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Laser triangulation (LT) is widely used in many fields due to its good stability, high resolution and fast speed. However, the accuracy in these applications suffers from severe constraints on the data acquisition accuracy of LT. To solve this problem, the optical triangulation principle, the object equation of the optical path relationship and the deviation of the laser spot centroid are applied to deduce a mathematical model. Therefore, the image sensor inclination errors can be quantitatively calculated, and the collected data are compensated in real time. Further, a threshold sub-pixel gray-gravity (GG) extraction algorithm is proposed; the gradient function and Gaussian fit algorithm are used to set thresholds to remove the impact of the spot edge noise area on the center location; and polynomial interpolation is employed to enhance the data density of the traditional GG method, thus improving the data acquisition accuracy of LT. Finally, the above methods are applied to on-machine measurement of the American Petroleum Institute (API) thread and the screw rotor, respectively. The experimental results prove that the proposed method can significantly improve the measurement accuracy of free-form curved surfaces using LT and that the improved laser spot center extraction algorithm is more suitable for free-form curved surfaces with smaller curvature and more uniform curvature changes.

The inconvenient loading and unloading of a long and heavy drill pipe gives rise to the difficulty in measuring the contour parameters of its threads at both ends. To solve this problem, in this paper we take the SCK230 drill pipe thread-repairing machine tool as a carrier to design and achieve a fast and on-machine measuring system based on a laser probe. This system drives a laser displacement sensor to acquire the contour data of a certain axial section of the thread by using the servo function of a CNC machine tool. To correct the sensor’s measurement errors caused by the measuring point inclination angle, an inclination error model is built to compensate data in real time. To better suppress random error interference and ensure real contour information, a new wavelet threshold function is proposed to process data through the wavelet threshold denoising. Discrete data after denoising is segmented according to the geometrical characteristics of the drill pipe thread, and the regression model of the contour data in each section is fitted by using the method of weighted total least squares (WTLS). Then, the thread parameters are calculated in real time to judge the processing quality. Inclination error experiments show that the proposed compensation model is accurate and effective, and it can improve the data acquisition accuracy of a sensor. Simulation results indicate that the improved threshold function is of better continuity and self-adaptability, which makes sure that denoising effects are guaranteed, and, meanwhile, the complete elimination of real data distorted in random errors is avoided. Additionally, NC50 thread-testing experiments show that the proposed on-machine measuring system can complete the measurement of a 25 mm thread in 7.8 s, with a measurement accuracy of ±8 μm and repeatability limit ≤ 4 μm (high repeatability), and hence the accuracy and efficiency of measurement are both improved.

Great length, large weight and other factors may cause difficulty in measuring the profile accuracy of the double-headed screw rotor. To solve this problem, an on-machine measuring system based on a laser-displacement sensor (LDS) was designed and implemented in this paper by taking an LXK100 four-axis whirlwind milling machine as the carrier. To improve the measurement accuracy of the system, the generalized variable-structural-element morphological method, polynomial interpolation algorithm and ellipse fitting method were first combined to realize the rapid subpixel centroid extraction from a noise-containing spot image, thus improving the data acquisition accuracy of the LDS, and then the hybrid method was experimentally verified. Next, a wavelet threshold function with high-order differentiability and adaptive wavelet coefficient contractility was constructed based on the hyperbolic tangent function, so as to inhibit the disturbance from random errors and preserve real profile information, and this method was simulated and verified. Subsequently, a smoothing algorithm for point cloud data was proposed based on the Lagrange multiplier method to avoid the defect of the piecewise curve-fitting method, that is, function continuity and differentiability could not be satisfied at piecewise points. Finally, the profile accuracy was calculated in real time according to the data reconstruction result and the machining quality was judged. The measurement experiment of the double-headed screw rotor indicates that the proposed on-machine measuring system can complete the profile accuracy measurement for a screw pitch within 39.7 s with measurement accuracy reaching ±8 μm, and the measurement uncertainties of the major axis, minor axis and screw pitch are 0.72 μm, 0.69 μm and 1.24 μm, respectively. Therefore, the measurement accuracy and efficiency are both remarkably improved.

Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recognition method based on image expansion and Dynamic Model-Agnostic Meta-Learning (DMAML) is proposed. In terms of data augmentation, a multi-connected conditional generative network is designed for generating palmprints; the network is trained using a gradient-penalized hybrid loss function and a dual time-scale update rule to help the model converge stably, and the trained network is used to generate an expanded dataset of palmprints. On this basis, the palmprint feature extraction network is designed considering the frequency domain and residual inspiration to extract the palmprint feature information. The DMAML training method of the network is investigated, which establishes a multistep loss list for query ensemble loss in the inner loop. It dynamically adjusts the learning rate of the outer loop by using a combination of gradient preheating and a cosine annealing strategy in the outer loop. The experimental results show that the palmprint dataset expansion method in this paper can effectively improve the training efficiency of the palmprint recognition model, evaluated on the Tongji dataset in an N-way K-shot setting, our proposed method achieves an accuracy of 94.62% ± 0.06% in the 5-way 1-shot task and 87.52% ± 0.29% in the 10-way 1-shot task, significantly outperforming ProtoNets (90.57% ± 0.65% and 81.15% ± 0.50%, respectively). Under the 5-way 1-shot condition, there was a 4.05% improvement, and under the 10-way 1-shot condition, there was a 6.37% improvement, demonstrating the effectiveness of our method.

This study characterised the hot-press forming process of long carbon fibre PA6 materials using laminates prepared from UD-CA708A prepregs manufactured by Nanjing Special Plastic Composites Materials Co., Ltd. In order to investigate the resin flow behaviour during the hot compression moulding process, a unified model integrating the material forming and resin flow sequences was established by Lagrangian and Eulerian discretization methods. Simultaneous measurements by rotational and torsional rheometers revealed that in-plane fibre flow dominated, and the long carbon fibre PA6 material showed anisotropic behaviour. The anisotropic viscosity tensor principal model was used to characterise this anisotropy, the parameters of which were determined experimentally by the rheometer. Based on these findings, a unified modelling approach for material forming and resin flow was developed and applied to simulation analysis. The validity of the anisotropic viscosity intrinsic model and the unified simulation framework is verified by integrating the rheological analysis, in-mold analysis, and evaluation of the microstructure and mechanical properties of the moulded specimens, which provides a technical framework and a strategy for the application of the model in complex geometries.

This study proposes the use of Physics-Informed Neural Networks (PINNs) to further advance the curvature interference analysis method. The nonlinear equation system encountered in determining the curvature interference limit line is embedded into the PINN loss function, thereby enabling the solution of high-dimensional, nonlinear equations. Computational results demonstrate that the PINN model achieves a solution accuracy on the order of 10−13 when solving multidimensional nonlinear systems, which is comparable to the classical Fsolve algorithm. The curvature interference analysis reveals the presence of two curvature interference boundary lines, although they rarely extend to the worm gear tooth surface. A study on the influence of design parameters on the interference boundaries indicates that the axial installation distance has the greatest impact. Inadequate axial spacing causes the interference limit line to shift toward the inner end of the worm gear, significantly increasing the risk of interference in that region. The proposed curvature interference analysis method based on PINNs can be extended to other types of gear drives. It also lays the foundation for future work on establishing both forward and inverse mappings between design parameters and curvature interference using PINNs.

Functional films have been widely utilized in flexible electronics, biosensing, and energy devices owing to their unique mechanical compliance and excellent interfacial transport efficiency. However, balancing structural precision with processing efficiency remains a key challenge in the scalable fabrication of uniform, low‐aspect‐ratio architectures across diverse material systems. Here, wide‐flow aerosol jet printing (WF‐AJP), a high‐throughput printing method capable of generating planar aerosol jets, is introduced. Reshaping the nozzle into a flattened rectangular geometry generates a collimated planar flow that enables millimeter‐scale deposition in a single pass (aspect ratio < 1:6000, thickness < 500 nm), a capability unattainable by conventional circular‐nozzle printing. Systematic cross‐scale characterization of the film formation process further reveals a delicate balance between macroscopic morphology and functional performance. A staggered deposition strategy that reduces interlayer accumulation and lowers electrical anisotropy is further implemented. Applications in conformal electrodes and skin‐interfaced sensors highlight the versatility of WF‐AJP for wearable and bio‐integrated electronics. This work establishes a structure‐controlled and scalable patterning paradigm for functional films, paving the way for future innovations in high‐throughput additive manufacturing. Wide‐flow aerosol jet printing (WF‐AJP) employs an anisotropic nozzle design to generate planar aerosol jets, enabling millimeter‐scale deposition of highly uniform, ultra‐low aspect ratio (<1:6000) films with substantially reduced anisotropy in functional performance. WF‐AJP establishes a structure‐controlled and inherently scalable patterning paradigm for functional films, paving the way for future advances in high‐throughput additive manufacturing.

Some natural rubber farmers mistakenly equate the ecological functions of rubber plantations with those of primary forests. This cognitive bias can hinder pesticide reduction efforts. Meanwhile, natural rubber farmers gain security through price insurance, which helps them adopt a long-term perspective on environmental protection, mitigating the negative impact of cognitive biases on pesticide use decisions. However, existing research often overlooks the influence of ecological cognitive bias on pesticide reduction and the moderating role of price insurance satisfaction. This study utilizes field survey data from Hainan and employs logit models and double machine learning models to empirically analyze the impact of ecological cognitive bias on pesticide reduction among natural rubber farmers. It further tests the underlying mechanisms using moderation models. The results indicate that (1) ecological cognitive bias negatively affects pesticide reduction among natural rubber farmers, and (2) price insurance satisfaction mitigates the impact of ecological cognitive bias on pesticide reduction. Based on these findings, the government should enhance education and training to raise ecological awareness among natural rubber farmers and reduce ecological cognitive biases. Disseminating knowledge about price insurance and establishing a comprehensive insurance system can secure farmers’ income and promote the rational use of pesticides.

The vibration isolator equipped with a negative stiffness corrector (NSC) excels at vibration isolation, but its stiffness often presents complex nonlinearity which needs to be approximated in calculation. To avoid the harmful effects of approximate stiffness, the NSC formed by the cam-roller mechanism with a quadratic polynomial trajectory (QCRM) is proposed to construct the vibration isolation system. From the inherent geometrical relationship in the structure, the generation mechanism of high-static-low-dynamic stiffness is analyzed, and the quasi-zero stiffness (QZS) condition of the system is derived. Based on the dynamic model of the QZS vibration isolator, the functions of response characteristics are solved by the harmonic balance method. Then, the absolute displacement transmissibility with different parameter values, and the vibration isolation performance under sinusoidal, multi-frequency wave, and random excitations are discussed. The simulated results show that the stiffness expression of the proposed QZS vibration isolator is directly a quadratic function, which removes the calculation error caused by approximate stiffness at large displacement and broadens the available isolation displacement range. Introducing the QCRM-NSC can significantly suppress the low-frequency vibration and resonance response without changing the load-bearing capacity of the vibration isolator. Under various excitations, the vibration isolation performance of the QZS vibration isolator all outperforms the linear counterpart.

The thermal error is a significant factor that influences the machining accuracy of machine tools, and error compensation is an economical and effective method for enhancing the accuracy of machine tools. However, establishing a precise thermal error prediction model is crucial for thermal error compensation. In this paper, the subtraction-average-based optimizer-based CNN-GRU neural network (SABO-CNN-GRU) is applied to integrated thermal error modeling. Through conducting a thermal characteristic experiment, temperature rise data and thermal error data were collected from the linear feed system of LXK300X helical groove CNC machine tool. The fuzzy c-means clustering and grey correlation analysis are employed to identify temperature-sensitive points in the linear feed system. By utilizing the temperature rise data from these sensitive points along with feed shaft thermal errors as data samples, and using the SABO algorithm to optimize the CNN-GRU prediction model, the thermal error prediction model of SABO-CNN-GRU is established. To validate its superiority and practicality, a comparative analysis is conducted with traditional thermal error prediction models based on CNN-GRU and SO-ELM. The results demonstrate that SABO-CNN-GRU model outperforms both models in terms of mean absolute error (MAE), root mean square error (RMSE), remaining prediction deviation (RPD), mean square error (MSE), and determination coefficient ( R 2 ) in accurately predicting results. Building upon this achievement, this paper develops a real-time thermal error compensation system which effectively reduces maximum thermal errors from 80.5 to 17.6 μm after implementing compensation measures. Effectively reducing the influence of thermal errors and improving the machining accuracy of machine tools.