Advances in Manufacturing最新文献_第3页

An AI-assistant health state evaluation method of sensing devices 传感设备的人工智能辅助健康状态评估方法

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-22 DOI: 10.1007/s40436-024-00517-w

Le-Feng Shi, Guan-Hong Chen, Gan-Wen Chen

引用次数: 0

Improved genetic algorithm based on reinforcement learning for electric vehicle front-end structure optimization design 基于强化学习的改进遗传算法在电动汽车前端结构优化设计中的应用

IF 4.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-15 DOI: 10.1007/s40436-024-00495-z

Feng-Yao Lyu, Zhen-Fei Zhan, Gui-Lin Zhou, Ju Wang, Jie Li, Xin He

{"title":"Improved genetic algorithm based on reinforcement learning for electric vehicle front-end structure optimization design","authors":"Feng-Yao Lyu, Zhen-Fei Zhan, Gui-Lin Zhou, Ju Wang, Jie Li, Xin He","doi":"10.1007/s40436-024-00495-z","DOIUrl":"10.1007/s40436-024-00495-z","url":null,"abstract":"<div><p>The structural optimization of electric vehicles involves numerous design variables and constraints, making it a complex engineering optimization task over the past decades. Many population-based evolutionary algorithms encounter issues such as converging to local optima and lacking population diversity when tackling such optimization problems. Consequently, the solutions obtained for the optimization may be flawed or suboptimal. To address these problems, an improved genetic algorithm (GA) based on reinforcement learning is proposed in this paper. The proposed method introduces a population delimitation method based on individual fitness ranking. The population is divided into two parts: the excellent population and the ordinary population, and different selection and cross-mutation methods are applied to each part separately. More efficient crossover and mutation methods are then applied to the ordinary population to enhance the generation of excellent individuals. Furthermore, the proposed approach replaces the traditional fixed crossover and mutation rates with a dynamic selection method based on reinforcement learning to enhance optimization efficiency. A markov decision process model is constructed based on GA environment in this context. The population state determination method and reward method are designed for reinforcement learning in the GA environment, dynamically selecting the most appropriate genetic parameters based on the current state of the population. Finally, the uncertainty in the manufacturing process is introduced into the optimization problem and the case study results demonstrate that the proposed reinforcement learning-based GA significantly outperforms other evolutionary algorithms when applied to solving the structural optimization of electric vehicles.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"12 3","pages":"556 - 575"},"PeriodicalIF":4.2,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-12 DOI: 10.1007/s40436-024-00506-z

Sheng Cai, Zhi-Chao Deng, Jia-Nan Wang, Nan Zhang

{"title":"Dissimilar metals welding processes realized by vaporizing metal foils","authors":"Sheng Cai, Zhi-Chao Deng, Jia-Nan Wang, Nan Zhang","doi":"10.1007/s40436-024-00506-z","DOIUrl":"https://doi.org/10.1007/s40436-024-00506-z","url":null,"abstract":"<p>In high-velocity impact welding (HVIW), vaporizing foil actuator welding (VFAW) can be utilized to join dissimilar metals. In comparison with conventional welding processes, the VFAW process minimizes energy loss, enhances weld strength, and effectively mitigates issues of overheating or material deformation associated with traditional welding methods. In this study, VFAW was utilized to successfully weld three different metal materials (Cu, Al6061-T6, Q235). An accurate smoothed particle hydrodynamics (SPH) model was established based on the experimental results. The impacts of collision angle and velocity of the flyer on the interface morphology of Cu/Al6061-T6 weld were investigated using the SPH method. The experimental results show that with an increase in the collision angle from 0° to 20°, both the wavelength and amplitude of the welding interface significantly increase. The tail vortex phenomenon also becomes more pronounced with the angle of tail rotation caused by particle motion gradually increasing. But when the collision angle exceeds 20°, the wavelength and amplitude of the welding interface tend to stabilize while its influence on tail vortex phenomenon decreases. The tail rotation angle induced by particle motion continues to increase, although at a decreasing rate. When the initial collision angle is kept constant, both the wavelength and amplitude of the welding interface continue to rise with increasing collision velocity up to 900 m/s. The wake vortex phenomenon becomes more pronounced as the number of particles in the jet gradually increases.</p>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"37 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pears classification by identifying internal defects based on X-ray images and neural networks 基于 X 射线图像和神经网络识别内部缺陷，对梨进行分类

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-10 DOI: 10.1007/s40436-024-00512-1

Ning Wang, Sai-Kun Yu, Zheng-Pan Qi, Xiang-Yan Ding, Xiao Wu, Ning Hu

{"title":"Pears classification by identifying internal defects based on X-ray images and neural networks","authors":"Ning Wang, Sai-Kun Yu, Zheng-Pan Qi, Xiang-Yan Ding, Xiao Wu, Ning Hu","doi":"10.1007/s40436-024-00512-1","DOIUrl":"https://doi.org/10.1007/s40436-024-00512-1","url":null,"abstract":"<p>In order to increase the sales and profitability, it is essential to classify the pears according to the external morphology (including shape, color and luster) and internal defects that can be quantitatively detected by various approaches. However, the existing classification methods concentrate mainly on the external quality rather than the internal defects. Therefore, this investigation develops an efficient and accurate classification method that can identify the internal sclerosis and bruises by combining the X-ray non-destructive testing and the convolutional neural network. Initially, the relations between the characteristics of the internal defects, i.e., internal sclerosis and bruises, and the grayscale features of the X-ray images are analyzed to provide the experimental data and demonstrate the theoretical foundations. Then, the X-ray images are processed by resolution reduction, feature enhancement and gradient reconstruction to improve the training efficiency and classification precision. Finally, the 18-layer residual network (ResNet-18) is optimized and trained to identify the internal bruises and sclerosis and classify the pears based on the identification results. It is found that the overall accuracy can reach 96.67% for identifying the bruised and sclerotic pears. The proposed method could also be applied to other fruits for defects identification and quality classification.</p>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"78 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cutting performance and effectiveness evaluation on organic monolayer embrittlement in ductile metal precision machining 韧性金属精密加工中有机单层脆化的切削性能和效果评估

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-10 DOI: 10.1007/s40436-024-00513-0

Chao-Jun Zhang, Song-Qing Li, Pei-Xuan Zhong, Fei-Fan Zhang, Wen-Jun Deng

{"title":"Cutting performance and effectiveness evaluation on organic monolayer embrittlement in ductile metal precision machining","authors":"Chao-Jun Zhang, Song-Qing Li, Pei-Xuan Zhong, Fei-Fan Zhang, Wen-Jun Deng","doi":"10.1007/s40436-024-00513-0","DOIUrl":"https://doi.org/10.1007/s40436-024-00513-0","url":null,"abstract":"<p>In the traditional machining field, the addition of cutting fluid can appropriately reduce cutting forces, dissipate cutting heat, and facilitate the machining process. However, the use of cutting fluids has environmental implications. Recently, a phenomenon known as organic monolayer embrittlement (OME) has been proposed, which could address this issue. OME can reduce cutting forces, enhance surface quality, and improve machining performance without the need for cutting fluids, particularly noticeable in ductile metals like pure copper. This study conducted micro-cutting experiments on pure copper to investigate the microstructural features, cutting performance, chip flow patterns, and the effectiveness of OME. The results indicate that OME alters chip flow patterns from sinuous flow to segmented quasi-periodic micro-fracture flow, resulting in a 42% and 63% reduction in cutting forces for copper materials with different initial hardness. This phenomenon significantly improves surface quality, diminishes surface defects caused by adhesion, and effectively reduces work hardening layers. The study also demonstrates that OME is a physical phenomenon closely related to the adsorption properties of organic catalytic agents and van der Waals interactions. Materials with higher initial hardness exhibit less pronounced OME due to a sufficiently high grain boundary density, impeding dislocation movement during shear deformation and causing a local stress increase at the free surface of the chip. This leads to a change in chip flow patterns, improving machining performance, analogous to the adsorption effect of organic catalytic agents.</p>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"40 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural design and simulation of PDMS/SiC functionally graded substrates for applications in flexible hybrid electronics 应用于柔性混合电子器件的 PDMS/SiC 功能分级基底的结构设计与模拟

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-08 DOI: 10.1007/s40436-024-00510-3

Jian-Jun Yang, Yin-Bao Song, Zheng-Hao Li, Luo-Wei Wang, Shuai Shang, Hong-Ke Li, Hou-Chao Zhang, Rui Wang, Hong-Bo Lan, Xiao-Yang Zhu

{"title":"Structural design and simulation of PDMS/SiC functionally graded substrates for applications in flexible hybrid electronics","authors":"Jian-Jun Yang, Yin-Bao Song, Zheng-Hao Li, Luo-Wei Wang, Shuai Shang, Hong-Ke Li, Hou-Chao Zhang, Rui Wang, Hong-Bo Lan, Xiao-Yang Zhu","doi":"10.1007/s40436-024-00510-3","DOIUrl":"https://doi.org/10.1007/s40436-024-00510-3","url":null,"abstract":"<p>Flexible hybrid electronics possess significant potential for applications in biomedical and wearable devices due to their advantageous properties of good ductility, low mass, and portability. However, they often exhibit a substantial disparity in elastic modulus between the flexible substrate and rigid components. This discrepancy can result in damage to the rigid components themselves and detachment from the substrate when subjected to tensile, bending, or other loads. Consequently, it diminishes the lifespan of flexible hybrid electronics and restricts their broader-scale application. Therefore, this paper proposes a polydimethylsiloxane (PDMS)/SiC functionally graded flexible substrate based on variable stiffness properties. Initially, ABAQUS simulation is employed to analyze how variations in stiffness impact the stress-strain behavior of PDMS/SiC functionally graded flexible substrates. Subsequently, we propose a multi-material 3D printing process for fabricating PDMS/SiC functionally graded flexible substrates and develop an advanced multi-material 3D printing equipment to facilitate this process. Tensile specimens with the functional gradient of PDMS/SiC are successfully fabricated and subjected to mechanical testing. The results from the tensile tests demonstrate a significant enhancement in the tensile rate (from 21.6% to 35%) when utilizing the PDMS/SiC functionally graded flexible substrate compared to those employing only PDMS substrate. Furthermore, the application of PDMS/SiC functional gradient flexible substrate exhibits remarkable bending and tensile properties in stretchable electronics and skin electronics domains. The integrated fabrication approach of the PDMS/SiC functionally graded flexible substrate structure presents a novel high-performance solution along with its corresponding 3D printing methodology for stretchable flexible electronics, skin electronics, and other related fields.</p>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"10 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Separation of fringe patterns in fast deflectometric measurement of transparent optical elements based on neural network-assisted fast iterative filtering method 基于神经网络辅助快速迭代滤波法的透明光学元件快速偏转测量中的条纹分离技术

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-04 DOI: 10.1007/s40436-024-00509-w

Ting Chen, Pei-De Yang, Xiang-Chao Zhang, Wei Lang, Yu-Nuo Chen, Min Xu

{"title":"Separation of fringe patterns in fast deflectometric measurement of transparent optical elements based on neural network-assisted fast iterative filtering method","authors":"Ting Chen, Pei-De Yang, Xiang-Chao Zhang, Wei Lang, Yu-Nuo Chen, Min Xu","doi":"10.1007/s40436-024-00509-w","DOIUrl":"https://doi.org/10.1007/s40436-024-00509-w","url":null,"abstract":"<p>Transparent optical elements play a significant role in optical imaging and sensing, and the form qualities of these elements are critical to the functionalities of opto-electrical equipment. Therefore, rapid measurement of advanced transparent optical devices is urgently needed. Deflectometry, as a commonly used measurement method, has broad applications in form measurement. However, there are some challenges in the reflective deflectometric measurement of transparent elements, such as fringe superposition, low reflectivity, and non-uniform backgrounds, which severely affect the measurement accuracy. To address these issues, a single-frame fringe separation method is proposed for the deflectometric measurement of transparent elements. A fast iterative filtering method is utilized for coarse fringe separation and a convolutional neural network is adopted to solve the information leakage and incomplete fringe separation. The construction of the neural network involves improving and refining the filtering method to achieve precise separation of fringes. The proposed method achieves fringe separation and forms reconstruction of the upper and lower surfaces. Through simulations and experiments, the effectiveness and robustness of the proposed method are demonstrated, and the measurement accuracy can achieve 65 nm root-of-mean-squared-error (RMSE).</p>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"76 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints 预测自冲铆接疲劳寿命和失效模式的数据驱动方法

IF 4.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-07-01 DOI: 10.1007/s40436-024-00498-w

Jian Wang, Qiu-Ren Chen, Li Huang, Chen-Di Wei, Chao Tong, Xian-Hui Wang, Qing Liu

{"title":"A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints","authors":"Jian Wang, Qiu-Ren Chen, Li Huang, Chen-Di Wei, Chao Tong, Xian-Hui Wang, Qing Liu","doi":"10.1007/s40436-024-00498-w","DOIUrl":"10.1007/s40436-024-00498-w","url":null,"abstract":"<div><p>In lightweight automotive vehicles, the application of self-piercing rivet (SPR) joints is becoming increasingly widespread. Considering the importance of automotive service performance, the fatigue performance of SPR joints has received considerable attention. Therefore, this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR joints. The dataset comprises three specimen types: cross-tensile, cross-peel, and tensile-shear. To ensure data consistency, a finite element analysis was employed to convert the external loads of the different specimens. Feature selection was implemented using various machine-learning algorithms to determine the model input. The Gaussian process regression algorithm was used to predict fatigue life, and its performance was compared with different kernel functions commonly used in the field. The results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue life. Among the data points, 95.9% fell within the 3-fold error band, and the remaining 4.1% exceeded the 3-fold error band owing to inherent dispersion in the fatigue data. To predict the failure location, various tree and artificial neural network (ANN) models were compared. The findings indicated that the ANN models slightly outperformed the tree models. The ANN model accurately predicts the failure of joints with varying dimensions and materials. However, minor deviations were observed for the joints with the same sheet. Overall, this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"12 3","pages":"538 - 555"},"PeriodicalIF":4.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning-based calibration method for strength simulation of self-piercing riveted joints 基于机器学习的自冲铆接强度模拟校准方法

IF 4.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-06-25 DOI: 10.1007/s40436-024-00502-3

Yu-Xiang Ji, Li Huang, Qiu-Ren Chen, Charles K. S. Moy, Jing-Yi Zhang, Xiao-Ya Hu, Jian Wang, Guo-Bi Tan, Qing Liu

{"title":"A machine learning-based calibration method for strength simulation of self-piercing riveted joints","authors":"Yu-Xiang Ji, Li Huang, Qiu-Ren Chen, Charles K. S. Moy, Jing-Yi Zhang, Xiao-Ya Hu, Jian Wang, Guo-Bi Tan, Qing Liu","doi":"10.1007/s40436-024-00502-3","DOIUrl":"10.1007/s40436-024-00502-3","url":null,"abstract":"<div><p>This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted (SPR) joints. Strength simulations were conducted through the integrated modeling of SPR joints from process to performance, while physical quasi-static tensile tests were performed on combinations of DP600 high-strength steel and 5754 aluminum alloy sheets under lap-shear loading conditions. A sensitivity study of the critical simulation parameters (e.g., friction coefficient and scaling factor) was conducted using the controlled variables method and Sobol sensitivity analysis for feature selection. Subsequently, machine-learning-based surrogate models were used to train and accurately represent the mapping between the detailed joint profile and its load-displacement curve. Calibration of the simulation model is defined as a dual-objective optimization task to minimize errors in key load displacement features between simulations and experiments. A multi-objective genetic algorithm (MOGA) was chosen for optimization. The three combinations of SPR joints illustrated the effectiveness of the proposed framework, and good agreement was achieved between the calibrated models and experiments.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"12 3","pages":"465 - 483"},"PeriodicalIF":4.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical/experimental investigation of the effect of the laser treatment on the thickness distribution of a magnesium superplastically formed part 激光处理对镁合金超塑性成形部件厚度分布影响的数值/实验研究

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-06-20 DOI: 10.1007/s40436-024-00497-x

Angela Cusanno, Pasquale Guglielmi, Donato Sorgente, Gianfranco Palumbo

{"title":"Numerical/experimental investigation of the effect of the laser treatment on the thickness distribution of a magnesium superplastically formed part","authors":"Angela Cusanno, Pasquale Guglielmi, Donato Sorgente, Gianfranco Palumbo","doi":"10.1007/s40436-024-00497-x","DOIUrl":"https://doi.org/10.1007/s40436-024-00497-x","url":null,"abstract":"<p>The growing need for high-performance components in terms of shape and mechanical properties encourages the adoption of integrated technological solutions. In the present work, a novel methodology for affecting the superplastic behaviour and, in turn, the thickness distribution of magnesium alloy components is proposed. Through heat treatments using a CO<sub>2</sub> laser, the grain size was locally changed, thus modifying the superplastic behaviour in a predefined area of the blank. Both the grain coarsening produced by the laser heat treatment and the superplastic forming of the heat treated blank were simulated using a finite element model, which allowed to set the related process parameters for the manufacturing of the investigated case study (a truncated cone). The thermal finite element model of the laser heat treatment, calibrated using the experimental temperature evolutions acquired in specific areas during the heat treatment, was used to evaluate the influence of process parameters on the grain size evolution. The laser heat treatment was able to significantly promote the grain growth, increasing the mean grain size from about 8 µm to twice (about 17 µm). The resulting grain size distributions were implemented in the mechanical finite element model of the superplastic forming process and the combination of laser parameters which allowed to obtain the most uniform thickness distribution on the final component was finally experimentally reproduced and measured for validation purposes. Even in the case of the laboratory scale application, characterised by quite small dimensions, the proposed approach revealed to be effective, to improving the thinning factor (<i>t</i><sub>MIN</sub>/<i>t</i><sub>AVG</sub>) of the formed part from 0.85 to 0.89, and providing an increase in the thickness uniformity of about 4.7%.</p>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"17 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0