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Mechanical properties prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

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Chinese Journal of Mechanical Engineering 2009年第1期22卷 36-41页

作者： LONG Jiangqi LAN Fengchong CHEN Jiqing YU Ping School of Mechanical ＆ Automotive Engineering South China University of Technology Guangzhou 510641 China

For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm （GA） is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters. The training samples＇ parameters and the corresponding joints＇ mechanical properties are supplied to the artificial neural network （ANN） for training. The validating samples＇ experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model＇s prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

关键词： genetic algorithm BP neural network mechanical clinching joint properties prediction

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Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

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Advances in Manufacturing 2024年第3期12卷 591-602页

作者： Yu-Tong Yang Zhong-Yuan Qiu Zhen Zheng Liang-Xi Pu Ding-Ding Chen Jiang Zheng Rui-Jie Zhang Bo Zhang Shi-Yao Huang Key Laboratory for Light-Weight Materials Nanjing Tech UniversityNanjing211816People’s Republic of China Xi’an Jiaotong-Liverpool University Suzhou215000JiangsuPeople’s Republic of China Materials Academy JITRI Suzhou215100JiangsuPeople’s Republic of China Key Laboratory for Light-weight Materials Nanjing University of Science and TechnologyNanjing210009People’s Republic of China College of Materials Science and Engineering Chongqing UniversityChongqing400044People’s Republic of China Collaborate Innovation Center of Steel Technology University of Science and Technology BeijingBeijing100083People’s Republic of China Chongqing Millison Technologies Inc. Chongqing401321People’s Republic of China

High-pressure die casting(HPDC)is one of the most popular mass production processes in the automotive industry owing to its capability for part ***,the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property ***,a methodology for property prediction must be *** characterization,simulation technologies,and artificial intelligence(AI)algorithms were ***,an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg ***,a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model ***,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 *** 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.

关键词： Artificial intelligence(AI) properties prediction High-pressure die-casting(HPDC) Image recognition Machine learning

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Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer properties

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Chinese Journal of Polymer Science 2024年第12期42卷 2059-2068,I0014页

作者： Ivan Zlobin Nikita Toroptsev Gleb Averochkin Alexander Pavlov Center NTI“Digital Materials Science:New Materials and Substances” Bauman Moscow State Technical University2S1 Baumanskaya St.5/1105005MoscowRussia A.N.Nesmeyanov Institute of Organoelement Compounds of Russian Academy of Sciences Vavilova St.28Bld.1119334MoscowRussia

Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer *** date,several approaches have been implemented to represent the chemical structure in machine learning models,among which Mol2Vec embeddings have attracted considerable attention in the cheminformatics community since their introduction in ***,for small datasets,the use of chemical structure representations typically increases the dimensionality of the input dataset,resulting in a decrease in model ***,the limited diversity of polymer chemical structures hinders the training of reliable embeddings,necessitating complex task-specific architecture *** address these challenges,we examined the efficacy of Mol2Vec pre-trained embeddings in deriving vectorized representations of *** study assesses the impact of incorporating Mol2Vec compound vectors into the input features on the efficacy of a model reliant on the physical properties of 214 *** results will hopefully highlight the potential for improving prediction accuracy in polymer studies by incorporating pre-trained embeddings or promote their utilization when dealing with modestly sized polymer databases.

关键词： properties prediction High dimensional embeddings Machine learning Mol2Vec

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