Volume 7 , Issue 1 , PP: 40-49, 2023 | Cite this article as | XML | Html | PDF
Ahmed Hatip 1 * , Karla Zayood 2 , Rabah Scharif 3
Doi: https://doi.org/10.54216/IJWAC.070104
The convergence of Industry 4.0 and sustainability has brought forth a new era of manufacturing, where data-driven approaches play a pivotal role in achieving operational efficiency while minimizing environmental impact. This paper presents an innovative framework for sustainable smart manufacturing through data-driven predictive maintenance planning. By integrating advanced analytics and machine learning, we propose a preemptive equipment management approach that not only optimizes production processes but also fosters environmental responsibility. Our methodology combines the power of Long Short-Term Memory (LSTM) networks for pattern modeling and the Sea Lion Optimization Algorithm for feature selection. We demonstrate the effectiveness of our approach through a comprehensive empirical analysis conducted on a real case study, where the results indicate significant improvements over baseline studies, as evidenced by reduced Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), along with higher R-squared (R2) values. Our findings emphasize the synergy between technological innovation and sustainability imperatives, positioning our approach as a catalyst for reshaping modern manufacturing practices.
Sustainable manufacturing , green technologies , Environmental efficiency , Sustainable operations , Sustainable supply chain , Life cycle assessment , Equipment Management , Smart Factories , Artificial Intelligence.
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