Customer requirements analysis is the key step for product variety design of mass customiza-tion(MC). Quality function deployment (QFD) is a widely used management technique for understanding the voice of the customer...Customer requirements analysis is the key step for product variety design of mass customiza-tion(MC). Quality function deployment (QFD) is a widely used management technique for understanding the voice of the customer (VOC), however, QFD depends heavily on human subject judgment during extracting customer requirements and determination of the importance weights of customer requirements. QFD pro-cess and related problems are so complicated that it is not easily used. In this paper, based on a general data structure of product family, generic bill of material (GBOM), association rules analysis was introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, and decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin was used to illustrate the proposed method.展开更多
Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable microstructures.We propose a multiscale datadriven approach with a hierarchical learning strategy fo...Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable microstructures.We propose a multiscale datadriven approach with a hierarchical learning strategy for the discovery of a generic physics-constrained anisotropic constitutive model for the heterogeneous hyperelastic materials.Based on the sparse multiscale experimental data,the constitutive artificial neural networks for hyperelastic component phases containing composite interfaces are established by the particle swarm optimization algorithm.A microscopic finite element coupled constitutive artificial neural networks solver is introduced to obtain the homogenized stress-stretch relation of heterogeneous materials with different microstructures.And a dense stress-stretch relation dataset is generated by training a neural network through the FE results.Further,a generic invariant representation of strain energy function(SEF)is proposed with a parameter set being implicitly expressed by artificial neural networks(SANN),which describes the hyperelastic properties of heterogeneous materials with different microstructures.A convexity constraint is imposed on the SEF to ensure that the multiscale constitutive model is physically relevant,and the ℓ_(1) regularization combined with thresholding is introduced to the loss function of SANN to improve the interpretability of this model.Finally,the multiscale model is hierarchically trained,cross-validated and tested using the experimental data of cord-rubber composite materials with different microstructures.The proposed multiscale model provides a convenient and general methodology for constitutive modeling of heterogeneous hyperelastic materials.展开更多
基金the National Natural Science Founda-tion of China (No. 70471022)the NSFC / Hong KongResearch Grant Council (No. 70418013)
文摘Customer requirements analysis is the key step for product variety design of mass customiza-tion(MC). Quality function deployment (QFD) is a widely used management technique for understanding the voice of the customer (VOC), however, QFD depends heavily on human subject judgment during extracting customer requirements and determination of the importance weights of customer requirements. QFD pro-cess and related problems are so complicated that it is not easily used. In this paper, based on a general data structure of product family, generic bill of material (GBOM), association rules analysis was introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, and decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin was used to illustrate the proposed method.
基金supported by the Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0296)Strategic Priority Research Program of the Chinese Academy of Sciences(XDC06030102)+1 种基金National Key R&D Program of China(2020YFA0713603)National Natural Science Foundation of China(12271409).
文摘Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable microstructures.We propose a multiscale datadriven approach with a hierarchical learning strategy for the discovery of a generic physics-constrained anisotropic constitutive model for the heterogeneous hyperelastic materials.Based on the sparse multiscale experimental data,the constitutive artificial neural networks for hyperelastic component phases containing composite interfaces are established by the particle swarm optimization algorithm.A microscopic finite element coupled constitutive artificial neural networks solver is introduced to obtain the homogenized stress-stretch relation of heterogeneous materials with different microstructures.And a dense stress-stretch relation dataset is generated by training a neural network through the FE results.Further,a generic invariant representation of strain energy function(SEF)is proposed with a parameter set being implicitly expressed by artificial neural networks(SANN),which describes the hyperelastic properties of heterogeneous materials with different microstructures.A convexity constraint is imposed on the SEF to ensure that the multiscale constitutive model is physically relevant,and the ℓ_(1) regularization combined with thresholding is introduced to the loss function of SANN to improve the interpretability of this model.Finally,the multiscale model is hierarchically trained,cross-validated and tested using the experimental data of cord-rubber composite materials with different microstructures.The proposed multiscale model provides a convenient and general methodology for constitutive modeling of heterogeneous hyperelastic materials.
