Sensitivity-Driven Deep Learning Model for Tribological Prediction in Al7075/B4C Nanocomposites Article Swipe
Ranjeet Kumbhar
,
Appaso M. Gadade
,
Rajmeet Singh
,
Divyanshi R Kumar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-8189627/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-8189627/v1
The development of lightweight, durable composites for industrial use is constrained by traditional tribological evaluation methods that are costly, time-consuming, and inadequate for capturing nonlinear interactions between material and operational parameters. This study proposes an integrated framework combining Global Sensitivity Analysis (GSA) and Machine Learning (ML) to predict the coefficient of friction (COF) and wear rate in Al7075/B4C nanocomposites. Four GSA techniques - Sobol indices, delta index, PAWN index, and mutual information - were employed to rank the significance of input parameters, including applied load, B4C reinforcement percentage, time, sliding velocity, and sliding distance. Using 10,800 experimental records from pin-on-disc tests, a Deep Residual Regression Network (DRRN) was developed to model tribological behavior. The Al7075 matrix was reinforced with boron carbide (B4C) particles at weight fractions of 0%, 4%, 8%, and 12%. Results show that B4C reinforcement significantly enhances wear resistance, with the 12% B4C composite reducing wear rate by 77% under severe conditions. The proposed framework achieved high predictive accuracy (R² = 0.93 for COF, 0.99 for wear rate).
Related Topics
Concepts
Boron carbide
Tribology
Materials science
Composite material
Composite number
Sensitivity (control systems)
Residual
Reinforcement
Bearing (navigation)
Boron nitride
Sobol sequence
Friction coefficient
Nonlinear system
Carbide
Reinforcement learning
Artificial intelligence
Matrix (chemical analysis)
Coefficient of friction
Machine learning
Silicon carbide
Deep learning
Artificial neural network
Composite laminates
Nanocomposite
Mechanical engineering
High dimensional
Reliability (semiconductor)
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.21203/rs.3.rs-8189627/v1
- https://www.researchsquare.com/article/rs-8189627/latest.pdf
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W7106620459
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7106620459Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-8189627/v1Digital Object Identifier
- Title
-
Sensitivity-Driven Deep Learning Model for Tribological Prediction in Al7075/B4C NanocompositesWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-25Full publication date if available
- Authors
-
Ranjeet Kumbhar, Appaso M. Gadade, Rajmeet Singh, Divyanshi R KumarList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-8189627/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-8189627/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-8189627/latest.pdfDirect OA link when available
- Concepts
-
Boron carbide, Tribology, Materials science, Composite material, Composite number, Sensitivity (control systems), Residual, Reinforcement, Bearing (navigation), Boron nitride, Sobol sequence, Friction coefficient, Nonlinear system, Carbide, Reinforcement learning, Artificial intelligence, Matrix (chemical analysis), Coefficient of friction, Machine learning, Silicon carbide, Deep learning, Artificial neural network, Composite laminates, Nanocomposite, Mechanical engineering, High dimensional, Reliability (semiconductor)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.accuracy | 161 |
| abstract_inverted_index.achieved | 158 |
| abstract_inverted_index.employed | 75 |
| abstract_inverted_index.enhances | 139 |
| abstract_inverted_index.friction | 52 |
| abstract_inverted_index.indices, | 65 |
| abstract_inverted_index.material | 28 |
| abstract_inverted_index.proposed | 156 |
| abstract_inverted_index.proposes | 34 |
| abstract_inverted_index.reducing | 147 |
| abstract_inverted_index.behavior. | 113 |
| abstract_inverted_index.capturing | 24 |
| abstract_inverted_index.combining | 38 |
| abstract_inverted_index.composite | 146 |
| abstract_inverted_index.developed | 109 |
| abstract_inverted_index.distance. | 94 |
| abstract_inverted_index.fractions | 126 |
| abstract_inverted_index.framework | 37, 157 |
| abstract_inverted_index.including | 83 |
| abstract_inverted_index.nonlinear | 25 |
| abstract_inverted_index.particles | 123 |
| abstract_inverted_index.velocity, | 91 |
| abstract_inverted_index.Al7075/B4C | 58 |
| abstract_inverted_index.Regression | 105 |
| abstract_inverted_index.composites | 6 |
| abstract_inverted_index.evaluation | 15 |
| abstract_inverted_index.inadequate | 22 |
| abstract_inverted_index.industrial | 8 |
| abstract_inverted_index.integrated | 36 |
| abstract_inverted_index.predictive | 160 |
| abstract_inverted_index.reinforced | 118 |
| abstract_inverted_index.techniques | 62 |
| abstract_inverted_index.Sensitivity | 40 |
| abstract_inverted_index.coefficient | 50 |
| abstract_inverted_index.conditions. | 154 |
| abstract_inverted_index.constrained | 11 |
| abstract_inverted_index.development | 2 |
| abstract_inverted_index.information | 72 |
| abstract_inverted_index.operational | 30 |
| abstract_inverted_index.parameters, | 82 |
| abstract_inverted_index.parameters. | 31 |
| abstract_inverted_index.percentage, | 88 |
| abstract_inverted_index.pin-on-disc | 100 |
| abstract_inverted_index.resistance, | 141 |
| abstract_inverted_index.traditional | 13 |
| abstract_inverted_index.experimental | 97 |
| abstract_inverted_index.interactions | 26 |
| abstract_inverted_index.lightweight, | 4 |
| abstract_inverted_index.significance | 79 |
| abstract_inverted_index.tribological | 14, 112 |
| abstract_inverted_index.reinforcement | 87, 137 |
| abstract_inverted_index.significantly | 138 |
| abstract_inverted_index.nanocomposites. | 59 |
| abstract_inverted_index.time-consuming, | 20 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |