Journal article Open Access

An End-to-End Framework for the Additive Manufacture of Optimized Tubular Structures

JUN YE; PINELOPI KYVELOU; FILIPPO GILARDI; HONGJIA LU; MATTHEW GILBERT; LEROY GARDNER


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    "doi": "10.1109/ACCESS.2021.3132797", 
    "description": "<p>Although additive manufacturing (AM) has been maturing for some years, it has only recently started to capture the interest of the cost-sensitive construction industry. The research presented herein is seeking to integrate AM into the construction sector through the establishment of an automated end-to-end framework for the generation of high-performance AM structures, combining sophisticated optimization techniques with cutting edge AM methods. Trusses of tubular cross-section subjected to different load cases have been selected as the demonstrators of the proposed framework. Optimization studies, featuring numerical layout and geometry optimization techniques, are employed to obtain the topology of the examined structures, accounting for practical and manufacturing constraints. Cross-section optimization is subsequently undertaken, followed by a series of geometric operations for the design of free-form joints connecting the optimized members. Solid models of the optimized designs are then exported for wire arc additive manufacturing (WAAM). Following determination of the optimal printing sequence, the trusses are printed and inspected. The efficiency of the optimized designs has been assessed by means of finite element modelling and compared against equivalent conventional designs. Design efficiency (reflected in the capacity-to-mass ratios) was at least doubled for all optimized trusses (when compared to their equivalent reference designs), demonstrating the effectiveness of the proposed optimization framework.</p>", 
    "language": "eng", 
    "title": "An End-to-End Framework for the Additive Manufacture of Optimized Tubular Structures", 
    "license": {
      "id": "CC-BY-4.0"
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    "journal": {
      "volume": "vol. 9", 
      "issue": "SPECIAL SECTION ON METAL ADDITIVE MANUFACTURING", 
      "pages": "pp. 165476-165489", 
      "title": "IEEE Access"
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        "title": "Intelligent data-driven pipeline for the manufacturing of certified metal parts through Direct Energy Deposition processes", 
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        "program": "H2020", 
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    "keywords": [
      "Additive manufacturing", 
      "Optimized trusses", 
      "End-to-end framework", 
      "Free-form joints", 
      "Geometry optimization", 
      "Layout optimization;"
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    "publication_date": "2021-12-03", 
    "creators": [
      {
        "affiliation": "College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China", 
        "name": "JUN YE"
      }, 
      {
        "orcid": "0000-0003-1798-6817", 
        "affiliation": "Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, U.K", 
        "name": "PINELOPI KYVELOU"
      }, 
      {
        "affiliation": "MX3D, 1014 BK Amsterdam, The Netherlands", 
        "name": "FILIPPO GILARDI"
      }, 
      {
        "affiliation": "Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.", 
        "name": "HONGJIA LU"
      }, 
      {
        "affiliation": "Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.", 
        "name": "MATTHEW GILBERT"
      }, 
      {
        "affiliation": "Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, U.K", 
        "name": "LEROY GARDNER"
      }
    ], 
    "notes": "This work was supported by the European Union's Horizon 2020 Research and Innovation Programme ''Intelligent data-driven pipeline for the manufacturing of certified metal parts through Direct Energy Deposition process (INTEGRADDE)'' under Grant 820776.", 
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