STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research

Developing an AI-Based Novel Forecasting Framework for Surface Irregularity in Metal Matrix Materials

  1. M.V. Kulkarni1 Engineering Science and Humanities, Sanjivani College of Engineering, Kopargaon
  2. P. William Department of Information Technology, Sanjivani College of Engineering, Kopargaon,
  3. Pravin B Khatkale Sanjivani University, Kopargaon,
  4. Harshal P. Varade Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon
  5. Sandip R. Thorat Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon

Abstract

Surface irregularity in metal matrix materials (MMM) signifies the deviations from smoothness, influencing structural integrity and performance frequently arising from the manufacturing process along with intrinsic material characteristics that influence effectiveness. Limitations in data, model interpretability and complexity are the difficulties that impede artificial intelligence (AI) based surface irregularity in MMM. In this study, we suggested a novel framework of Gaussian regression fused multi-strategy adaptive boosting classifier (GR-MABC) for the prediction of surface irregularity in MMM. To begin with, the graphene-reinforced aluminum matrix composites dataset was gathered. After data collection, Z-Score normalization is applied for data preprocessing. After preprocessing we split the dataset into test and train. The train process involves our proposed GR-MABC technique and then the test and GR-MABC technique outcome contains the performance analysis. At last, we utilized parameters for proposed and existing algorithms, our proposed algorithm GR-MABC outcomes are MAE (0.0291), RMSE (0.0295) and R2 (0.9817). In a comparison of both proposed and existing methods, our proposed GR-MABC technique achieves superior outcomes. This research not only improves the reliability, but it also promises a better effective method for forecasting and addressing with material quality concerns, as well as improving the manufacturing process for greater efficiency and product dependability.

Keywords

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