The majority of vehicle structural failures originate from joint areas.Cyclic loading is one of the primary factors in joint failures,making the fatigue performance of joints a critical consideration in vehicle struct...The majority of vehicle structural failures originate from joint areas.Cyclic loading is one of the primary factors in joint failures,making the fatigue performance of joints a critical consideration in vehicle structure design.The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries.Therefore,there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry.In this work,we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis(FEA)results.Different machine learning(ML)algorithms are adopted to investigate the fatigue life of three typical adhesive joints,namely lap shear,coach peel and KSII joints.After the feature engineering and tuned process of the ML models,the preferable model using the Gaussian process regression algorithm is established,fed with eight input parameters,namely thicknesses of the substrates,line forces and bending moments of the adhesive bonded joints obtained from FEA.The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation.It demonstrates that for life prediction of adhesive joints,the data-driven solutions can constitute an improvement over conventional solutions.展开更多
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 fa...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(R2)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.展开更多
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 join...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.展开更多
基金funded by the Construction Project of the National Natural Science Foundation(Grant No.52205377)National Key Research and Development Program(Grant No.2022YFB4601804)Key Basic Research Project of Suzhou(Grant Nos.#SJC2022029,#SJC2022031).
文摘The majority of vehicle structural failures originate from joint areas.Cyclic loading is one of the primary factors in joint failures,making the fatigue performance of joints a critical consideration in vehicle structure design.The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries.Therefore,there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry.In this work,we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis(FEA)results.Different machine learning(ML)algorithms are adopted to investigate the fatigue life of three typical adhesive joints,namely lap shear,coach peel and KSII joints.After the feature engineering and tuned process of the ML models,the preferable model using the Gaussian process regression algorithm is established,fed with eight input parameters,namely thicknesses of the substrates,line forces and bending moments of the adhesive bonded joints obtained from FEA.The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation.It demonstrates that for life prediction of adhesive joints,the data-driven solutions can constitute an improvement over conventional solutions.
基金support provided by the Jiangsu Industrial Technology Research Institute and the Yangtze Delta Region Institute of Advanced Materialssupported by the National Natural Science Foundation of China(Grant No.52205377)+1 种基金the National Key Research and Development Program(Grant No.2022YFB4601804)the Key Basic Research Project of Suzhou(Grant Nos.#SJC2022029,#SJC2022031).
文摘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(R2)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.
基金supported by the National Natural Science Foundation of China(Grant No.52205377)the Key Basic Research Project of Suzhou(Grant Nos.SJC2022029,SJC2022031)the National Key Research and Development Program(Grant No.2022YFB4601804).
文摘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.
