manufactured parts. In this study, a Computer Numerical Control (CNC) milling machine was used to machine AISI 4340 steel bars with varying input parameters, including spindle speed (rpm), depth of cut (in), feed rate (in/min), and HNT concentration (wt.%). A design of experiments based on a three-level Box-Behnken approach was employed to identify the optimal values for these milling parameters. Spindle load (SL) and surface roughness (Ra) of the milled steel bars were measured after each test. A Response Surface Methodology (RSM) model was developed to optimize the input variables, which indicated that the optimal HNT concentration ranged from 0.11 to 0.17 wt.%. The regression models for Ra and SL demonstrated determination coefficients (R²) of 61.65% and 81.64%, respectively. The optimal values were a spindle speed of 920 rpm, a depth of cut of 0.02 in, a feed rate of 10.5 in/min, and an HNT concentration of 0.12 wt.%. The predicted values were 682 nm for Ra and 1.5 kW for SL, while confirmatory experiments resulted in Ra and SL values of 764 nm and 1.6 kW, respectively. These findings show that optimizing machining parameters, combined with the addition of HNTs to cutting fluids, can enhance the surface roughness of milled parts while reducing energy consumption. This optimization provides significant benefits, including reduced production costs, improved part quality, and a lower carbon footprint in the manufacturing process.

Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.

Surface roughness has played a crucial role in determining the quality and performance in service of the machined workpiece. To enhance the performance of the final product, it is necessary to quantify the final surface roughness accurately. To this end, massive physical models and data-driven methods have been devoted to modeling surface roughness. However, a high-performance physical and data-driven surface roughness prediction model is often subject to the complex modeling process and data insufficient in the milling process. To this end, a physics-informed neural network for surface roughness prediction in milling operations is proposed in this paper. By using the proposed method, the physical knowledge can be incorporated into the deep learning prediction model, which can effectively reduce the complexity and data dependencies in the modeling phase. To verify the applicability and accuracy of the model, cutting tests were conducted using various workpieces, cutting tools, and process parameters. The results demonstrated that the proposed method can effectively reduce the data dependence while depicting high performance, which is more reliable to be applied in the manufacturing industries.