Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

This research paper delves into an innovative utilization of neurosymbolic programming for forecasting wear rates in aluminum-silicon carbide (Al/SiC) metal matrix composites (MMCs). The study scrutinizes compositional transformations in MMCs with various weight percentages of SiC (0%, 3%, and 5%),...

Full description

Saved in:
Bibliographic Details
Published in:Lubricants 2023-06, Vol.11 (6), p.261
Main Authors: Mishra, Akshansh, Jatti, Vijaykumar S.
Format: Article
Language:English
Subjects:
Aircraft
Algorithms
Alloys
Aluminum alloys
Aluminum matrix composites
Analysis
Artificial intelligence
Artificial neural networks
Composite materials
Corrosion resistance
Energy efficiency
Heat conductivity
Mechanical properties
metal matric composites
Metal matrix composites
Neural networks
neurosymbolic artificial intelligence
Powder metallurgy
Prediction models
Programming
Silicon carbide
Taguchi, Genichi
Temperature
Wear rate
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This research paper delves into an innovative utilization of neurosymbolic programming for forecasting wear rates in aluminum-silicon carbide (Al/SiC) metal matrix composites (MMCs). The study scrutinizes compositional transformations in MMCs with various weight percentages of SiC (0%, 3%, and 5%), employing comprehensive spectroscopic analysis. The effect of SiC integration on the compositional distribution and ratio of elements within the composite is meticulously examined. In a novel move for this field of research, the study introduces and applies neurosymbolic programming as a novel computational modeling approach. The performance of this cutting-edge methodology is compared to a traditional simple artificial neural network (ANN). The neurosymbolic algorithm exhibits superior performance, providing lower mean squared error (MSE) values and higher R-squared (R2) values across both training and validation datasets. This highlights its potential for delivering more precise and resilient predictions, marking a significant development in the field. Despite the promising results, the study recognizes that the performance of the model might vary based on specific characteristics of the composite material and operational conditions. Thus, it encourages future studies to authenticate and expand these innovative findings across a wider spectrum of materials and conditions. This research represents a substantial advancement towards a more profound understanding of wear rates in Al/SiC MMCs and emphasizes the potential of the novel neurosymbolic programming in predictive modeling of complex material systems.
ISSN:2075-4442
2075-4442
DOI:10.3390/lubricants11060261