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学者姓名：林述温
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Abstract ：
现有挖掘机主构件参数优化设计方法无法保证挖掘作业全过程多工况位置工作性能最优,且没有考虑作业过程中的能耗特性.研究了以典型工况作业全过程动力和能耗特性为目标的挖掘机工作装置主构件参数多目标优化设计方法.通过对基于ADAMS的参数化挖掘机虚拟样机的动力学仿真,分析了四种典型工况作业过程的液压缸驱动力和功率特性,确定了工作装置的优化工况,建立了新的综合考虑挖掘机典型工况作业过程液压缸驱动力传动比和功率特性的多目标优化数学模型,并结合实例对挖掘机整机工作装置参数化虚拟样机的铲斗、斗杆和动臂主构件参数进行顺序多目标优化.优化结果表明:该优化设计方法可显著提高挖掘机作业全过程工作装置传递动力的性能,降低液压缸能耗.
Keyword ：
主构件参数 主构件参数 多目标优化 多目标优化 挖掘机 挖掘机 虚拟样机 虚拟样机 过程特性 过程特性 顺序优化 顺序优化
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| GB/T 7714 | 林述温 , 陆哲 , 危世佳 et al. 挖掘机工作过程动力特性仿真及主构件参数多目标优化设计方法 [J]. | 中国机械工程 , 2025 , 36 (6) : 1371-1379 . |
| MLA | 林述温 et al. "挖掘机工作过程动力特性仿真及主构件参数多目标优化设计方法" . | 中国机械工程 36 . 6 (2025) : 1371-1379 . |
| APA | 林述温 , 陆哲 , 危世佳 , 陈剑雄 , 顾天奇 , 谢钰 . 挖掘机工作过程动力特性仿真及主构件参数多目标优化设计方法 . | 中国机械工程 , 2025 , 36 (6) , 1371-1379 . |
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To enhance the excavator performance considering the digging force and boom lift force under typical working conditions, this paper aims to solve the complex multiobjective optimization of the excavator by proposing a new knowledge-based method. The digging force at multiple key points is utilized to characterize the excavator's performance during the working process. Then, a new optimization model is developed to address the imbalanced optimization quality among subobjectives obtained from the ordinary linear weighted model. The new model incorporates the loss degree relative to the optimal solution of each subobjective, aiming to achieve a more balanced optimization. Knowledge engineering is integrated into the optimization process to improve the optimization quality, utilizing a knowledge base incorporating seven different types of knowledge to store and reuse the information related to optimization. Furthermore, a knowledge-based multiobjective algorithm is proposed to perform the knowledge-guided optimization. Experimental results demonstrate that the proposed knowledge-based method outperforms existing methods, resulting in an average increase of 15.1% in subobjective values.
Keyword ：
excavator excavator knowledge engineering knowledge engineering multiobjective evolutionary algorithm multiobjective evolutionary algorithm multiobjective optimization multiobjective optimization Optimization design Optimization design
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| GB/T 7714 | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong et al. A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering [J]. | ADVANCES IN MECHANICAL ENGINEERING , 2024 , 16 (1) . |
| MLA | Lu, Zhe et al. "A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering" . | ADVANCES IN MECHANICAL ENGINEERING 16 . 1 (2024) . |
| APA | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong , Gu, Tianqi , Xie, Yu , Zhao, Zihao . A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering . | ADVANCES IN MECHANICAL ENGINEERING , 2024 , 16 (1) . |
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Due to their good approximation accuracy and local fitting characteristics, the moving least squares (MLS) and moving total least squares (MTLS) methods are widely used in various engineering fields. However, neither of these two methods is robust and they cannot effectively deal with outliers in measurement data. To eliminate the negative influence of outliers and achieve robust reconstruction, a novel MTLS method is proposed in this paper, which introduces local Bayesian estimation combined with clustering using representatives (CURE) algorithm. In the support domain, this method adopts a two-step process to remove the abnormal points and adjust the weights of discrete points through compound weighting. Bayesian estimation is first performed on discrete points to derive the reference model, and the residuals are calculated as the input of CURE clustering. The points with large residuals are classified into one cluster and removed. The remaining points undergo repeated processing until the iteration concludes. A gradient weight function based on the residuals and a compact support weight function are combined to determine the final estimated value using weighted Bayesian estimation. The simulations and experiments demonstrate that the proposed reconstruction method achieves excellent accuracy and robustness, surpassing several existing methods when handling highly contaminated datasets.
Keyword ：
Bayesian estimation Bayesian estimation Clustering using representatives Clustering using representatives Measurement data Measurement data Moving total least squares Moving total least squares Outliers Outliers
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| GB/T 7714 | Gu, Tianqi , Kang, Cheng , Tang, Dawei et al. A robust reconstruction method based on local Bayesian estimation combined with CURE clustering [J]. | INFORMATION SCIENCES , 2024 , 680 . |
| MLA | Gu, Tianqi et al. "A robust reconstruction method based on local Bayesian estimation combined with CURE clustering" . | INFORMATION SCIENCES 680 (2024) . |
| APA | Gu, Tianqi , Kang, Cheng , Tang, Dawei , Lin, Shuwen , Luo, Tianzhi . A robust reconstruction method based on local Bayesian estimation combined with CURE clustering . | INFORMATION SCIENCES , 2024 , 680 . |
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Abstract ：
为了研究斗杆结构应力神经网络建模方法,在分析不同典型工况下斗杆结构的应力分布特征基础上,确定斗杆结构的应力特征截面,分析斗杆结构各尺寸参数对各特征截面应力的灵敏度,据此确定斗杆结构应力神经网络模型的输入变量,进行结构应力神经网络建模,并对不同神经网络模型结构进行预测误差对比分析.结果表明:所建立的模型可快速预测斗杆结构特征截面的应力,实训样本预测误差小于 10%.
Keyword ：
BP神经网络 BP神经网络 应力特征截面 应力特征截面 应力预测 应力预测 挖掘机 挖掘机 斗杆结构 斗杆结构
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| GB/T 7714 | 李军 , 罗承旭 , 林述温 . 挖掘机斗杆结构应力神经网络建模 [J]. | 机械制造与自动化 , 2024 , 53 (4) : 154-160 . |
| MLA | 李军 et al. "挖掘机斗杆结构应力神经网络建模" . | 机械制造与自动化 53 . 4 (2024) : 154-160 . |
| APA | 李军 , 罗承旭 , 林述温 . 挖掘机斗杆结构应力神经网络建模 . | 机械制造与自动化 , 2024 , 53 (4) , 154-160 . |
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To address the limitation of existing excavator optimization methods, which primarily focus on the force performance while neglecting energy consumption and fail to realize environmentally friendly and low-carbon designs, this paper proposes a new multi-objective collaborative optimization method for an excavator to reduce energy consumption during the working process while maintaining optimal performance. By formulating a mathematical model that quantifies the energy consumption during the working process, this paper optimizes the working conditions by analyzing the energy consumption characteristics under typical working conditions. To overcome the limitation of existing linear weighting methods in multi-objective optimization, such as imbalanced optimization quality among sub-objectives, this paper proposes a new modeling approach based on the loss degree of sub-objectives. A multi-objective collaborative optimization model for the excavator with reduced energy consumption is established, and a corresponding multi-objective collaborative optimization algorithm is developed and applied to achieve optimal solutions for sub-objectives. The optimization results demonstrate that applying the new multi-objective collaborative optimization method to the excavator achieves better optimization quality than traditional methods. It also provides a more balanced improvement in the optimization values of each sub-objective, resulting in a significant reduction in the energy consumption of the excavator during operation.
Keyword ：
energy consumption modeling energy consumption modeling energy-saving energy-saving excavator excavator excavator working performance excavator working performance multiobjective optimization multiobjective optimization
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| GB/T 7714 | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong et al. A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction [J]. | ENERGIES , 2023 , 16 (20) . |
| MLA | Lu, Zhe et al. "A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction" . | ENERGIES 16 . 20 (2023) . |
| APA | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong , Gu, Tianqi , Xie, Yu . A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction . | ENERGIES , 2023 , 16 (20) . |
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In the past few decades, there has been significant progress made in metasurfaces and integrated and miniatur-ized optical devices. As one of the most prominent applications of metasurfaces, the metalens is the subject of significant research. In this paper, for achieving better focusing performance of the initial metalens designed by the Pancharatnam-Berry (PB) phase, a concept of micro-dimensional oscillation is proposed to optimize the geomet-ric parameters of nanopillars. A strategy of grouping iteration is proposed to reduce the loss rate and computational effort in a holistic way. Its essence is to divide an extremely large-scale optimization space into many overlapping groups. Meanwhile, an improved genetic-simulated annealing (IGSA) algorithm is presented for the optimal solution of each group. By introducing the adaptive crossover and mutation probabilities in traditional genetic algorithms, the IGSA algorithm has both strong global searching capability and excellent local searching capability. After optimization, the maximum field intensity of the central hot spot can be increased by about 8% compared to the initial metalens. Moreover, the field intensity of the side lobes around the hot spot is almost constant, and the central hot spot increases, which provides a potential for the realization of high imaging contrast.(c) 2022 Optica Publishing Group
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| GB/T 7714 | Gu, T. I. A. N. Q. I. , Gao, X. I. A. N. G. , Tang, D. A. W. E., I et al. Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared [J]. | APPLIED OPTICS , 2022 , 61 (32) : 9324-9333 . |
| MLA | Gu, T. I. A. N. Q. I. et al. "Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared" . | APPLIED OPTICS 61 . 32 (2022) : 9324-9333 . |
| APA | Gu, T. I. A. N. Q. I. , Gao, X. I. A. N. G. , Tang, D. A. W. E., I , Lin, S. H. U. W. E. N. , Fang, B. I. N. G. . Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared . | APPLIED OPTICS , 2022 , 61 (32) , 9324-9333 . |
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In the surface profile analysis, there are often a few observations that contain outliers. Due to the existence of outliers, the application of non-robust reconstruction algorithms for measurement data will become a huge problem because these methods are often sensitive to outliers and the approximation effectiveness will be greatly aggravated. In view of this, this paper presents a novel angle-based moving total least squares reconstruction method, to the best of our knowledge, that applies two-step pre-treatment to handle outliers. The first step is an abnormal point detection process that characterizes the geometric features of discrete points in the support domain through a new angle-based parameter constructed by total least square. Then, the point with the largest anomaly degree is removed, and a relevant weight function is defined to adjust the weights of the remaining points. After pretreatment, the final estimates are calculated by weighted total least squares (WTLS) based on the compact weight function. The detection and removal of outliers are automatic, and there is no need to set a threshold value artificially, which effectively avoids the adverse impacts of human operation. Numerical simulations and experiments verify the applicability of the proposed algorithm as well as its accuracy and robustness. (C) 2022 Optica Publishing Group
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| GB/T 7714 | Gu, Tianqi , Xiong, Cui , Tang, Dawei et al. Profile analysis with reconstruction robustness for measurement data subject to outliers [J]. | APPLIED OPTICS , 2022 , 61 (13) : 3777-3785 . |
| MLA | Gu, Tianqi et al. "Profile analysis with reconstruction robustness for measurement data subject to outliers" . | APPLIED OPTICS 61 . 13 (2022) : 3777-3785 . |
| APA | Gu, Tianqi , Xiong, Cui , Tang, Dawei , Chen, Jianxiong , Lin, Shuwen . Profile analysis with reconstruction robustness for measurement data subject to outliers . | APPLIED OPTICS , 2022 , 61 (13) , 3777-3785 . |
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This article is concerned with the reconstruction of contaminated measurement data based on the moving total least squares (MTLS) method, which is extensively applied to many engineering and scientific fields. Traditional MTLS method is lack of robustness and sensitive to the outliers in measurement data. Based on the framework of MTLS method, we proposed a robust MTLS method called RMTLS method by introducing a two-step pre-process to detect and remove the anomalous nodes in the support domain. The first step is an iterative regression procedure that combines with k-medoids clustering to automatically reduce the weight of anomalous node for a regressionbased reference (curve or surface). Based on the distances between reference and discrete points, the second step adopts a density function defined by a sorted distance sequence to select the normal points without setting a threshold artificially. After the two-step pre-process, weighted total least square is performed on the selected point set to obtain the estimation value. By disposing of the anomalous nodes in each independent support domain, multiple outliers can be suppressed within the whole domain. Furthermore, the suppression of multiple continual outliers is possible by adopting asymmetric support domain and introducing previous estimation points. The proposed method shows great robustness and accuracy in reconstructing the simulation and experiment data.
Keyword ：
K-medoids clustering K-medoids clustering Measurement data Measurement data Moving least squares Moving least squares Outlier Outlier
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| GB/T 7714 | Gu, Tianqi , Lin, Hongxin , Tang, Dawei et al. Robust moving total least squares: A technique for the reconstruction of measurement data in the presence of multiple outliers [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 167 . |
| MLA | Gu, Tianqi et al. "Robust moving total least squares: A technique for the reconstruction of measurement data in the presence of multiple outliers" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 167 (2022) . |
| APA | Gu, Tianqi , Lin, Hongxin , Tang, Dawei , Lin, Shuwen , Luo, Tianzhi . Robust moving total least squares: A technique for the reconstruction of measurement data in the presence of multiple outliers . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 167 . |
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The moving least squares (MLS) and moving total least squares (MTLS) are two of the most popular methods used for reconstructing measurement data, on account of their good local approximation accuracy. However, their reconstruction accuracy and robustness will be greatly reduced when there are outliers in measurement data. This article proposes an improved MTLS method (IMTLS), which introduces an improved random sample consensus (RANSAC) algorithm and a correction parameter in the support domain, to deal with the outliers and random errors. Based on the nodes within the support domain, firstly the improved RANSAC is used to generate a model for establishing the group of pre-interpolation and calculating the residual of each node. Subsequently, the abnormal degree of the node with the largest residual is evaluated by the correction parameter associated with the node residual and random errors. The node with certain abnormal degree will be eliminated and the remaining nodes are used to obtain the approximation coefficients. The correction parameter can be used for data reconstruction without insufficient or excessive elimination. The results of numerical simulation and measurement experiment show that the reconstruction accuracy and robustness of the IMTLS method is superior to the MLS and MTLS method.
Keyword ：
Measurement data Measurement data moving total least squares moving total least squares outliers outliers random sample consensus random sample consensus surface reconstruction surface reconstruction
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| GB/T 7714 | Gu, Tianqi , Luo, Zude , Tang, Dawei et al. Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter [J]. | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE , 2022 , 236 (12) : 1589-1600 . |
| MLA | Gu, Tianqi et al. "Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter" . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 236 . 12 (2022) : 1589-1600 . |
| APA | Gu, Tianqi , Luo, Zude , Tang, Dawei , Chen, Jianxiong , Lin, Shuwen . Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE , 2022 , 236 (12) , 1589-1600 . |
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Measuring and evaluating the geometric error of a linear axis periodically, is an essential operation in the day-to-day usage of a machine tool. In this paper, a system consisting of a novel one-dimension probe and a ball array is developed to fast estimate the geometric error a linear axis from the ball center deviations in three dimensions. The proposed 1D probe is assembled by an inductance micrometer and a simple fixture. Five measuring positions on the ball surface are selected to recognize the ball center offset caused by the geometric error. Then, an identification model is established to recognize the error at the ball center in the array. Moreover, a correction method is proposed to eliminate the installation error. It applies the least square method to form the virtual baseline by the measured ball centers, in order to eliminate the effect that resulted from the inaccuracy and the misalignment of the ball array during the manufacturing and setting, respectively. Then, the remaining part of the measured results is applied to evaluate the geometric error of the measured linear axis, including one positioning error and two straightness errors. Finally, a prototype system is developed to verify the correctness of the proposed 1D probe, while a measurement experiment is conducted on a machining center to verify the validity of the proposed method. The results indicate that the maximum absolute error among one positioning error and two straightness errors reach to 2.1 mu m, 2.3 mu m, and 1.6 mu m, respectively, while the root mean square error, and the average absolute error are no more than 2.0 mu m, when comparing with the results from the laser interferometer.
Keyword ：
1D ball array 1D ball array 1D probe 1D probe Error correction Error correction Geometric error Geometric error Linear axis Linear axis
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| GB/T 7714 | Chen, Jianxiong , Lin, Shuwen , Gu, Tianqi . Geometric error identification for machine tools using a novel 1D probe system [J]. | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY , 2021 , 114 (11-12) : 3475-3487 . |
| MLA | Chen, Jianxiong et al. "Geometric error identification for machine tools using a novel 1D probe system" . | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 114 . 11-12 (2021) : 3475-3487 . |
| APA | Chen, Jianxiong , Lin, Shuwen , Gu, Tianqi . Geometric error identification for machine tools using a novel 1D probe system . | INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY , 2021 , 114 (11-12) , 3475-3487 . |
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