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

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Abstract

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.

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Abbreviations

TO:

Topology optimization

AM:

Additive manufacturing

RP:

Rapid prototyping

DFM:

Design for manufacturing

DFAM:

Design for additive manufacturing

DFMA:

Design for manufacturing and assembly

DMLS:

Direct metal laser sintering

WAAM:

Wire arc additive manufacturing

FDM:

Fused deposition modeling

EBM:

Electron beam melting

SLS:

Selective laser sintering

HM:

Homogenization method

SIMP:

Solid isotropic material with penalization method

ESO:

Evolutionary structural optimizations

BESO:

Bi-directional evolutionary structural method

LSM:

Level set method

MAM:

Metal additive manufacturing

LENS:

Laser engineering net shape

NPJ:

Nano-particle jetting

SM:

Subtractive manufacturing

EQ:

Equality

INEQ:

Inequality

BC:

Boundary condition

MZD:

Minimized

DDM:

Direct digital manufacturing

CMS:

Cloud manufacturing services

PoA:

Point of access

CP:

Craft production

MP:

Mass production

MC:

Mass customization

AR:

Augmented reality

IN 4.0:

Industry 4.0

DT:

Digital twin

ML:

Machine learning

IoT:

Internet-of-things

MP:

Mass production

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Authors and Affiliations

  1. Industrial and Manufacturing Engineering Department, University of Engineering and Technology, Lahore, 548900, Pakistan

    Kashif Ishfaq, Muhammad Dawar Azhar Khan & Muhammad Asad Maqsood

  2. Department of Engineering Sciences, National University of Sciences and Technology, Islamabad, 44000, Pakistan

    Muhammad Atyab Azhar Khan

  3. Intelligent Systems Center, Missouri University of Science and Technology, Rolla, MO, 65409, USA

    Muhammad Arif Mahmood

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  3. Muhammad Atyab Azhar Khan
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  4. Muhammad Arif Mahmood
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  5. Muhammad Asad Maqsood
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Ishfaq, K., Khan, M.D.A., Khan, M.A.A. et al. A correlation among industry 4.0, additive manufacturing, and topology optimization: a state-of-the-art review. Int J Adv Manuf Technol 129, 3771–3797 (2023). https://doi.org/10.1007/s00170-023-12515-6

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