Browsing by Subject "Multiscale modeling"

Now showing 1 - 3 of 3
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    A framework for graph-base neural network using numerical simulation of metal powder bed fusion for correlating process parameters and defect generation
    (Elsevier, 2022) Akter Jahan, Suchana; Al Hasan, Mohammad; El-Mounayri, Hazim; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Powder bed fusion (PBF) is the most common technique used for metal additive manufacturing. This process involves consolidation of metal powder using a heat source such as laser or electron beam. During the formation of three-dimensional(3D) objects by sintering metal powders layer by layer, many different thermal phenomena occur that can create defects or anomalies on the final printed part. Similar to other additive manufacturing techniques, PBF has been in practice for decades, yet it is still going through research and development endeavors which is required to understand the physics behind this process. Defects and deformations highly impact the product quality and reliability of the overall manufacturing process; hence, it is essential that we understand the reason and mechanism of defect generation in PBF process and take appropriate measures to rectify them. In this paper, we have attempted to study the effect of processing parameters (scanning speed, laser power) on the generation of defects in PBF process using a graph-based artificial neural network that uses numerical simulation results as input or training data. Use of graph-based machine learning is novel in the area of manufacturing let alone additive manufacturing or powder bed fusion. The outcome of this study provides an opportunity to design a feedback controlled in-situ online monitoring system in powder bed fusion to reduce printing defects and optimize the manufacturing process.
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    Concurrent topology optimization of structures and materials
    (2013-12-11) Liu, Kai; Tovar, Andrés; Nematollahi, Khosrow; Koskie, Sarah; Anwar, Sohel
    Topology optimization allows designers to obtain lightweight structures considering the binary distribution of a solid material. The introduction of cellular material models in topology optimization allows designers to achieve significant weight reductions in structural applications. However, the traditional topology optimization method is challenged by the use of cellular materials. Furthermore, increased material savings and performance can be achieved if the material and the structure topologies are concurrently designed. Hence, multi-scale topology optimization methodologies are introduced to fulfill this goal. The objective of this investigation is to discuss and compare the design methodologies to obtaining optimal macro-scale structures and the corresponding optimal meso-scale material designs in continuum design domains. These approaches make use of homogenization theory to establish communication bridges between both material and structural scales. The periodicity constraint makes such cellular materials manufacturable while relaxing the periodicity constraint to achieve major improvements of structural performance. Penalization methods are used to obtain binary solutions in both scales. The proposed methodologies are demonstrated in the design of stiff structure and compliant mechanism synthesis. The multiscale results are compared with the traditional structural-level designs in the context of Pareto solutions, demonstrating benefits of ultra-lightweight configurations. Errors involved in the mult-scale topology optimization procedure are also discussed. Errors are mainly classified as mesh refinement errors and homogenization errors. Comparisons between the multi-level designs and uni-level designs of solid structures, structures using periodic cellular materials and non-periodic cellular materials are provided. Error quantifications also indicate the superiority of using non-periodic cellular materials rather than periodic cellular materials.
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    Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data
    (Elsevier, 2022) Chen, Minghan; Xu, Chunrui; Xu, Ziang; He, Wei; Zhang, Haorui; Su, Jing; Song, Qianqian; Biostatistics, School of Public Health
    Lung cancer is one of the leading causes of cancer-related death, with a five-year survival rate of 18%. It is a priority for us to understand the underlying mechanisms affecting lung cancer therapeutics’ implementation and effectiveness. In this study, we combine the power of Bioinformatics and Systems Biology to comprehensively uncover functional and signaling pathways of drug treatment using bioinformatics inference and multiscale modeling of both scRNA-seq data and proteomics data. Based on a time series of lung adenocarcinoma derived A549 cells after DEX treatment, we first identified the differentially expressed genes (DEGs) in those lung cancer cells. Through the interrogation of regulatory network of those DEGs, we identified key hub genes including TGFβ, MYC, and SMAD3 varied underlie DEX treatment. Further gene set enrichment analysis revealed the TGFβ signaling pathway as the top enriched term. Those genes involved in the TGFβ pathway and their crosstalk with the ERBB pathway presented a strong survival prognosis in clinical lung cancer samples. With the basis of biological validation and literature-based curation, a multiscale model of tumor regulation centered on both TGFβ-induced and ERBB-amplified signaling pathways was developed to characterize the dynamic effects of DEX therapy on lung cancer cells. Our simulation results were well matched to available data of SMAD2, FOXO3, TGFβ1, and TGFβR1 over the time course. Moreover, we provided predictions of different doses to illustrate the trend and therapeutic potential of DEX treatment. The innovative and cross-disciplinary approach can be further applied to other computational studies in tumorigenesis and oncotherapy. We released the approach as a user-friendly tool named BIMM (Bioinformatic Inference and Multiscale Modeling), with all the key features available at https://github.com/chenm19/BIMM.