Machine learning(ML)methods have been extensively applied to optimize additive manufacturing(AM)process parameters.However,existing studies predominantly focus on the relationship between processing parameters and pro...Machine learning(ML)methods have been extensively applied to optimize additive manufacturing(AM)process parameters.However,existing studies predominantly focus on the relationship between processing parameters and properties for specifc alloys,thus limiting their applicability to a broader range of materials.To address this issue,dimensionless parameters,which can be easily calculated from simple analytical expressions,were used as inputs to construct an ML model for classifying the relative density in laser-powder bed fusion.The model was trained using data from four widely used alloys collected from literature.The accuracy and generalizability of the trained model were validated using two laser-powder bed fusion(L-PBF)high-entropy alloys that were not included in the training process.The results demonstrate that the accuracy scores for both cases exceed 0.8.Moreover,the simple dimensionless inputs in the present model can be calculated conveniently without numerical simulations,thereby facilitating the recommendation of process parameters.展开更多
A new dimensionless number is proposed for dynamic plastic deformation analysis of clamped circular plates under underwater explosion loads by introducing dimensional analysis method to the basic dynamical governing e...A new dimensionless number is proposed for dynamic plastic deformation analysis of clamped circular plates under underwater explosion loads by introducing dimensional analysis method to the basic dynamical governing equations of circular plates.The relation between dimensionless final plastic deformation of circular plates and the new dimensionless number is established based on massive underwater explosion test data.Meanwhile,comparative analysis was discussed with two other published dimensionless parameters which indicated the new dimensionless number proposed in this paper is more effective and extensive to predict the dynamic plastic response of circular plates under underwater explosion condition.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
耐震时程分析(endurance time analysis,ETA)法作为结构响应分析领域的有效简化方法,基于频域地震动反应谱合成的耐震时程曲线无法准确反映时域的脉冲特性,限制了其在近断层脉冲型地震中的应用。为将ETA法应用到近断层脉冲型地震动作用...耐震时程分析(endurance time analysis,ETA)法作为结构响应分析领域的有效简化方法,基于频域地震动反应谱合成的耐震时程曲线无法准确反映时域的脉冲特性,限制了其在近断层脉冲型地震中的应用。为将ETA法应用到近断层脉冲型地震动作用下斜拉桥动力响应分析中,基于增量动力分析(incremental dynamic analysis,IDA)法研究了不同峰值下脉冲、高频分量对斜拉桥动力响应的贡献程度,构建了考虑脉冲和强度特性的斜拉桥动力响应预测模型,利用ETA法模拟高频分量下的斜拉桥动力响应并结合预测模型,预测了近断层脉冲型地震动下斜拉桥的动力响应。结果表明:建立的预测模型可以精确表达不同强度下高频分量与原始地震动响应之间的定量关系;基于ETA模型和IDA法计算出0.6 g下的平均动力响应最大相对误差不超过10%,具有良好的预测精度。研究成果为高效合理地计算近断层脉冲型地震动下斜拉桥的动力响应提供了技术支撑。展开更多
基金supported by the National Key R&D Program of China(Grant No.2023YFB4606502)the National Natural Science Foundation of China(Grant No.52471017)the Research Fund of the State Key Laboratory of Solidification Processing(NPU),China(Grant No.2020-TS-06).
文摘Machine learning(ML)methods have been extensively applied to optimize additive manufacturing(AM)process parameters.However,existing studies predominantly focus on the relationship between processing parameters and properties for specifc alloys,thus limiting their applicability to a broader range of materials.To address this issue,dimensionless parameters,which can be easily calculated from simple analytical expressions,were used as inputs to construct an ML model for classifying the relative density in laser-powder bed fusion.The model was trained using data from four widely used alloys collected from literature.The accuracy and generalizability of the trained model were validated using two laser-powder bed fusion(L-PBF)high-entropy alloys that were not included in the training process.The results demonstrate that the accuracy scores for both cases exceed 0.8.Moreover,the simple dimensionless inputs in the present model can be calculated conveniently without numerical simulations,thereby facilitating the recommendation of process parameters.
基金supported by the National Natural Science Foundation of China(12402444)。
文摘A new dimensionless number is proposed for dynamic plastic deformation analysis of clamped circular plates under underwater explosion loads by introducing dimensional analysis method to the basic dynamical governing equations of circular plates.The relation between dimensionless final plastic deformation of circular plates and the new dimensionless number is established based on massive underwater explosion test data.Meanwhile,comparative analysis was discussed with two other published dimensionless parameters which indicated the new dimensionless number proposed in this paper is more effective and extensive to predict the dynamic plastic response of circular plates under underwater explosion condition.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
