查看更多>>摘要：Roping is a severe band-like surface defect that occurs in deformed aluminum alloy sheets.Accurate roping prediction and rating are essential for industrial applica-tions.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 rop-ing 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 rop-ing patterns,the roping dataset constructed from experimen-tal 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 imple-mented 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.Con-sequently,the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.
查看更多>>摘要：High-pressure die casting(HPDC)is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation.How-ever,the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation.Therefore,a methodology for property predic-tion must be developed.Material characterization,simula-tion technologies,and artificial intelligence(AI)algorithms were employed.Firstly,an image recognition technique was employed to construct a temperature-microstructure charac-teristic model for a typical HPDC A17Si0.2Mg alloy.Moreo-ver,a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results.Additionally,the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model,allowing accurate prediction of the property distribution of the HPDC Al-Si alloy.The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.
查看更多>>摘要：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 strat-egy is first applied to each class to minimize the negative impacts of outliers.Subsequently,a newly designed bound-ary-strengthening learning GAN(BSLGAN)is employed to generate additional samples for minority classes.By incorporating supplementary classifier and innovative training mechanism,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.
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维普