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Title: Dimensionless numbers in additive manufacturing

Authors:
 [1];  [1]; ORCiD logo [1];  [1]
  1. Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1361777
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Applied Physics
Additional Journal Information:
Journal Volume: 121; Journal Issue: 6; Related Information: CHORUS Timestamp: 2018-02-14 20:17:04; Journal ID: ISSN 0021-8979
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English

Citation Formats

Mukherjee, T., Manvatkar, V., De, A., and DebRoy, T.. Dimensionless numbers in additive manufacturing. United States: N. p., 2017. Web. doi:10.1063/1.4976006.
Mukherjee, T., Manvatkar, V., De, A., & DebRoy, T.. Dimensionless numbers in additive manufacturing. United States. doi:10.1063/1.4976006.
Mukherjee, T., Manvatkar, V., De, A., and DebRoy, T.. Tue . "Dimensionless numbers in additive manufacturing". United States. doi:10.1063/1.4976006.
@article{osti_1361777,
title = {Dimensionless numbers in additive manufacturing},
author = {Mukherjee, T. and Manvatkar, V. and De, A. and DebRoy, T.},
abstractNote = {},
doi = {10.1063/1.4976006},
journal = {Journal of Applied Physics},
number = 6,
volume = 121,
place = {United States},
year = {Tue Feb 14 00:00:00 EST 2017},
month = {Tue Feb 14 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1063/1.4976006

Citation Metrics:
Cited by: 5works
Citation information provided by
Web of Science

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  • The paper presents a study aimed at extending the neural network mapping ability. In traditional modeling, operational process parameters (gas/material temperature, air velocity, etc.) are the inputs and outputs to and from the network. In this approach dimensionless numbers (Re, Ar, H/d) were used as inputs to predict the heat transfer coefficient in a fluidized bed drying process. To produce the data set necessary to train the networks, drying trials of different materials in a fluidized bed were carried out. A series of simulations were performed and several neural networks structures were tested to find an optimal topology of themore » network. Training data set contained information only about two materials. The networks were tested using data obtained for the third product. Performance of the network was satisfactory, however further improvement of mapping ability may be expected after filtration of the testing data.« less
  • Additive manufacturing (AM) holds great potentials in enabling superior engineering functionality, streamlining supply chains, and reducing life cycle impacts compared to conventional manufacturing (CM). This study estimates the net changes in supply-chain lead time, life cycle primary energy consumption, greenhouse gas (GHG) emissions, and life cycle costs (LCC) associated with AM technologies for the case of injection molding, to shed light on the environmental and economic advantages of a shift from international or onshore CM to AM in the United States. A systems modeling framework is developed, with integrations of lead-time analysis, life cycle inventory analysis, LCC model, and scenariosmore » considering design differences, supply-chain options, productions, maintenance, and AM technological developments. AM yields a reduction potential of 3% to 5% primary energy, 4% to 7% GHG emissions, 12% to 60% lead time, and 15% to 35% cost over 1 million cycles of the injection molding production depending on the AM technology advancement in future. The economic advantages indicate the significant role of AM technology in raising global manufacturing competitiveness of local producers, while the relatively small environmental benefits highlight the necessity of considering trade-offs and balance techniques between environmental and economic performances when AM is adopted in the tooling industry. The results also help pinpoint the technological innovations in AM that could lead to broader benefits in future.« less
    Cited by 2
  • Additive manufacturing (AM) holds great potentials in enabling superior engineering functionality, streamlining supply chains, and reducing life cycle impacts compared to conventional manufacturing (CM). This study estimates the net changes in supply-chain lead time, life cycle primary energy consumption, greenhouse gas (GHG) emissions, and life cycle costs (LCC) associated with AM technologies for the case of injection molding, to shed light on the environmental and economic advantages of a shift from international or onshore CM to AM in the United States. A systems modeling framework is developed, with integrations of lead-time analysis, life cycle inventory analysis, LCC model, and scenariosmore » considering design differences, supply-chain options, productions, maintenance, and AM technological developments. AM yields a reduction potential of 3% to 5% primary energy, 4% to 7% GHG emissions, 12% to 60% lead time, and 15% to 35% cost over 1 million cycles of the injection molding production depending on the AM technology advancement in future. The economic advantages indicate the significant role of AM technology in raising global manufacturing competitiveness of local producers, while the relatively small environmental benefits highlight the necessity of considering trade-offs and balance techniques between environmental and economic performances when AM is adopted in the tooling industry. The results also help pinpoint the technological innovations in AM that could lead to broader benefits in future.« less