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Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition
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Toward a Digital Thread and Data Package for Metals-Additive Manufacturing.

D B Kim1,2, P Witherell3, Y Lu4

  • 1Manufacturing and Engineering Technology, College of Engineering, Tennessee Technological Univ., 920 N. Peachtree Avenue, Lewis Hall 111A, Cookeville, TN 38505.

Smart and Sustainable Manufacturing Systems
|July 11, 2017
PubMed
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Additive manufacturing (AM) faces challenges in data management and traceability for full production readiness. This study proposes data models and an AM information map to improve part producibility and process repeatability.

Area of Science:

  • Engineering and Materials Science
  • Manufacturing Technology
  • Data Science and Informatics

Background:

  • Additive Manufacturing (AM) promises industrial revolution benefits like customization and efficiency.
  • Current AM adoption is limited by challenges in data manageability, traceability, and accountability.
  • Lack of robust data infrastructure hinders AM's transition to a full production-ready technology.

Purpose of the Study:

  • To address critical challenges in Additive Manufacturing data management and traceability.
  • To develop data models supporting validation and conformance methodologies in AM.
  • To enhance part producibility, process repeatability, and part-to-part reproducibility in AM.

Main Methods:

  • Development of data models for AM information management.
Keywords:
additive manufacturingconformancedigital threadrepeatabilityreproducibilityvalidation

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  • Creation of an AM information map leveraging informatics.
  • Application of three case studies to demonstrate data structures and digital thread implementation.

    • Main Results:

    • Proposed data models facilitate the development of validation and conformance methodologies.
    • The AM information map enhances part producibility and process repeatability.
    • Case studies highlight the importance of baseline data structures and part provenance via a digital thread.

    Conclusions:

    • Data models and information maps are crucial for overcoming AM production challenges.
    • Establishing a digital thread with baseline data structures ensures part provenance and reproducibility.
    • This work contributes to making Additive Manufacturing a fully production-ready technology.