TY - GEN
T1 - Metamodels for Rapid Analysis of Large Sets of Building Designs for Robotic Constructability
T2 - 18th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments: Space Exploration, Utilization, Engineering, and Construction in Extreme Environments, Earth and Space 2022
AU - Muthumanickam, Naveen Kumar
AU - Duarte, José Pinto
AU - Nazarian, Shadi
AU - Bilén, Sven G.
N1 - Funding Information:
The work has been funded by the NASA BAA Next Space Technologies for Exploration Partnerships -2 (NextSTEP-2), In-Situ Resource Utilization (ISRU) Technology. The authors would like to thank the following individuals for their input and help: Hari Nayar, Julie Kleinhenz, Diane Linne, Jerry Sanders, and Brian Wilcox.
Funding Information:
The study on which this paper is based was supported by NASA MUREP Institutional Research Opportunity Grant under Cooperative Agreement #80NSSC19M0196. The results and opinions expressed in this paper do not necessarily reflect the views and policies of the National Aeronautics and Space Administration.
Funding Information:
A Lunar Space Technology Research (LuSTR) grant awarded to Michigan Technological University (MTU) has prompted the development of a Percussive Heated Cone Penetrometer (PHCP). This technology will allow for the active sampling of geotechnical data and thermal calorimetric measurements of lunar regolith in PSRs. This paper will focus on the sig nificance of the thermal measurements collected by the PHCP. Through the addition of heat and the active sampling of the temperatures surrounding the heated probe, temperature profiles will indicate phase changes for many volatiles present. This data will then be used in a predictive mathematical model to derive the volatile contents surrounding the PHCP.
Funding Information:
This project was undertaken with the financial support of the Canadian Space Agency, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Fonds de Recherche du Québec - Nature et Technologies (FRQNT). The authors would also like to thank Dominique Tremblay and Pierre-Lucas Aubin-Fournier for assistance with designing and building the experimental apparatus, and George Butt for assistance with performing the experiments.
Funding Information:
The author would like to thank the Norwich University Faculty Development Funding for the Charles A. Dana Research Fellowship AY19 -20 and the resourceful contribution of the Kreitzberg Library.
Funding Information:
This work is supported by NASA Small Business Technology Transfer (STTR) program 2021 (award number T7.04-2630 (STTR 2021-1)). We would like to thank our technical omtor Benveirly Kemmerer for her smooth management of this waard.
Funding Information:
The authors would like to express our gratitude to Dr. Mike Pereira, Mr. Duane Revilock, and Mr. Charles Ruggeri, Aerospace Engineers at NASA Glenn Research Center, for sharing their insight and expertise along with the test data, photos and videos generated during the tests. Their support greatly assisted with this research. The authors are also grateful to Mr. William Emmerling (retired) at FAA William J. Hughes Technical Center for his constant support and advice on this research, and Dr. Gilbert Queitzsch (retired) at FAA for his helpful discussion and feedback for this research. This research was conducted under FAA cooperative agreement 692M151840003 and sponsored by the Aircraft Catastrophic Failure Prevention Program (ACFPP).
Funding Information:
This work is funded by NASA SSERVI under the RESOURCE (Resource Exploration and Science of OUR Cosmic Environment) contract.
Funding Information:
This work was funded by the Laboratory Directed Research & Development (LDRD) program at Sandia National Laboratories, and the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number DE-SC-0000230927 and under the Collaboratory on Mathematics and Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PhILMs) project. The development of the ideas presented herein was funded in part by the third author’s Presidential Early Career Award for Scientists and Engineers (PECASE). Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not
Funding Information:
This work was partially funded by National Key R&D Program of China with grant No. 2017YFC1503106 and Science & Tec hnology Project of China Energy Engineering Group Planning and Engineering Co., Ltd with grant No. GSKJ2-T02-2020.
Funding Information:
The authors would like to thank the Korean Technology for financial support of this research.
Funding Information:
This work is supported by Louisiana Space Grant Consortium (LaSPACE), and Bert S. Turner Department of Construction Management at LSU. The authors would like to also thank Dr. Jennifer Edmunson (NASA Marshall Space Flight Center) for her valuable support.
Funding Information:
This work was supported by the Sandia National Laboratories (SNL) Laboratory-directed Research and Development (LDRD) program, and the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number DE-SC-0000230927 and under the Col-laboratory on Mathematics and Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PhILMs) project. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
Funding Information:
This work is funded by the Technology Mission Directorate.
Funding Information:
The authors acknowledge the financial support received from the Quake Core, NZ, and University of Canterbury Doctoral Scholarship along with the technical support during the planning and testing phase of the research provided by Prof. Timothy Sullivan, Mr. Tim Perigo, Mr. Mosese Fifita, Mr. Alan Thirlwell, and the Structural Engineering Laboratory team.
Funding Information:
This work is supported by a Lunar Surface Technology Research (LuSTR) grant from 1$6$¶V 6SDFH 7HFKQRORJ\ 5HVHDUFK *UDQWV 3URJUDP 166& .
Funding Information:
We wish to express our sincere appreciation to Mr. Matt Duggan and Dr. Valery Aksamentov, project leads for Boeing for their effective central roles in initiating and supporting these efforts, and The Boeing Company for financial support of the study. We also owe a debt of gratitude to our own SICSA graduates who contributed to this project: Osaid Sasi and Albert Rajkumar.
Funding Information:
*Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.
Publisher Copyright:
© ASCE.
PY - 2023
Y1 - 2023
N2 - Disruptive robotic construction technologies such as additive deposition of cementitious materials like concrete (or “3D concrete printing”) require the synchronous operation of multiple pieces of equipment in the production setup. In such an environment, it is crucial to simulate the robotic motions (for toolpath clashes) and the cementitious material behavior (for toolpath failures) to ensure fail-proof constructability of the envisioned building geometry. However, toolpath clash detection requires 4D simulations of the production setup, which are computationally graphics intensive, whereas toolpath failure detection requires actual 3D printing of test parts from the geometry to identify areas prone to failure while 3D printing, which is physically tedious. Both these processes, being computationally and physically intensive, have largely curtailed designers from simulating and exploring large sets of design options with varying geometries and toolpath configurations. To overcome this and allow designers to explore large sets of design possibilities, this paper proposes two novel computational metamodels capable of performing robotic toolpath clash detection and failure detection with significantly reduced times than the earlier approaches. The developed metamodels were used to rapidly simulate large sets of building design options for robotic constructability in the NASA 3D-Printed Mars Habitat Challenge.
AB - Disruptive robotic construction technologies such as additive deposition of cementitious materials like concrete (or “3D concrete printing”) require the synchronous operation of multiple pieces of equipment in the production setup. In such an environment, it is crucial to simulate the robotic motions (for toolpath clashes) and the cementitious material behavior (for toolpath failures) to ensure fail-proof constructability of the envisioned building geometry. However, toolpath clash detection requires 4D simulations of the production setup, which are computationally graphics intensive, whereas toolpath failure detection requires actual 3D printing of test parts from the geometry to identify areas prone to failure while 3D printing, which is physically tedious. Both these processes, being computationally and physically intensive, have largely curtailed designers from simulating and exploring large sets of design options with varying geometries and toolpath configurations. To overcome this and allow designers to explore large sets of design possibilities, this paper proposes two novel computational metamodels capable of performing robotic toolpath clash detection and failure detection with significantly reduced times than the earlier approaches. The developed metamodels were used to rapidly simulate large sets of building design options for robotic constructability in the NASA 3D-Printed Mars Habitat Challenge.
UR - http://www.scopus.com/inward/record.url?scp=85146568069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146568069&partnerID=8YFLogxK
U2 - 10.1061/9780784484470.073
DO - 10.1061/9780784484470.073
M3 - Conference contribution
AN - SCOPUS:85146568069
T3 - Earth and Space 2022: Space Exploration, Utilization, Engineering, and Construction in Extreme Environments - Selected Papers from the 18th Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments
SP - 871
EP - 884
BT - Earth and Space 2022
A2 - Dreyer, Christopher B.
A2 - Littell, Justin
PB - American Society of Civil Engineers (ASCE)
Y2 - 25 April 2022 through 28 April 2022
ER -