A prototype knowledge-based system for the material selection of polymeric-based composites for automotive components

Sapuan, S. M.; Abdalla, Hassan

doi:10.1016/s1359-835x(98)00049-9

Cited by 58 publications

(24 citation statements)

References 11 publications

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“…They implemented artificial neural network with genetic algorithm for optimizing the multi-objectives of materials and selecting the best candidate materials. They took into consideration not only the mechanical and economical properties (Sapuan and Abdalla, 1998), but also the environmental factors for selection. However, to the knowledge of the authors, limited studies have been conducted on materials selection of natural fibre reinforced polymer composites with ANN.…”

Section: Introductionmentioning

confidence: 99%

Integration of Artificial Neural Network and Expert System for Material Classification of Natural Fibre Reinforced Polymer Composites

Ali¹,

Salit²,

Zainudin³

et al. 2015

American Journal of Applied Sciences

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Diversified choice of materials from natural fibre reinforced polymer composites with similar properties complicate the materials selection for engineering products. Implementation of expert system alone makes it difficult to scrutinize the vast selected materials. Hybrid of expert system with neural network technology is desired. Classification of material through neural network under various criteria influences the decision in narrowing down the selection. In this study, the integration of artificial neural network with expert system for material classification is explored. The computational tool Matlab is proposed for classification and the materials focused were natural fibre composites. Levenberg-Marquardt training algorithm, which provides faster rate of convergence, is applied for training the feed forward network. The system proves to be consistant with 93.3% classification accuracy with 15 neurons in the hidden layer. The validation of the output is compared with the target on the basis of desired mechanical properties of natural fibre reinforced polymer composites for automotive interior components.

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Section: Introductionmentioning

confidence: 99%

Integration of Artificial Neural Network and Expert System for Material Classification of Natural Fibre Reinforced Polymer Composites

Ali¹,

Salit²,

Zainudin³

et al. 2015

American Journal of Applied Sciences

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“…2,917 cm 21 is due to Symmetric and asymmetric stretching vibrations of CH 2 and CH 3 groups of cellulose and hemicellulose. Benzene ring stretching of Lignin is at 1,602 cm 21 …”

Section: Surface Functional Groups Of Compositesmentioning

confidence: 99%

Thermal, static, and dynamic mechanical properties of bark cloth (ficus brachypoda) laminar epoxy composites

Rwawiire

Tomková

2015

Polymer Composites

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Bark cloth is a natural nonwoven fibrous fabric produced in Uganda from three species of trees: Ficus natalensis, Ficus brachypoda, and Antiaris toxicaria. The recently developed bark cloth composites has created a new class of textile composites which can find applications in interior automotive panels. Four bark cloth plies were utilized, the resin was infused using Vacuum Assisted Resin Transfer Molding (VARTM). Scanning Electron Microscopy was used to study the fabric's fiber morphology, surface functional groups were characterized using Fourier Transform Infrared. Overall, the static and the Dynamical mechanical analysis showed that the developed composites had a tensile strength ranging from 22 to 29 MPa whereas the flexural strength ranging from 45 to 100 MPa. The glass transition temperature of the composites was ranging from 53°C to 63°C. POLYM. COMPOS., 38:199–204, 2017. © 2015 Society of Plastics Engineers

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“…Sapuan and Abdalla (1998) conducted material selection for composite pedal box system using rule and frame based expert system. However, the system was incapable to automatically rank the materials after the first selection stage.…”

Section: Introductionmentioning

confidence: 99%

Development of expert system for biobased polymer material selection: food packaging application

Sanyang

Sapuan

2015

J Food Sci Technol

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Biobased food packaging materials are gaining more attention owing to their intrinsic biodegradable nature and renewability. Selection of suitable biobased polymers for food packaging applications could be a tedious task with potential mistakes in choosing the best materials. In this paper, an expert system was developed using Exsys Corvid software to select suitable biobased polymer materials for packaging fruits, dry food and dairy products. If -Then rule based system was utilized to accomplish the material selection process whereas a score system was formulated to facilitate the ranking of selected materials. The expert system selected materials that satisfied all constraints and selection results were presented in suitability sequence depending on their scores. The expert system selected polylactic acid (PLA) as the most suitable material.

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A prototype knowledge-based system for the material selection of polymeric-based composites for automotive components

Cited by 58 publications

References 11 publications

Integration of Artificial Neural Network and Expert System for Material Classification of Natural Fibre Reinforced Polymer Composites

Integration of Artificial Neural Network and Expert System for Material Classification of Natural Fibre Reinforced Polymer Composites

Thermal, static, and dynamic mechanical properties of bark cloth (ficus brachypoda) laminar epoxy composites

Development of expert system for biobased polymer material selection: food packaging application

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