Tao Wang’s research while affiliated with Shenzhen University and other places

Evaluation of Milling cutter health is important to guarantee workpiece accuracy and productivity efficiency. The traditional evaluation criterion of milling cutter health is not accurate due to the lack of three-dimensional (3D) information, particularly near the tool’s tip area. This study is aimed to evaluate smaller milling cutter health by the tooltip volumetric wear parameters. Firstly, the 3D point cloud of milling cutter tip was obtained using shape-from-focus method; then the volumetric wear parameters of milling cutter’s tip were extracted from the 3D point cloud. Secondly, the effectiveness of using volumetric wear parameters to evaluate milling cutter health was verified by comparing the VB (the max width of flank wear) value and the wear volume on workpiece surface roughness. The experimental results showed that the variation trend of wear volume and VB value was similar. Additionally, the surface roughness experience showed that the wear volume’s change was more noticeable than VB value in the some case of surface roughness changes of the workpiece. This observation proves that volumetric wear parameters are effective to characterize milling cutter wear health. This study provides a new approach to evaluate milling cutter health.

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model’s performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model’s mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.

Borosilicate glass has good light transmittance and stable chemical property, which is an ideal material for microfluidic chip matrix, However, brittle fracture is easy to occur during processing, which is a typical material difficult to process. This limits the surface accuracy. In recent years, longitudinal torsional ultrasonic vibration–assisted milling (LTUVAM) has been proposed as an effective way to machine hard and brittle materials. To elucidate the mechanism of material removal methods and elaborate the material removal mechanism during longitudinal torsional ultrasonic vibration–assisted milling process, this paper established a 3D morphology prediction model to investigate the material removal method. Firstly, the three-dimensional surface morphology model was created by describing the intricate tool path and the workpiece surface morphology reconstruction process under ultrasonic conditions while also considering the tool run-out effect. The model exhibited a certain degree of scalability, enabling the acquisition of various process morphology prediction models by altering the tool coordinates via matrix manipulation. Secondly, the accuracy of the model was verified by longitudinal torsional ultrasonic processing experiments. Thirdly, longitudinal torsional ultrasonic vibration–assisted milling experiments and longitudinal ultrasonic vibration–assisted milling experiments were conducted, and the surface roughness of the machined workpieces was measured. The experimental results show that LTUVAM further reduces the roughness compared with LUVAM, indicating that the reduction ratio reaches a maximum value of 61.06% at the spindle speed of 16,000 RPM, and then, the reduction ratio decreases to 42.88% with the increase of spindle speed. Combining modeling and experimental results, we concluded that LTUVAM was more advantageous in achieving plastic material removal and enhancing the surface quality of the machined surface compared to LUVAM. And under the same process conditions, as the spindle speed increases, the surface roughness of the workpiece increases first and then decreases.

Wall impingement, particularly liquid–wall impingement, has been demonstrated to be one of the critical causes of combustion deterioration in plateau diesel engines. Obviously, the complexity of wall impingement is exacerbated by the plateau scenario. However, fundamental studies specifically dedicated to this phenomenon are still inconclusive and insufficiently detailed, obviating the feasibility of the targeted design and optimization of diesel engines operating in regions with different altitudes. Consequently, the second part of this investigation, presented in this work, focused on the detailed physical and chemical processes of impinging spray combustion under different altitude conditions. A wall impingement system was designed to generate an impinging spray flame. The impingement distance was varied from 77 to 37 mm to cover different situations of wall impingement. The liquid spray, ignition, and combustion processes were visualized in detail by using different optical diagnostics. The results showed that the variation of the liquid length with the impingement distance was mainly dependent on the liquid impingement under the same altitude condition. The effect of the impingement distance on the ignition distance was more sensitive to the altitude. The quantitative analysis of the flame natural luminosity confirmed the decisive effect of the impinging flame morphology on the ambient entrainment and fuel–air mixing under different altitude conditions, and it also revealed that there was an optimal impingement distance under identical altitude conditions to achieve minimum soot emissions. And interestingly, the optimal impingement distance increased with altitude. Finally, the spray combustion processes of an impinging diesel jet were determined to occur in four typical regions, upon which a schematic diagram depicting the flame structure of an impinging diesel jet was proposed to phenomenologically describe the role of altitude in impinging spray combustion processes. Based on this, an attempt was made to explore some new perspectives beyond the popular solutions to recover and improve the performance of plateau diesel engines.

Heavy-duty diesel engines operating in plateau regions experience deteriorated combustion. However, the lack of up-to-date information on the spray-combustion process limits the fundamental understanding of the role of altitude. In this work, the in-cylinder thermodynamic conditions of a real diesel engine operating under different altitudes were reproduced in a constant-volume combustion chamber (CVCC). The liquid spray, ignition, and combustion processes were visualized in detail using different optical diagnostics. Apart from predictable results, some interesting new findings were obtained to improve the understanding of free spray-combustion processes with different altitudes. The spatial distributions of ignition kernels provided direct evidence of higher peak pressure rise rates for high-altitude diesel engines. The percent of stoichiometric air was calculated to confirm that the net effect of altitude was an increase in the amount of air-entrained upstream of the lifted flame; therefore, the soot levels deduced from flame images were inconsistent with those from real engines, revealing that accelerating the soot oxidation process could effectively reduce engine soot emissions in plateau regions. Finally, a novel schematic diagram of the spray flame structure was proposed to phenomenologically describe the role of altitude in influencing the spray-combustion process of a free jet.

Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.

In precision machining, the milling tool’ geometry has a great influence on the milled surface quality. The research on milling tool state monitoring was mainly based on one-dimensional signals and two-dimensional images, which could indirectly obtain the tool state and wear area, but it could not provide the volume of milling tool wear and breakage area, thereby making it difficult to achieve quantitative analysis tool wear. This paper proposed a three-dimensional (3D) reconstruction method of the milling tool tip, it could build a 3D model of the milling tool tip, and then the volume of the wear and breakage region of the milling tool tip was extracted by the 3D model. Firstly, the focusing degree of image sequence’s pixels was calculated based on the non-subsampled discrete shearlet transform (NSST) and Laplace algorithm, and the 3D reconstruction of the milling tool tip was completed according to the shape-from-focus (SFF) principle; secondly, the depth values were optimized by fitting the focusing degree curve of pixels in the image sequence with Gaussian function; finally, the volume of the 3D point cloud of the milling tool tip was calculated by the Simpson double numerical integration method, and the material loss in the damaged region could be obtained. In the 3D reconstruction experiment of the milling tool tip, comparing the different focus degree evaluation operators of SFF, the proposed 3D reconstruction method has the least noise and the best performance in the root-mean-square error, correlation, and smoothness indexes.

The initial response of PETN under the coupling of preheating, impact and defects was simulated by Multiscale Shock Technique (MSST) method and molecular dynamics. The temperature change of PETN during impact compression can be divided into three stages: (1) the elastoplastic change of the system caused by initial compression; (2) part of PETN decomposes and releases energy to raise temperature; (3) a secondary chemical reaction occurs, resulting in rapid temperature rise. Under the given conditions, a higher initial preheating temperature will lead to faster decomposition of PETN; The existence of defects will accelerate the decomposition of PETN molecules; Coupling the highest preheating temperature with defects will lead to the fastest decomposition of PETN molecules, while in the defect-free PETN system with a preheating temperature of 300 K, the decomposition of PETN molecules is the slowest. For the case of Us = 8 km·s⁻¹, the effect of defects on the initial PETN reaction is greater than the initial preheating temperature; When the impact velocity is greater than 9 km·s⁻¹, the impact velocity is an important factor affecting the decomposition of PETN molecules. For Us = 10 km·s⁻¹, NO2 is the main initial product in the defective PETN crystal, while in the perfect PETN crystal, it is the combination of NO2 and HONO. The chemical reaction kinetics analysis shows that the preheating temperature and defects will accelerate the decomposition of PETN. The higher the preheating temperature, the faster the decomposition of PETN. For the case of Us = 7 km·s⁻¹, 8 km·s⁻¹ and 9 km·s⁻¹, the existence of defects will increase the decomposition rate by more than 50% regardless of the initial preheating temperature. In the case of Us = 10 km·s⁻¹, the improvement of decomposition rate by defects is not as significant as the initial preheating temperature.

In precision machining, the milling tool’ geometry has a great influence on the milled surface quality. The research on milling tool state monitoring was mainly based on one-dimensional signals and two-dimensional images, which could indirectly obtain the tool state and wear area, but it could not provide the volume of milling tool wear and breakage area, thereby making it difficult to achieve quantitative analysis tool wear. This paper proposed a three-dimensional (3D) reconstruction method of the milling tool tip, it could build a 3D model of the milling tool tip, and then the volume of the wear and breakage region of the milling tool tip was extracted by the 3D model. Firstly, the focusing degree of image sequence’s pixels was calculated based on the non-subsampled discrete shearlet transform (NSST) and Laplace algorithm, and the 3D reconstruction of the milling tool tip was completed according to the shape-from-focus (SFF) principle; secondly, the depth values were optimized by fitting the focusing degree curve of pixels in the image sequence with Gaussian function; finally, the volume of the 3D point cloud of the milling tool tip was calculated by the Simpson double numerical integration method, and the material loss in the damaged region could be obtained. In the 3D reconstruction experiment of the milling tool tip, comparing the different focus degree evalution operators of SFF, the 3D point cloud obtained by this paper's proposed 3D reconstruction method has the least noise and the best performance in the root-mean-square error, correlation, and smoothness indexes. In addition, compared with Genmagic software, the 3D point cloud volume calculation method adopted in this paper could accurately calculate the 3D point cloud volume of the milling tool tip, and the percentage error was less than 1%.