Reinforcement learning based approach for the optimization of mechanical properties of additively manufactured specimens

Mishra, Akshansh; Jatti, Vijaykumar S.

doi:10.1007/s12008-023-01257-0

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…42 Based on the composition and processing factors, ML models may predict the mechanical, thermal, and tribological characteristics of FRECs. Based on the fiber type, orientation, volume fraction, resin parameters, and processing circumstances, 43 ML may forecast material attributes such as tensile strength, flexural strength, impact resistance, and thermal conductivity. 44 In the outer groove rolling process for a profile ring, the deformation coefficient of the cross-sectional profile per revolution was introduced, demonstrating its fluctuation under various conditions.…”

Section: Introductionmentioning

confidence: 99%

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Subramanyam,

Kotikula,

Bennehalli

et al. 2024

ACS Omega

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Recent studies focus on enhancing the mechanical features of natural fiber composites to replace synthetic fibers that are highly useful in the building, automotive, and packing industries. The novelty of the work is that the woven areca sheath fiber (ASF) with different fiber fraction epoxy composites has been fabricated and tested for its tribological responses on three-body abrasion wear testing machines along with its mechanical features. The impact of the fiber fraction on various features is examined. The study also revolves around the development and validation of a machine learning predictive model using the random forest (RF) algorithm, aimed at forecasting two critical performance parameters: the specific wear rate (SWR) and the coefficient of friction (COF). The void fraction is observed to vary between 0.261 and 3.8% as the fiber fraction is incremented. The hardness of the mat rises progressively from 40.23 to 84.26 HRB. A fair ascent in the tensile strength and its modulus is also observed. Even though a short descent in flexural strength and its modulus is seen for 0 to 12 wt % composite specimens, they incrementally raised to the finest values of 52.84 and 2860 MPa, respectively, pertinent to the 48 wt % fiber-loaded specimen. A progressive rise in the ILSS and impact strength is perceptible. The wear behavior of the specimens is reported. The worn surface morphology is studied to understand the interface of the ASF with the epoxy matrix. The RF model exhibited outstanding predictive prowess, as evidenced by high R-squared values coupled with low mean-square error and mean absolute error metrics. Rigorous statistical validation employing paired t tests confirmed the model's suitability, revealing no significant disparities between predicted and actual values for both the SWR and COF.

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Section: Introductionmentioning

confidence: 99%

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Subramanyam,

Kotikula,

Bennehalli

et al. 2024

ACS Omega

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A review of machine learning in additive manufacturing: design and process

Chen,

Zhang,

Yan

et al. 2024

Int J Adv Manuf Technol

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A Review of the Applications of Machine Learning for Prediction and Analysis of Mechanical Properties and Microstructures in Additive Manufacturing

Deshmankar,

Challa,

Singh

et al. 2024

Journal of Computing and Information Science in Engineering

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This article provides an insightful review of the recent applications of machine learning (ML) techniques in additive manufacturing (AM) for the prediction and amelioration of mechanical properties, as well as the analysis and prediction of microstructures. AM is the modern digital manufacturing technique adopted in various industrial sectors because of its salient features, such as the fabrication of geometrically complex and customized parts, the fabrication of parts with unique properties and microstructures, and the fabrication of hard-to-manufacture materials. The functioning of the AM processes is complicated. Several factors, such as process parameters, defects, cooling rates, thermal histories, and machine stability, have a prominent impact on AM products' properties and microstructure. To establish the relationship between these AM factors and the end product properties and microstructure is difficult. Several studies have utilized different ML techniques to optimize AM processes and predict mechanical properties and microstructure. This paper discusses the applications of various ML techniques in AM to predict mechanical properties and optimization of AM processes for the amelioration of mechanical properties of end parts. Also, ML applications for segmentation, prediction, and analysis of AM fabricated material's microstructures and acceleration of microstructure prediction procedures are discussed in this paper.

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Reinforcement learning based approach for the optimization of mechanical properties of additively manufactured specimens

Cited by 3 publications

References 15 publications

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

A review of machine learning in additive manufacturing: design and process

A Review of the Applications of Machine Learning for Prediction and Analysis of Mechanical Properties and Microstructures in Additive Manufacturing

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