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Machine Design Modern Techniques and Innovative Technologies

Hussein Younus Razzaq 1 , Hussein Mohammed Hasan 1 and Kadhim Raheem Abbas 1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1897 , Sixth International Scientific Conference for Iraqi Al Khwarizmi Society (FISCAS) 2021 22-23 November 2020, Cairo, Egypt Citation Hussein Younus Razzaq et al 2021 J. Phys.: Conf. Ser. 1897 012072 DOI 10.1088/1742-6596/1897/1/012072

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1 Karbala Technical Institute, Al-Furat Al-Awsat Technical University, 56001, Karbala, Iraq

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The manufacturing sector is facing a challenge in this 21 st century to continue developing their business by applying a new and innovative production technology and system. This is to help the novel ways of manufacturing process to move forward, where, the Machine Design will feature and compile the newest product line with an inventive technology to keep modernized techniques at the top of mind for our OEMs, end-users, integrators, and the entire supply community. This research paper will explore how the simulation derived model of Mechatronic could manage the most complex scheme of the machinery profile with a systematic approach by understanding the concept with precise machine design actions, dynamic behavior, and effective interaction with the various components of the machine. Mainly, the Mechatronic engineers will unite the mechanics, and electronics principles and compute them to generate more economical, a simpler, and reliable system.

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Journal of Mechanical Design

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The Journal of Mechanical Design publishes technical papers concerned with design automation, including design representation, virtual reality, geometric design, design evaluation, design optimization, risk and reliability-based optimization, design sensitivity analysis, system design integration, ergonomic and aesthetic considerations, and design for market systems; design of direct contact systems, including cams, gears, and power transmission systems; design education; design of energy, fluid, and power handing systems; design innovation and devices, including design of smart products and materials; design for manufacturing and the life cycle, including design for the environment, DFX, and sustainable design; design of mechanisms and robotic systems, including design of macro-, micro- and nano-scaled mechanical systems, machine component, and machine system design; design theory and methodology, including creativity in design, decision analysis, design cognition, and design synthesis.

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Vol 3, No 2 (2016)

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MACHINE DESIGN

PUBLISHED BY: University of Novi Sad Faculty of Technical Sciences ISSN 1821-1259 Print e-ISSN 2406-0666 Online

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GENERAL INFORMATION

Dear Colleagues, the journal Machine Design publishes fundamental research about mechanical engineering and design including machineelements, design fundamentals, computer aided design, product forms, shapes and performances, manufacturing processes and technologies, theory of materials, its structures and capabilities, product design management, technology management, communication and cognitive science. The journal Machine Design is published by the Faculty of Technical Sciences in Novi Sad four issues per year . Publishing this journal we would like to make mechanical engineering more interesting and to promote it as an important branch of engineering in the light of modern techniques and new technologies. The journal is a good opportunity to show and present the results of our recent work and researching. Also, it is a chance for leader researchers and scientists in the field of machine design from abroad to represent their researching results. In such way, we would like to obtain insight in the present situation of mechanical engineering in the region, to know and learn about researching in other institutions, to compare results and find out new solutions, as well as to make new contacts and find out mutual interests for international cooperation and researching on a project or some topic. The journal Machine Design is on the Index Copernicus international journals master list and on DOAJ – Directory of Open Access Journals . Its editorial board will try further to develop this publication in order to achieve and maintain a high quality of publications, so we can receive an Impact factor. Our goals are to be referred in international publication databases, to provide an international medium for scientific contribution and participation to mechanical engineers and to create a platform for the communication between science and industry in the field of technical sciences. Also, we would like to promote and to encourage international cooperation, mutual researching, projects and publishing papers between foreign partners’ institutions. Thus, we want to help better understanding and knowing about work and researching of colleagues from all over the world. I hope You will recognize the interest to publish Your paper in the journal Machine Design ; so, with a great pleasure, I call You to send further Your papers for this journal.

With deep respect and gratitude, Editors, Siniša Kuzmanović and Milan Rackov

Machine Design is currently indexed by

CONTACT INFO

Address: Faculty of Technical Sciences Trg Dositeja Obradovića 6 21000 Novi Sad Serbia

Telephone numbers: +381 21 485 2358 +381 64 153 22 67 +381 64 190 31 04 Fax number: +381 21 6350 592

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Web address: www.mdesign.ftn.uns.ac.rs

PUBLISHER: University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia SUPPORTED BY: ADEKO, Association for Design, Elements and Constructions CEEPUS III RS0304; CEEPUS III PL0033; CEEPUS III BG0703 EDITORS: Siniša KUZMANOVIĆ Milan RACKOV ISSN 1821-1259 Print e-ISSN 2406-0666 Online

Materials Horizons

Artificial intelligence and machine learning in design of mechanical materials.

* Corresponding authors

a Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave. 1-290, Cambridge, Massachusetts 02139, USA E-mail: [email protected] Tel: +1 617 452 2750

b Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, USA

c Department of Engineering Science, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan

d Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, Massachusetts 02139, USA

e Center for Materials Science and Engineering, 77 Massachusetts Ave, Cambridge, Massachusetts 02139, USA

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.

• This article is part of the themed collections: Materials Horizons 10th anniversary regional spotlight collection: The Americas , Recent Review Articles and 2021 Materials Horizons Advisory Board collection

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K. Guo, Z. Yang, C. Yu and M. J. Buehler, Mater. Horiz. , 2021,  8 , 1153 DOI: 10.1039/D0MH01451F

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Structural Plan Schema Generation Through Generative Adversarial Networks

• Published: 25 March 2024

Cite this article

• Kamile Öztürk Kösenciğ   ORCID: orcid.org/0000-0002-7497-9261 1 ,
• Elif Bahar Okuyucu   ORCID: orcid.org/0000-0003-1144-6588 2 &
• Özgün Balaban   ORCID: orcid.org/0000-0002-7270-2058 3  

This paper suggests a workflow that generates floor plans with structural elements. Generating structural layouts in a BIM environment with the implementation of a machine learning method allows a future projection for fast and easy exploration of multiple design options. Pix2Pix, a Generative Adversarial Networks (GAN) model, takes the wall layout as input and generates a structural layout by learning from existing knowledge used to generate a decision support system for structural layout generation. The paper also suggest an additional script as a fine-adjustment model to refine the structural layout based on predetermined structural rules. This script increases the accuracy of the structural layouts generated by the GAN algorithm. Based on the test dataset, the research demonstrates a 64% success rate in providing structural schema assistance. Considering the results, this study seems to have the potential to be a supportive application in the early design phase.

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Acknowledgements

The authors Kamile Öztürk Kösenciğ and Bahar Okuyucu are equal contributors to this research. Furthermore, the authors express their sincere gratitude to Alper Boray for his invaluable support and expertise in significantly enhancing the fine adjustment script, which played a crucial role in the outcome. His contributions are greatly appreciated.

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Department of Architecture, Kocaeli University, Anitpark, 41050, İzmit, Kocaeli, Turkey

Kamile Öztürk Kösenciğ

Department of Architecture, Istanbul Technical University, Taşkışla, Istanbul, Turkey

Elif Bahar Okuyucu

Tilburg School of Economics and Management, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, Netherlands

Özgün Balaban

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Correspondence to Kamile Öztürk Kösenciğ .

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Kösenciğ, K.Ö., Okuyucu, E.B. & Balaban, Ö. Structural Plan Schema Generation Through Generative Adversarial Networks. Nexus Netw J (2024). https://doi.org/10.1007/s00004-024-00766-z

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Accepted : 13 February 2024

Published : 25 March 2024

DOI : https://doi.org/10.1007/s00004-024-00766-z

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