Advances in Manufacturing最新文献_第3页

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 5.2 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":"https://doi.org/10.1007/s40436-024-00502-3","url":null,"abstract":"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.","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":null,"pages":null},"PeriodicalIF":5.2,"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":"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 CO2 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 (tMIN/tAVG) of the formed part from 0.85 to 0.89, and providing an increase in the thickness uniformity of about 4.7%.","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":null,"pages":null},"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

CMAGAN: classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction CMAGAN：针对工业不平衡数据的分类器辅助少数增强生成对抗网络及其在故障预测中的应用

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-06-18 DOI: 10.1007/s40436-024-00496-y

Wen-Jie Wang, Zhao Liu, Ping Zhu

{"title":"CMAGAN: classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction","authors":"Wen-Jie Wang, Zhao Liu, Ping Zhu","doi":"10.1007/s40436-024-00496-y","DOIUrl":"https://doi.org/10.1007/s40436-024-00496-y","url":null,"abstract":"Class imbalance is a common characteristic of industrial data that adversely affects industrial data mining because it leads to the biased training of machine learning models. To address this issue, the augmentation of samples in minority classes based on generative adversarial networks (GANs) has been demonstrated as an effective approach. This study proposes a novel GAN-based minority class augmentation approach named classifier-aided minority augmentation generative adversarial network (CMAGAN). In the CMAGAN framework, an outlier elimination strategy is first applied to each class to minimize the negative impacts of outliers. Subsequently, a newly designed boundary-strengthening learning GAN (BSLGAN) is employed to generate additional samples for minority classes. By incorporating a supplementary classifier and innovative training mechanisms, the BSLGAN focuses on learning the distribution of samples near classification boundaries. Consequently, it can fully capture the characteristics of the target class and generate highly realistic samples with clear boundaries. Finally, the new samples are filtered based on the Mahalanobis distance to ensure that they are within the desired distribution. To evaluate the effectiveness of the proposed approach, CMAGAN was used to solve the class imbalance problem in eight real-world fault-prediction applications. The performance of CMAGAN was compared with that of seven other algorithms, including state-of-the-art GAN-based methods, and the results indicated that CMAGAN could provide higher-quality augmented results.","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505970","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

Improving RSW nugget diameter prediction method: unleashing the power of multi-fidelity neural networks and transfer learning 改进 RSW 金块直径预测方法：释放多保真度神经网络和迁移学习的力量

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-06-18 DOI: 10.1007/s40436-024-00503-2

Zhong-Jie Yue, Qiu-Ren Chen, Zu-Guo Bao, Li Huang, Guo-Bi Tan, Ze-Hong Hou, Mu-Shi Li, Shi-Yao Huang, Hai-Long Zhao, Jing-Yu Kong, Jia Wang, Qing Liu

引用次数: 0

Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms 使用 cGAN 和机器学习算法预测黑色金属材料疲劳特性的数据驱动方法

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-06-03 DOI: 10.1007/s40436-024-00491-3

Si-Geng Li, Qiu-Ren Chen, Li Huang, Min Chen, Chen-Di Wei, Zhong-Jie Yue, Ru-Xue Liu, Chao Tong, Qing Liu

引用次数: 0

Laser welding monitoring techniques based on optical diagnosis and artificial intelligence: a review 基于光学诊断和人工智能的激光焊接监控技术：综述

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-06-03 DOI: 10.1007/s40436-024-00493-1

Yi-Wei Huang, Xiang-Dong Gao, Perry P. Gao, Bo Ma, Yan-Xi Zhang

引用次数: 0

Exoskeleton active assistance strategy for human muscle activation reduction during linear and circular walking 在直线和环形行走过程中减少人体肌肉激活的外骨骼主动辅助策略

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-05-31 DOI: 10.1007/s40436-024-00504-1

Wen-Tao Sheng, Ke-Yao Liang, Hai-Bin Tang

引用次数: 0

Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading 用于铝合金板材索状缺陷分析的深度学习方法：预测与分级

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-05-24 DOI: 10.1007/s40436-024-00499-9

Yuan-Zhe Hu, Ru-Xue Liu, Jia-Peng He, Guo-Wei Zhou, Da-Yong Li

引用次数: 0

A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing 基于机器学习的多目标优化方法，用于提高涡轮叶片制造中蜡型的尺寸精度

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-05-17 DOI: 10.1007/s40436-024-00492-2

Jing Dai, Song-Zhe Xu, Chao-Yue Chen, Tao Hu, San-san Shuai, Wei-Dong Xuan, Jiang Wang, Zhong-Ming Ren

引用次数: 0

Research progress of magnetorheological polishing technology: a review 磁流变抛光技术的研究进展：综述

IF 5.2 2区工程技术

Advances in Manufacturing Pub Date : 2024-05-16 DOI: 10.1007/s40436-024-00490-4

Ming-Ming Lu, Ya-Kun Yang, Jie-Qiong Lin, Yong-Sheng Du, Xiao-Qin Zhou

引用次数: 2