A correlation among industry 4.0, additive manufacturing, and topology optimization: a state-of-the-art review

被引:3
|
作者
Ishfaq, Kashif [1 ]
Khan, Muhammad Dawar Azhar [1 ]
Khan, Muhammad Atyab Azhar [2 ]
Mahmood, Muhammad Arif [3 ]
Maqsood, Muhammad Asad [1 ]
机构
[1] Univ Engn & Technol, Ind & Mfg Engn Dept, Lahore 548900, Pakistan
[2] Natl Univ Sci & Technol, Dept Engn Sci, Islamabad 44000, Pakistan
[3] Missouri Univ Sci & Technol, Intelligent Syst Ctr, Rolla, MO 65409 USA
关键词
Topology optimization; Additive manufacturing; Design for manufacturing and assembly; Industrial revolution 4.0; SELF-SUPPORTING STRUCTURES; OVERHANG CONSTRAINT; GENETIC ALGORITHM; PART ORIENTATION; RESIDUAL-STRESS; DESIGN; FEA; METAMATERIALS; INTEGRATION; COMPONENTS;
D O I
10.1007/s00170-023-12515-6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper discusses additive manufacturing (AM) and topology optimization (TO) and their relationship with industrial revolution 4.0. An overview of different AM techniques is given, along with the importance of design for manufacturing and assembly in progressing AM. The potential of AM to build complicated geometries with great precision has attracted a lot of interest in recent years. TO, one of the major enabling technologies in AM, has been essential in building compliant systems with improved performance across numerous industries. The development of hybrid mechanisms that integrate both compliant and stiff pieces because of improvements in "TO" algorithms has improved their usefulness and efficiency. Augmented realty and digital twins (DTs) have been used with "TO" to improve product design visualization and collaboration. Synergies between IN 4.0, TO, and AM have been discussed along with their cross-domain relevance. Machine learning involvement for more robust integration of IN 4.0 with TO and AM have also been discussed. The development of the Digital Triad, which combines DTs, digital threads, and digital trust to enable effective and secure data sharing and cooperation, is the result of the convergence of internet-of-things, cloud computing, and big data analytics. However, concerns about data privacy and cybersecurity still need to be resolved. The use of machine learning algorithms for cyberattack detection and mitigation as well as secure block chain-based frameworks for managing intellectual property rights are just a few of the frameworks and tactics that researchers have suggested to lower cybersecurity risks in AM systems. The establishment of new standards and guidelines for the cybersecurity of AM systems is anticipated to result from ongoing research in this area.
引用
收藏
页码:3771 / 3797
页数:27
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