Advances in Manufacturing最新文献_第3页

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

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 4.2 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":"10.1007/s40436-024-00496-y","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"12 3","pages":"603 - 618"},"PeriodicalIF":4.2,"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 4.2 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 4.2 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

{"title":"Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms","authors":"Si-Geng Li, Qiu-Ren Chen, Li Huang, Min Chen, Chen-Di Wei, Zhong-Jie Yue, Ru-Xue Liu, Chao Tong, Qing Liu","doi":"10.1007/s40436-024-00491-3","DOIUrl":"10.1007/s40436-024-00491-3","url":null,"abstract":"<div><p>The stress-life curve (S–N) and low-cycle strain-life curve (E–N) are the two primary representations used to characterize the fatigue behavior of a material. These material fatigue curves are essential for structural fatigue analysis. However, conducting material fatigue tests is expensive and time-intensive. To address the challenge of data limitations on ferrous metal materials, we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous materials. In addition, a data-augmentation framework is introduced using a conditional generative adversarial network (cGAN) to overcome data deficiencies. By incorporating the cGAN-generated data, the accuracy (<i>R</i><sup>2</sup>) of the Random Forest Algorithm-trained model is improved by 0.3–0.6. It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"12 3","pages":"447 - 464"},"PeriodicalIF":4.2,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141254318","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

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 4.2 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 4.2 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

{"title":"Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading","authors":"Yuan-Zhe Hu, Ru-Xue Liu, Jia-Peng He, Guo-Wei Zhou, Da-Yong Li","doi":"10.1007/s40436-024-00499-9","DOIUrl":"10.1007/s40436-024-00499-9","url":null,"abstract":"<div><p>Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets. Accurate roping prediction and rating are essential for industrial applications. Recently, the authors introduced an artificial neural network (ANN) model to efficiently forecast roping behavior across the thickness of large regions with texture gradients. In this study, the previously proposed ANN model for roping prediction is briefly reviewed, and a few-shot learning (FSL)-based method is developed for roping grading with limited samples. To consider the directionality of the roping patterns, the roping dataset constructed from experimental observations is transformed into the frequency domain for more compact characterization. A transfer-based FSL method is further presented for grade roping with manifold mixup regularization and the Sinkhorn mapping algorithm. A new component-focused representation is also implemented for data-processing, exploiting the close correlation between roping and power distribution in the frequency domain. The ultimate FSL method achieved an optimal accuracy of 95.65% in roping classification with only five training samples per class, outperforming four typical FSL methods. This FSL approach can be applied to grade the roping morphologies predicted by the ANN model. Consequently, the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"12 3","pages":"576 - 590"},"PeriodicalIF":4.2,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100324","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 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 4.2 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

{"title":"A multi-objective optimization based on machine learning for dimension precision of wax pattern in turbine blade manufacturing","authors":"Jing Dai, Song-Zhe Xu, Chao-Yue Chen, Tao Hu, San-San Shuai, Wei-Dong Xuan, Jiang Wang, Zhong-Ming Ren","doi":"10.1007/s40436-024-00492-2","DOIUrl":"10.1007/s40436-024-00492-2","url":null,"abstract":"<div><p>Wax pattern fabrication in the investment casting of hollow turbine blades directly determines the dimension accuracy of subsequent casting, and therefore significantly affects the quality of final product. In this work, we develop a machine learning-based multi-objective optimization framework for improving dimension accuracy of wax pattern by optimizing its process parameters. We consider two optimization objectives on the dimension of wax pattern, i.e., the surface warpage and core offset. An active learning of Bayesian optimization is employed in data sampling to determine process parameters, and a validated numerical model of injection molding is used to compute objective results of dimension under varied process parameters. The collected dataset is then leveraged to train different machine learning models, and it turns out that the Gaussian process regression model performs best in prediction accuracy, which is then used as the surrogate model in the optimization framework. A genetic algorithm is employed to produce a non-dominated Pareto front using the surrogate model in searching, followed by an entropy weight method to select the most optimal solution from the Pareto front. The optimized set of process parameters is then compared to empirical parameters obtained from previous trial-and-error experiments, and it turns out that the maximum and average warpage results of the optimized solution decrease 26.0% and 20.2%, and the maximum and average errors of wall thickness compared to standard part decrease from 0.22 mm and 0.051 7 mm using empirical parameters to 0.10 mm and 0.035 6 mm using optimized parameters, respectively. This framework is demonstrated capable of addressing the challenge of dimension control arising in the wax pattern production, and it can be reliably deployed in varied types of turbine blades to significantly reduce the manufacturing cost of turbine blades.</p></div>","PeriodicalId":7342,"journal":{"name":"Advances in Manufacturing","volume":"12 3","pages":"428 - 446"},"PeriodicalIF":4.2,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966118","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