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

Mishra, Akshansh; Jatti, Vijaykumar S.

doi:10.3390/lubricants11060261

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…This research highlights the effectiveness of ANN in predicting tool wear, offering a novel approach to monitoring and optimizing machining processes. Mishra et al [26] presented an algorithm based on neurosymbolic artificial intelligence for predicting wear behavior in AMMCs reinforced with SiC. Their findings indicate superior performance over traditional ANN models, emphasizing the potential of integrating symbolic reasoning with neural learning for enhanced predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fabrication and Dry-Sliding Wear Characterization of Open-Cell AlSn6Cu–Al2O3 Composites with LSTM-Based Coefficient of Friction Prediction

Kolev,

Drenchev,

Petkov

et al. 2024

Metals

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This study investigates the fabrication, wear characterization, and coefficient of friction (COF) prediction of open-cell AlSn6Cu–Al2O3 composites obtained by a liquid-state processing technique. Focusing on wear behavior under varying loads using the pin-on-disk method, this research characterizes microstructure and phase composition via SEM, EDS, and XRD analyses. A novel aspect of this research is the application of an LSTM recurrent neural network model for the fast and accurate prediction of the COF of the composites, eliminating the need for extensive experimental work. Additionally, feature importance analysis using Random Forest regressors is conducted to ascertain the relative contribution of each input variable to the output variable, enhancing our understanding of the wear mechanisms in these materials. The results demonstrate the effectiveness of the composite’s reinforcement in improving wear resistance, highlighting the critical role of mechanical stress and the reinforcement’s hardness in the wear process. The quantitative findings related to the wear behavior include a mass-wear reduction in the open-cell AlSn6Cu–Al2O3 composite from 8.05 mg to 1.90 mg at 50 N and a decrease from 17.55 mg to 8.10 mg at 100 N, demonstrating the Al2O3 particles’ effectiveness in improving wear resistance under different loads.

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Section: Introductionmentioning

confidence: 99%

Fabrication and Dry-Sliding Wear Characterization of Open-Cell AlSn6Cu–Al2O3 Composites with LSTM-Based Coefficient of Friction Prediction

Kolev,

Drenchev,

Petkov

et al. 2024

Metals

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Multi-Objective Metaheuristic Optimization for Enhancing Wear Characteristics in Al6060/Si3N4/Bn Hybrid Composites

Jai Rajesh,

Balambica,

Achudhan

2024

J. Phys.: Conf. Ser.

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This paper is devoted to research work on the tribological parameters of Si3N4/BN coating composites whose goal is to minimize the wear rate and COF (coefficient of friction). A variety of experimental studies were carried out employing several parameter variations such as various amplitudes of applied loading, sliding speed, distance, and Si3N4 and BN weight fractions. The experimental results were applied to make a sense of the models which expressed the COF and the wear rate with the Si3N4 and BN weight percentages; applied load; sliding velocity as well as sliding distance. Predictive models were calibrated on platform and experimental results and have shown good agreement. Furthermore, among cause identification was performed an in-depth mistake analysis to reveal affects on the accuracy of prediction. The paper will then branch out into multi-objective optimization through applying various algorithms, such as Particle Swarm (PSO), Firefly Algorithm, Cuckoo-Search, Grey Wolf Optimization (GWO), Multi-objective Teaching-Learning Based Optimization (MOTLBO), and Genetic Algorithm mixed with Non-dominated Sorting Genetic Algorithm II (GA-NSGA-II The optimized tribological parameters were figured out. For instance, it was revealed at what weight percentages Si3N4 and BN were optimal, how applied normal load and sliding velocity should be and how far sliding distance ought to be. The work is summarized in a part depicting the validation of models as predictive and the targeting of multiple objectives to further enhance the overall tribological results. The real world implications of enhancing abrasion resistance as regards composite coatings which consist of Si3N4/BN particles and the potential uses in different industries are described as well. Apart from this, it is also suggested to perform several additional experiments for this purpose, such as the testing different optimization algorithms and analyzing different impacting parameters. It contributes to the response of the tribological influence on composite coating revealing more complex data about molecular processes that are consequently useful for shell-like materials design.

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Tribological analysis of titanium alloy (Ti-6Al-4V) hybrid metal matrix composite through the use of Taguchi’s method and machine learning classifiers

Jatti,

Sawant,

Deshpande

et al. 2024

Front. Mater.

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The preparation and tribological behavior of the titanium metal matrix (Ti-6Al-4V) composite reinforced with tungsten carbide (WCp) and graphite (Grp) particles were investigated in this study. The stir casting procedure was used to fabricate the titanium metal matrix composites (TMMCs), which had 8 weight percent of WCp and Grp. The tribological studies were designed using Taguchi’s L27 orthogonal array technique and were carried out as wear tests using a pin-on-disc device. According to Taguchi’s analysis and ANOVA, the most significant factors that affect wear rate are load and distance, followed by velocity. The wear process was ascertained by scanning electron microscopy investigation of the worn surfaces of the composite specimens. Pearson’s heatmap and Feature importance (F-test) were plotted for data analysis to study the significance of input parameters on wear. Machine learning classification algorithms such as k-nearest neighbors, support vector machine, and XGBoost algorithms accurately classified the wear rate data, giving an accuracy value of 71.25%, 65%, and 56.25%, respectively.

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

Cited by 3 publications

References 32 publications

Fabrication and Dry-Sliding Wear Characterization of Open-Cell AlSn6Cu–Al2O3 Composites with LSTM-Based Coefficient of Friction Prediction

Fabrication and Dry-Sliding Wear Characterization of Open-Cell AlSn6Cu–Al2O3 Composites with LSTM-Based Coefficient of Friction Prediction

Multi-Objective Metaheuristic Optimization for Enhancing Wear Characteristics in Al6060/Si3N4/Bn Hybrid Composites

Tribological analysis of titanium alloy (Ti-6Al-4V) hybrid metal matrix composite through the use of Taguchi’s method and machine learning classifiers

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