[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125189-en":3,"doc-seo-125189-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125189,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Artificial intelligence and machine learning for additive manufacturing composites toward enriching Metaverse technology","Digital environments such as Metaverse have expanded over the past decade, motivating engineering methods that can support this technology. This study develops an AI-based approach for analyzing and predicting the mechanical properties of carbon-fiber-reinforced syntactic thermoset composites produced via additive manufacturing. The proposed direction targets the metaverse value chain by enabling AI/ML-driven simulations, design exploration, and user-tailored feedback. An Adaptive Neuro-Fuzzy Inference System (ANFIS) model anticipates mechanical-system behavior with limited measurements and is validated using flexure and compression testing. The method supports near-realistic prediction while reducing weight, improving mechanical performance, and simplifying product complexity.","Article  \nArtificial intelligence and machine learning for additive manufacturing composites toward enriching Metaverse technology  \nFaris M. AL-Oqla*, Nashat Nawafleh  \nDepartment of Mechanical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan  \n* Corresponding author: Faris M. AL-Oqla, [fmaloqla@hu.edu.jo](fmaloqla@hu.edu.jo)  \nCITATION  \n\n| AL-Oqla FM, Nawafleh N. Artificial intelligence and machine learning for additive manufacturing composites toward enriching Metaverse\u003Cbr>technology. Metaverse. 2024; 5(2): 2785. [https://doi.org/10.54517/m.v5i2.2785](https://doi.org/10.54517/m.v5i2.2785)\u003Cbr>ARTICLE INFO |\n| --- |\n| Received: 26 June 2024\u003Cbr>Accepted: 1 August 2024\u003Cbr>Available online: 30 October 2024\u003Cbr>COPYRIGHT |\n\nCopyright © 2024 by author(s) . Metaverse is published by Asia Pacific Academy of Science Pte. Ltd. This work is licensed under the Creative Commons Attribution (CC BY) license. [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)[ ](https://creativecommons.org/licenses/)[by/4.0/](by/4.0/)  \nAbstract: As a result of the growing significance and application of technology across a wide range of fields, digital environments such as Metaverse started to take shape over the span of the previous decade. This study aims to discover an area of engineering that could benefit from this new technology by developing an artificial intelligence (AI)—based approach to analyzing and predicting the mechanical properties of carbon fiber reinforced syntactic thermoset composites that are made through additive manufacturing (AM). These composites are intended to be utilized as a tool for metaverse technology in a variety of domains—as the presence of the limitations in the currently experimental methods. The metaverse allows for the generation of simulations through the application of artificial intelligence (AI) and machine learning (ML) . Consequently, this paves the way for individuals to investigate various design possibilities and view the virtual manifestation of those possibilities. This is made possible by the use of machine learning algorithms, which allow for the monitoring and evaluation of user performance, as well as the provision of individualized feedback and suggestions for improvement. As a consequence of this, it is feasible that professionals will be able to get education and training that are both more efficient and effective. Consequently, this work aims to introduce an Adaptive Neuro-Fuzzy Inference System (ANFIS)—based model, which is able to effectively anticipate the behavior of mechanical systems in a variety of settings without the need for significant measurements. The validity of the ANFIS model was determined through the utilization offlexure and compression testing. The approach that was used to improve the technical assessment of the manufactured composites—is verified by the model ’s near-realistic predictions. Moreover, this method is superb for lowering weight, enhancing mechanical qualities, and minimizing product complexity.  \nKeywords: metaverse; composite materials; additive manufacturing; machine learning; adaptive neuro-fuzzy inference system (ANFIS)  \n1. Introduction  \nStarting with the introduction of the “metaverse” concept by Neal Stephenson in his book “Snow Crash” in 1992 [1], people are trying to immerse themselves in this environment in order to live a life that is similar to that of reality. The former term “meta” refers to a place that is beyond reality, also known as a realistic domain. The latter term, “verse” refers to the universe [2] . Examples include prospective online communities, the global web, and virtual worlds, all of which contribute to the overall air of ambiguity that surrounds it. The general view, on the other hand, is that users who are located in the real world connect to and control their avatars that are located in the metaverse by means of access terminals. This allows them to completely submerge t","cbCaidKbMywKXcU4","https://ap.wps.com/l/cbCaidKbMywKXcU4","pdf",1526799,1,15,"English","en",105,"# Introduction\n## Metaverse concept and enabling technologies\n## Interdependence, user base, and ecosystem dynamics\n# Method and model overview\n## AI/ML-based property prediction for AM composites\n## ANFIS model and validation\n# Experimental validation\n## Flexure testing\n## Compression testing\n# Outcomes and implications\n## Prediction accuracy and design/education potential","[{\"question\":\"What engineering problem does the study address for Metaverse technology?\",\"answer\":\"The study seeks an engineering approach that uses AI to analyze and predict the mechanical properties of AM-made carbon fiber reinforced syntactic thermoset composites, enabling support for Metaverse applications.\"},{\"question\":\"How does the proposed ANFIS model contribute to mechanical property prediction?\",\"answer\":\"It anticipates the behavior of mechanical systems in different settings without requiring significant measurements, then provides predictions that closely match validated results.\"},{\"question\":\"How is the model validated in the paper?\",\"answer\":\"Validation is performed using flexure and compression testing, confirming near-realistic predictions for the manufactured composites.\"}]","Artificial intelligence and machine learning for additive manufacturing composites toward enriching Metaverse technology | 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engineering problem does the study address for Metaverse technology?","Question",{"text":75,"@type":76},"The study seeks an engineering approach that uses AI to analyze and predict the mechanical properties of AM-made carbon fiber reinforced syntactic thermoset composites, enabling support for Metaverse applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed ANFIS model contribute to mechanical property prediction?",{"text":80,"@type":76},"It anticipates the behavior of mechanical systems in different settings without requiring significant measurements, then provides predictions that closely match validated results.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model validated in the paper?",{"text":84,"@type":76},"Validation is performed using flexure and compression testing, confirming near-realistic predictions for the manufactured 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