Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to...Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to miniaturization of electronic components,it is challenging to directly measure or numerically predict the mechanical response of small-sized critical interconnections in board-level packaging structures to ensure the mechanical reliability of electronic devices in projectiles under harsh working conditions.To address this issue,an indirect measurement method using the Bayesian regularization-based load identification was proposed in this study based on finite element(FE)pre-dictions to estimate the load applied on critical interconnections of board-level packaging structures during the process of projectile penetration.For predicting the high-strain-rate penetration process,an FE model was established with elasto-plastic constitutive models of the representative packaging ma-terials(that is,solder material and epoxy molding compound)in which material constitutive parameters were calibrated against the experimental results by using the split-Hopkinson pressure bar.As the impact-induced dynamic bending of the printed circuit board resulted in an alternating tensile-compressive loading on the solder joints during penetration,the corner solder joints in the edge re-gions experience the highest S11 and strain,making them more prone to failure.Based on FE predictions at different structural scales,an improved Bayesian method based on augmented Tikhonov regulariza-tion was theoretically proposed to address the issues of ill-posed matrix inversion and noise sensitivity in the load identification at the critical solder joints.By incorporating a wavelet thresholding technique,the method resolves the problem of poor load identification accuracy at high noise levels.The proposed method achieves satisfactorily small relative errors and high correlation coefficients in identifying the mechanical response of local interconnections in board-level packaging structures,while significantly balancing the smoothness of response curves with the accuracy of peak identification.At medium and low noise levels,the relative error is less than 6%,while it is less than 10%at high noise levels.The proposed method provides an effective indirect approach for the boundary conditions of localized solder joints during the projectile penetration process,and its philosophy can be readily extended to other scenarios of multiscale analysis for highly nonlinear materials and structures under extreme loading conditions.展开更多
Measuring the internal stress of Al alloy forgings accurately is critical for controlling the deformation during the subsequent machine process.In this work,the crack compliance method was used to calculate the intern...Measuring the internal stress of Al alloy forgings accurately is critical for controlling the deformation during the subsequent machine process.In this work,the crack compliance method was used to calculate the internal residual stress of Al-Cu high strength alloys,and the effect of various model parameters of crack compliance method on the calculated precision was studied by combining the numerical simulation and experimental method.The results show that the precision first increased and then decreased with increasing the crack range.The decreased precision when using a high crack range was due to the strain fluctuation during the machining process,and the optimized crack range was 71%of the thickness of forgings.Low orders of Legendre polynomial can result in residual stress curve more smooth,while high orders led to the occurrence of distortion.The Tikhonov regularization method effectively suppressed the distortion of residual stress caused by the fluctuation of strain data,which significantly improved the precision.In addition,The crack compliance method with optimized parameters was used to measure the residual stress of Al-Cu alloy with different quenching methods.The calculated results demonstrated that the distribution of residual stress was obtained accurately.展开更多
Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is...Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52475166,52175148)the Regional Collaboration Project of Shanxi Province(Grant No.202204041101044).
文摘Modern warfare demands weapons capable of penetrating substantial structures,which presents sig-nificant challenges to the reliability of the electronic devices that are crucial to the weapon's perfor-mance.Due to miniaturization of electronic components,it is challenging to directly measure or numerically predict the mechanical response of small-sized critical interconnections in board-level packaging structures to ensure the mechanical reliability of electronic devices in projectiles under harsh working conditions.To address this issue,an indirect measurement method using the Bayesian regularization-based load identification was proposed in this study based on finite element(FE)pre-dictions to estimate the load applied on critical interconnections of board-level packaging structures during the process of projectile penetration.For predicting the high-strain-rate penetration process,an FE model was established with elasto-plastic constitutive models of the representative packaging ma-terials(that is,solder material and epoxy molding compound)in which material constitutive parameters were calibrated against the experimental results by using the split-Hopkinson pressure bar.As the impact-induced dynamic bending of the printed circuit board resulted in an alternating tensile-compressive loading on the solder joints during penetration,the corner solder joints in the edge re-gions experience the highest S11 and strain,making them more prone to failure.Based on FE predictions at different structural scales,an improved Bayesian method based on augmented Tikhonov regulariza-tion was theoretically proposed to address the issues of ill-posed matrix inversion and noise sensitivity in the load identification at the critical solder joints.By incorporating a wavelet thresholding technique,the method resolves the problem of poor load identification accuracy at high noise levels.The proposed method achieves satisfactorily small relative errors and high correlation coefficients in identifying the mechanical response of local interconnections in board-level packaging structures,while significantly balancing the smoothness of response curves with the accuracy of peak identification.At medium and low noise levels,the relative error is less than 6%,while it is less than 10%at high noise levels.The proposed method provides an effective indirect approach for the boundary conditions of localized solder joints during the projectile penetration process,and its philosophy can be readily extended to other scenarios of multiscale analysis for highly nonlinear materials and structures under extreme loading conditions.
基金Project(51875583)supported by the National Natural Science Foundation of ChinaProject(zzyjkt2018-03)supported by the State Key Laboratory of High Performance Complex Manufacturing,China。
文摘Measuring the internal stress of Al alloy forgings accurately is critical for controlling the deformation during the subsequent machine process.In this work,the crack compliance method was used to calculate the internal residual stress of Al-Cu high strength alloys,and the effect of various model parameters of crack compliance method on the calculated precision was studied by combining the numerical simulation and experimental method.The results show that the precision first increased and then decreased with increasing the crack range.The decreased precision when using a high crack range was due to the strain fluctuation during the machining process,and the optimized crack range was 71%of the thickness of forgings.Low orders of Legendre polynomial can result in residual stress curve more smooth,while high orders led to the occurrence of distortion.The Tikhonov regularization method effectively suppressed the distortion of residual stress caused by the fluctuation of strain data,which significantly improved the precision.In addition,The crack compliance method with optimized parameters was used to measure the residual stress of Al-Cu alloy with different quenching methods.The calculated results demonstrated that the distribution of residual stress was obtained accurately.
基金supported by the National Natural Science Fundation of China (60736021)the Joint Funds of NSFC-Guangdong Province(U0735003)
文摘Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.
