Evaluation of mechanical properties of fiber-reinforced syntactic foam thermoset composites: A robust artificial intelligence modeling approach for improved accuracy with little datasets
{"title":"Evaluation of mechanical properties of fiber-reinforced syntactic foam thermoset composites: A robust artificial intelligence modeling approach for improved accuracy with little datasets","authors":"Nashat Nawafleh, F. Al-Oqla","doi":"10.1515/jmbm-2022-0285","DOIUrl":null,"url":null,"abstract":"Abstract Fiber accumulation due to printing ink inconsistency makes additive manufacturing (AM) of reinforced thermoset syntactic foam composites difficult. This study predicts and analyzes the mechanical properties of AM-made carbon fiber-reinforced syntactic thermoset composites to overcome experimental limitations. Thus, an adaptive neuro-fuzzy inference system (ANFIS)-based model creates an accurate mechanical behavior prediction under a variety of conditions without experimental inquiry. Compression and flexure tests assessed the ANFIS model’s validation. The model’s predictions were very close to reality, validating the approach taken to improve the technical assessment of the created composites, which are perfect for weight reduction, mechanical improvement, and product complexity.","PeriodicalId":17354,"journal":{"name":"Journal of the Mechanical Behavior of Materials","volume":" ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Mechanical Behavior of Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/jmbm-2022-0285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 3
Abstract
Abstract Fiber accumulation due to printing ink inconsistency makes additive manufacturing (AM) of reinforced thermoset syntactic foam composites difficult. This study predicts and analyzes the mechanical properties of AM-made carbon fiber-reinforced syntactic thermoset composites to overcome experimental limitations. Thus, an adaptive neuro-fuzzy inference system (ANFIS)-based model creates an accurate mechanical behavior prediction under a variety of conditions without experimental inquiry. Compression and flexure tests assessed the ANFIS model’s validation. The model’s predictions were very close to reality, validating the approach taken to improve the technical assessment of the created composites, which are perfect for weight reduction, mechanical improvement, and product complexity.
期刊介绍:
The journal focuses on the micromechanics and nanomechanics of materials, the relationship between structure and mechanical properties, material instabilities and fracture, as well as size effects and length/time scale transitions. Articles on cutting edge theory, simulations and experiments – used as tools for revealing novel material properties and designing new devices for structural, thermo-chemo-mechanical, and opto-electro-mechanical applications – are encouraged. Synthesis/processing and related traditional mechanics/materials science themes are not within the scope of JMBM. The Editorial Board also organizes topical issues on emerging areas by invitation. Topics Metals and Alloys Ceramics and Glasses Soils and Geomaterials Concrete and Cementitious Materials Polymers and Composites Wood and Paper Elastomers and Biomaterials Liquid Crystals and Suspensions Electromagnetic and Optoelectronic Materials High-energy Density Storage Materials Monument Restoration and Cultural Heritage Preservation Materials Nanomaterials Complex and Emerging Materials.