RSM and machine learning-based optimization of vitrified grinding wheels from furnace slag–ceramic waste blends

The increasing cost, limited availability, and environmental concerns associated with conventional grinding wheel materials have necessitated the exploration of alternative, sustainable options. In this study, a novel abrasive grinding wheel was developed using furnace slag and ceramic wastes, employing the vitrified bonding method. The aim was to formulate a cost-effective and high-performance grinding wheel while promoting the recycling of industrial by-products. A D-optimal mixture design was utilized to generate experimental formulations, and the grinding wheels were produced with fixed proportions of silicon carbide (95 wt. %) and sulfur (1 wt. %) along with varying percentages of furnace slag and ceramics. Mechanical tests, including hardness, compressive strength, and wear rate, were conducted alongside thermal analysis using thermogravimetric analysis, microstructural characterization using scanning electron microscopy, and elemental composition determination via X-ray fluorescence. Response Surface Methodology (RSM) and a machine learning model based on Random Forest with Bayesian optimization were employed for multi-objective optimization of the input factors. The RSM yielded an optimal formulation of 1.53 wt. % furnace slag and 2.47 wt. % ceramics, while the ML model predicted optimal values at 1.567 and 2.538 wt. %, respectively. The grinding wheel produced using the ML-optimized formulation demonstrated excellent agreement between predicted and experimental values: hardness of 93.5 HV, compressive strength of 3.65 MPa, and wear rate of 1.54 m3/N m, outperforming the control wheel in wear resistance. Statistical evaluation revealed that the ML model provided superior prediction accuracy across all performance indices compared to RSM. The study concludes that integrating industrial waste materials with AI-driven optimization offers a viable pathway toward the sustainable and high-performance development of grinding wheels. Future work is recommended to explore extended curing protocols and alternative waste materials for broader industrial applications.

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Journal
AIP Advances
Published
2026-02-01
DOI
https://doi.org/10.1063/5.0307084
Primary Topic
Recycling and utilization of industrial and municipal waste in materials production
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article
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article

RSM and machine learning-based optimization of vitrified grinding wheels from furnace slag–ceramic waste blends

I. E. Ekengwu, P. N. Atanmo, O. C. Okafor, K. C. Owuama
AIP Advances
Recycling and utilization of industrial and municipal waste in materials production
article

RSM and machine learning-based optimization of vitrified grinding wheels from furnace slag–ceramic waste blends

I. E. Ekengwu, P. N. Atanmo, O. C. Okafor, K. C. Owuama
article en

Abstract

The increasing cost, limited availability, and environmental concerns associated with conventional grinding wheel materials have necessitated the exploration of alternative, sustainable options. In this study, a novel abrasive grinding wheel was developed using furnace slag and ceramic wastes, employing the vitrified bonding method. The aim was to formulate a cost-effective and high-performance grinding wheel while promoting the recycling of industrial by-products. A D-optimal mixture design was utilized to generate experimental formulations, and the grinding wheels were produced with fixed proportions of silicon carbide (95 wt. %) and sulfur (1 wt. %) along with varying percentages of furnace slag and ceramics. Mechanical tests, including hardness, compressive strength, and wear rate, were conducted alongside thermal analysis using thermogravimetric analysis, microstructural characterization using scanning electron microscopy, and elemental composition determination via X-ray fluorescence. Response Surface Methodology (RSM) and a machine learning model based on Random Forest with Bayesian optimization were employed for multi-objective optimization of the input factors. The RSM yielded an optimal formulation of 1.53 wt. % furnace slag and 2.47 wt. % ceramics, while the ML model predicted optimal values at 1.567 and 2.538 wt. %, respectively. The grinding wheel produced using the ML-optimized formulation demonstrated excellent agreement between predicted and experimental values: hardness of 93.5 HV, compressive strength of 3.65 MPa, and wear rate of 1.54 m3/N m, outperforming the control wheel in wear resistance. Statistical evaluation revealed that the ML model provided superior prediction accuracy across all performance indices compared to RSM. The study concludes that integrating industrial waste materials with AI-driven optimization offers a viable pathway toward the sustainable and high-performance development of grinding wheels. Future work is recommended to explore extended curing protocols and alternative waste materials for broader industrial applications.

AIP AdvancesVol. 16(2)
Chukwuemeka Odumegwu Ojukwu University (NG), Imo State University (NG), Nnamdi Azikiwe University (NG)
Openalex Percentile: Top 50%
Recycling and utilization of industrial and municipal waste in materials production
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