Framing Artificial Intelligence (AI) Additive Manufacturing (AM)

被引:13
|
作者
Heiden, Bernhard [1 ,4 ]
Alieksieiev, Volodymyr [2 ]
Volk, Matthias [3 ]
Tonino-Heiden, Bianca [4 ]
机构
[1] Carinthia Univ Appl Sci, Ind Engn & Management Studiengang, Europastr 4, A-9524 Villach, Austria
[2] Natl Tech Univ, Inst Mech Engn & Transport, Kharkiv Polytech Inst, Kyrpychova 2, UA-61002 Kharkiv, Ukraine
[3] Otto von Guericke Univ, Dept Tech & Business Informat Syst ITI, Univ Pl 2, D-39106 Magdeburg, Germany
[4] Karl Franzens Univ Graz, Philosophy Inst, Heinrichstr 26-V, A-8010 Graz, Austria
关键词
Artificial Intelligence; AI; Additive Manufacturing; AM; Production; Production Technology; Production Control; Production Engineering; System Theory; Graph Theory; BIG DATA;
D O I
10.1016/j.procs.2021.04.161
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Nowadays AM is a rapidly growing and emerging discipline in manufacturing, as well as AI is in informational applications. Both are related to logistical and self-referential/-copying concepts which make them scalable. What is in AM osmotic mass production spreading is in AI-related Cyber-Physical Systems (CPS) the osmotic computational approach. AI-AM self-propagatedly framed is itself an emerging field, which can be logically or systematically unified. The paper investigates firstly recent developments in the field of the AM process flow and how it is related to AI applications. The result is a list of logistical, organisational, and industrial process steps as well as modern and future AI-AM applications. The extended approach then gives prospect to a meta-perspectively embedded osmotic decentralized computing, as well as an osmotic manufacturing paradigm, which utilizes glocal functions, concerning local production as well as global distributed material and information transport nets and their connection graphs. (C) 2021 The Authors. Published by Elsevier B.V.
引用
收藏
页码:387 / 394
页数:8
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