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- 저자 : Sharp Michael (검색결과 2 건)
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Neural network-based build time estimation for additive manufacturing: a performance comparison
Oh Yosep,Sharp Michael,Sprock Timothy,권순조 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.5
Additive manufacturing (AM) has brought positive opportunities with phenomenal changes to traditional manufacturing. Consistent efforts and novel studies into AM use have resolved critical issues in manufacturing and broadened technical boundaries. Build time estimation is one of the critical issues in AM that still needs attention. Accurate build time estimation is key for feasibility studies, preliminary design, and process/production planning. Recent studies have provided the possibility of neural network (NN)-based build time estimation. In particular, traditional artificial NN (ANN)- and convolutional NN (CNN)-based methods have been demonstrated. However, very little has been done on the performance comparison for build time estimation among the different types of NNs. This study is aimed at filling this gap by designing various NNs for build time estimation and comparing them. Two types of features are prepared as inputs for the NNs by processing three-dimensional (3D) models: (1) representative features (RFs) including dimensions, part volume, and support volume; and (2) the set of voxels generated from designating the cells occupied by the workpiece in a mesh grid. With the combination of NN types and input feature types, we design three NNs: (1) ANN with RFs; (2) ANN with voxels; and (3) CNN with voxels. To obtain large enough label data for reliable training, we consider simulation build time from commercial slicing applications rather than actual build time. The simulation build time is calculated based on a material extrusion process. To address various cases for input models, two design factors (scale and rotation) are considered by controlling the size and build orientation of 3D models. In computational experiments, we reveal that the CNN-based estimation is often more accurate than others. Furthermore, the design factors affect the performance of build time estimation. In particular, the CNN-based estimation is strongly influenced by changing the size of 3D models.
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TAZ, a Transcriptional Modulator of Mesenchymal Stem Cell Differentiation
Hong, Jeong-Ho,Hwang, Eun-Sook,McManus, Michael T.,Amsterdam, Adam,Tian, Yu,Kalmukova, Ralitsa,Mueller, Elisabetta,Benjamin, Thomas,Spiegelman, Bruce M.,Sharp, Phillip A.,Hopkins, Nancy,Yaffe, Michael 이화여자대학교 약학연구소 2005 藥學硏究論文集 Vol.- No.16
Mesenchymal stem celts (MSCs) are a pluripotent cell type that can differentiate into several distinct lineages. Two key transcription factors, Runx2 and peroxisome protiferator-activated receptor γ(PPARγ), drive MSCs to differentiate into either osteoblasts or adipocytes, respectively. How these two transcription factors are regulated in order to specify these alternate cell fates remains a pivotal question. Here we report that a 14-3-3-binding protein, TAZ(transcrip-tional coactivator with PDZ-binding motif), coactivates RunxB-dependent gene transcription while repressing PPARγ-dependent gene transcription. By modulating TAZ expression in model cell lines, mouse embryonic fibroblasts, and primary MSCs in culture and in zebrafish in vivo, we observed alterations in osteogenic versus adipogenic potential. These results indicate that TAZ functions as a molecular rheostat that modulates MSC differentiation.
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