Volumetric error decoupling is a critical prerequisite for effective error compensation. In this paper, the forward volumetric error model is established using the screw theory. Additionally, the Jacobian matrix based on the product of exponential is derived to construct the linear relationship between the volumetric error and the axis motion and decouple the volumetric error model. To address the limitation of compensation motion, a step-by-step decoupling method is proposed, where attitude and position errors are compensated sequentially. After detecting the actual geometric errors of the grinding machine, the volumetric error can be determined, and the compensation motion commands for each axis are calculated to correct the volumetric error. The simulation result shows that the mean value of the comprehensive error ranges can be reduced from 19.7 μm to 1.8 μm, demonstrating the effectiveness of the proposed method.

Existing wearable data gloves for gesture recognition often face challenges in achieving both high precision and real-time performance. To address these limitations, we propose a data glove design incorporating fiber Bragg gratings (FBGs) encapsulated in flexible materials and positioned near the interphalangeal joints. This setup enables effective gesture recognition. First, the principle of fiber grating curvature sensing is derived, followed by curvature calibration of the FBG data glove through flexible encapsulation. An experimental platform was constructed to assess the glove's performance in static and dynamic digital gesture recognition. Calibration results demonstrate a linear correlation between wavelength drift and curvature changes, with a comprehensive sensitivity of 0.0126 nm/degrees. Static and dynamic experimental findings confirm that the FBG sensors effectively monitor wavelength shifts induced by finger curvature variations. Analysis of different digital gestures at various time intervals revealed the wavelength offsets corresponding to the straightening and bending states of each finger. The five FBG sensors, encapsulated in flexible materials and positioned at the proximal interphalangeal joints, capture curvature changes across the five fingers, enabling accurate, real-time recognition of both static and dynamic gestures. This study highlights the potential of the developed wearable data glove for tracking and recognizing fine movements of the human hand.

Thermal errors have become the main factor affecting the machine tool accuracy. Statistical prediction and compensation models are commonly established based on the measured temperature data to reduce thermal errors. Current literature typically focuses on first selecting temperature-sensitive points (TSPs) to reduce multicollinearity and then building thermal error models for CNC machines. Thus, this two-step approach loses useful information after the variable selection and prevents thermal error models from using this lost information. In addition, there are few approaches that can simultaneously reduce multicollinearity through variable selection and model thermal errors in one single step. Therefore, to fill the research gap, a one-shot thermal error modeling and prediction approach is proposed based on the individually penalized ridge regression (IPRR). Specifically, the traditional ridge regression method, which intrinsically fails to select TSPs, is adopted and modified by setting up individually penalized ridge parameters for each variable, thereby achieving both variable selection and thermal error modeling simultaneously. Then, the proposed IPRR algorithm is compared using the experimental data with the existing methods. The comparison results show that the IPRR algorithm can significantly improve the prediction accuracy by 10

Welding is a critical process in numerous industrial production scenarios. To ensure safe production, defects that occur during welding must be timely and effectively identified. Deep learning has been widely used in welding defect identification due to its powerful feature extraction capabilities, but in environments with limited computing resources, large deep learning models are difficult to deploy. Thus, a lightweight model for welding defect recognition should be devised while maintaining high accuracy under limited computational resources. Accordingly, we propose a weld defect recognition model called LSRSNet and based on a lightweight architecture. Specifically, based on the SqueezeNet model, our model uses the lightweight linear deformable convolution to simplify the model and improve the recognition accuracy. Moreover, a squeeze-and-excitation attention mechanism is integrated into the Fire module of the original SqueezeNet to enhance the feature expression ability, and a residual structure is constructed to prevent degradation of the deep network and improve the learning ability of complex features. Experimental results show that compared with the original model, the accuracy of the improved LSRSNet on a weld defect dataset increases by 9.5%, with notably fewer parameters than the original model. We offer a novel algorithmic for welding defect recognition and believe that LSRSNet can be deployed for industrial testing.

Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature-sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable-forgetting factor-based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFF-RLS method can maintain a high prediction accuracy of 1.75 μm and robustness of 0.16 μm on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments.

Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

机床热误差预测模型在不同工况下难以保持高预测精度是导致热误差实际补偿效果差的重要原因,对此本文提出一种基于迁移学习的异工况下机床热误差建模方法.首先利用核均值匹配算法获取不同工况下机床温度数据间的迁移权重,从而提出基于迁移学习的热误差建模方法;对不同工况下热误差数据进行差异显著性检验,并利用本文所提方法建立热误差预测模型,分析建模效果;然后比对分析本文所提建模方法与常用建模方法的实际预测效果,最后进行补偿验证实验以证明本文所提方法的有效性.结果表明,本文所提基于迁移学习的建模方法能够有效提升建模效果,其中迁移学习结合LASSO算法针对不同工况下热误差数据的预测精度和稳健性分别达到 3.73 和 1.14 μm,补偿后机床X/Y/Z 3 个方向热误差分别保持在-2.3～3.1 μm、-3.4～3.9 μm和-3.3～4.6 μm范围内.

Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extreme gradient boosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models.

Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC machine tools based on the adaptive Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms. Specifically, the adaptive LASSO method enjoys the oracle property of selecting temperature-sensitive variables. After the temperature-sensitive variable selection, the XGBoost algorithm is further adopted to model and predict thermal errors. Since the XGBoost algorithm is decision tree based, it has natural advantages to address the multicollinearity and provide interpretable results. Furthermore, based on the experimental data from the Vcenter-55 type 3-axis vertical machining center, the proposed algorithm is compared with benchmark methods to demonstrate its superior performance on prediction accuracy with 7.05 μm (over 14.5% improvement), robustness with 5.61 μm (over 12.9% improvement), worst-case scenario predictions with 16.49 μm (over 25.0% improvement), and percentage errors with 13.33% (over 10.7% improvement). Finally, the real-world applicability of the proposed model is verified through thermal error compensation experiments.

Thermal errors are one of the main factors affecting the accuracy of high-precision computer numerical control machine tools. Modeling and compensation are the most common approaches for reducing the influence of thermal error on machine tool accuracy. Accuracy and robustness are the key indicators of machine tool thermal error prediction models, especially under different working conditions. Existing thermal error modeling algorithms provide only point predictions of the thermal error; however, interval predictions of the thermal error are important for understanding the stochastic nature of the thermal error prediction and analysis of reliable risk. To address these challenges, this study proposes a novel thermal error modeling method based on Gaussian process regression (GPR) that provides interval predictions of thermal error and achieves high prediction accuracy and robustness. First, multiple batches of experimental data are used to establish the GPR thermal error model to ensure sufficient modeling information. Second, while existing methods select temperature-sensitive points (TSPs) before modeling, the GPR algorithm can adaptively select TSPs during training of the thermal error GPR prediction model. Third, the proposed model provides interval predictions of thermal errors for evaluating the thermal error prediction reliability. The prediction effects of the GPR model are compared with those of existing thermal error models. The experimental results indicate that the proposed model has the highest prediction accuracy and robustness under different working conditions of the tested compensation models. Furthermore, thermal error compensation experiments are conducted to verify the effectiveness of the proposed model.

针对目前应用于电子皮肤的触觉传感器不能兼具柔韧性和多模态信息感知等问题,对封装于同一柔性聚合物传感单元中的两根光纤光栅触觉传感器的材质识别功能进行了有限元仿真和实验研究.首先,推导了光纤光栅触觉传感原理和基于热传递的接触物体材质识别机理;然后,对封装材料和接触物体进行了热力学仿真分析;最后,搭建了实验系统平台,对光纤光栅柔性触觉传感器进行了材质识别实验研究.仿真和实验结果表明,当接触物体为70℃的铝、铁、塑料等材质时,由于热传递引起的光纤光栅温度传感器中心波长漂移最大值分别为:△λB1=0.588 9 nm、△λB2=0.277 3 nm和△λB3=0.169 2 nm,且在接触的前10 s内中心波长漂移随时间变化率分别为k1=31 pm/s、k2=19 pm/s和k3=6 pm/s.扩展光纤光栅触滑觉传感器的接触物体材质识别功能,可实现电子皮肤更多模态信息的感知,具有一定的应用价值.

针对火力发电机组因结构复杂、指标众多且难以量化导致的燃烧检测仪器综合性能评价难以实现的问题,提出了一种基于信息熵-灰色模糊融合模型的火电机组燃烧检测仪器综合性能评价方法,建立了信息熵-灰色模糊评价模型.以发电机组飞灰含碳量工程范例的3种检测方式对比验证所提方法,采用模糊数学量化仪器性能、适用性能、风险性能3类语言标度,以从优隶属度规范化11项指标建立模型输入,用灰色关联系数矩阵综合考量输入量之间的耦合关联度,以熵权法替代传统专家法,剔除赋权过程的主观影响.对比结果表明,3种方案的二级指标权重在去人为干扰环境下,分别占总体的14.63％、58.51％、26.86％,对最优结果1的隶属度分别达到0.4265、0.6642和0.9673.与单因素分析方法相比,该模型在评价结果上具有一致性,而在多数据分析和去人为干扰中表现出更好的综合评价效能.

Chatter has been playing an important role in the stability and quality assurance of milling processes. To effectively predict the stability of milling processes, in this paper, we investigate multiple milling parameters, including the spindle speed, axial milling depth, radial milling width, milling cutter radius, chip thickness, and feed rate. The influence mechanism of these parameters on milling stability is analyzed systematically. Based on the analysis results, three-dimensional (3D) stability lobe diagrams (SLDs) under multiple milling parameters can be obtained, which provide a theoretical basis of preventing and suppressing chatter. The effectiveness of the SLDs is verified by the actual milling experiment. Compared with the traditional two-dimensional SLDs, the proposed 3D SLDs with multiple parameters are more comprehensive, accurate and practical, which show important theoretical significance and engineering application value for the chatter stability prediction and control.

For computer numerical control (CNC) machine tools, thermal error is currently compensated according to a single fixed point on the worktable. When the worktable is in motion, the thermal error obviously differs across the worktable due to manufacturing, assembly, and other factors from driving and supporting devices. This causes the thermal error compensation by the single-point (SP) method to have great uncertainty for the whole worktable. To solve this problem, this paper proposes a sub-regional (SR) method to compensate for the thermal error of a worktable. The SR method divides the worktable into different regions and establishes a thermal error compensation model for each region, which are then combined to compensate for the thermal error of the whole worktable. The influence of the number of regions on the compensation effect of the SR method was analyzed theoretically and validated experimentally to provide a basis for determining a reasonable number of regions. The prediction effects of the SR method were compared with the SP method. And the compensation effects of the SR method were compared with a two dimensional thermal error map compensation method. The experimental results showed that the SR method has high prediction accuracy and stability and has better compensation effect in practical application for the whole worktable.

The modeling and compensation method is a common method for reducing the influence of thermal error on the accuracy of machine tools. The prediction accuracy and robustness of the thermal error model are two key performance measures for evaluating the compensation effect. However, it is difficult to maintain the prediction accuracy and robustness at the desired level when the ambient temperature exhibits strong seasonal variations. Therefore, a year-round thermal error modeling and compensation method for the spindle of machine tools based on ambient temperature intervals (ATIs) is proposed in this paper. First, the ATIs applicable to the thermal error prediction models (TEPMs) under different ambient temperatures are investigated, where the C-Means clustering algorithm is utilized to determine ATIs. Furthermore, the prediction effect of different numbers of ATIs is analyzed to obtain the optimal number of ATIs. Then, the TEPMs corresponding to different ATIs in the annual ambient temperature range are established. Finally, the established TEPMs of ATIs are used to predict the experimental data of the entire year, and the prediction accuracy and robustness of the proposed ATI model are analyzed and compared with those of the low and high ambient temperature models. The prediction accuracies of the ATI model are 20.6% and 41.7% higher than those of the low and high ambient temperature models, respectively, and the robustness is improved by 48.8% and 62.0%, respectively. This indicates that the proposed ATI method can achieve high prediction accuracy and robustness regardless of the seasonal temperature variations throughout the year.

In combination with the training tasks and objectives of engineering certification, integrate the curriculum ideological and political education ideas into the course of control instruments and apparatus, fully explore the elements of ideological and political education contained in the knowledge points of the course, and cultivate students’ feelings of home and country, craftsman spirit, dialectical thinking, legal consciousness, moral norms as well as sustainable development consciousness, so as to improve the quality of talent training.

通过建立预测模型对机床热误差进行补偿,是有效解决热误差造成机床精度下降问题的常用方法.本文提出一种基于正则化的数控机床热误差自适应稳健建模算法,能够在建模过程中自适应选择温度敏感点(TSPs),并具有高预测精度和稳健性.首先基于结构风险最小化原则对热误差建模稳健性机理进行分析,进而利用正则化算法中LASSO解的稀疏性实现自适应TSP选择.然后基于不同实验条件的热误差数据,分析所提建模算法的预测效果,并与常用的多元线性回归、BP神经网络和岭回归算法进行比对分析.结果表明,本文所提建模算法具有最高的预测精度和稳健性,分别为5.22和1.69 μm.最后,利用所建立的预测模型进行热误差补偿实验,以验证本文所提建模算法的实际补偿效果.

为了提高数控机床热误差补偿模型的预测精度与稳健性,对主成分算法在数控机床主轴热误差建模中的应用进行了研究。首先,根据主成分算法原理,提出基于主成分分析的温度敏感点选择算法和热误差建模算法。然后,以一台三轴立式加工中心为对象进行全年温度范围内的主轴热误差测量实验,并基于实验数据建立主轴热误差主成分回归（Principal Component Regression,PCR）模型。进而,将所建立的PCR模型与多元线性回归模型、BP神经网络模型和岭回归模型的预测精度与稳健性进行比对分析,实验结果表明PCR模型在该四种模型中具有最高的预测精度和稳健性,分别达到6.8μm和2.4μm。最后,使用所建立的PCR模型对按照转速图谱运行的机床主轴热误差进行预测,预测精度和稳健性分别为6.12μm和3.43μm。并将PCR模型嵌入到热误差补偿控制器中进行热误差补偿实验,以验证本文建模算法的有效性。

建立预测模型对热误差进行预测和补偿是解决机床热误差问题的常用方法,该方法中模型的预测精度和稳健性易受环境温度影响而明显下降,对此本文提出了基于偏最小二乘法的热误差稳健建模算法.首先使用相关系数法筛选温度敏感点,并建立热误差偏最小二乘回归预测模型.进而基于全年环境温度下的多批次热误差实验数据,分析最佳的温度敏感点个数.最后建立热误差偏最小二乘回归模型,并与普通多元线性回归模型的预测效果比对分析.结果表明本文所提算法平均预测精度为5.7 μm,模型稳健性为0.56 μm,相较于普通多元线性回归算法,预测精度和稳健性分别提高13.8％和49.5％.说明本文所提的热误差稳健建模算法能够在环境温度变化较大时保持高预测精度和高稳健性.