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

Open Access

Year : 2024 | Volume : | : | Page : –
By

M.V. Kulkarni1,

P. William,

Pravin B Khatkale,

Harshal P. Varade,

Sandip R. Thorat,

Apurv Verma,

  1. 1Engineering Science and Humanities, Sanjivani College of Engineering, Kopargaon Maharashtra India
  2. Assistant Professor 2Department of Information Technology, Sanjivani College of Engineering, Kopargaon, Maharashtra India
  3. Faculty member 3Faculty of Information Technology, Victorian Institute of Technology, Australia 4Sanjivani University, Kopargaon Maharashtra India
  4. Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon Maharashtra India
  5. Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon Maharashtra India
  6. 6Department of CSE, Shri Shankaracharya Institute of Professional Management and Technology, Raipur Chhattisgarh India

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: Surface Irregularity, Metal Matrix Materials (MMM), Artificial Intelligence (AI), Gaussian regression fused multi-strategy adaptive boosting classifier (GR-MABC).

How to cite this article: M.V. Kulkarni1, P. William, Pravin B Khatkale, Harshal P. Varade, Sandip R. Thorat, Apurv Verma. Developing an AI-Based Novel Forecasting Framework for Surface Irregularity in Metal Matrix Materials. Journal of Polymer and Composites. 2024; ():-.
How to cite this URL: M.V. Kulkarni1, P. William, Pravin B Khatkale, Harshal P. Varade, Sandip R. Thorat, Apurv Verma. Developing an AI-Based Novel Forecasting Framework for Surface Irregularity in Metal Matrix Materials. Journal of Polymer and Composites. 2024; ():-. Available from: https://journals.stmjournals.com/jopc/article=2024/view=159248

Full Text PDF Download


References

  1. La Monaca, A., Murray, J.W., Liao, Z., Speidel, A., Robles-Linares, J.A., Axinte, D.A., Hardy, M.C. and Clare, A.T., 2021. Surface integrity in metal machining-Part II: Functional performance. International Journal of Machine Tools and Manufacture, 164, p.103718. DOI: https://doi.org/10.1016/j.ijmachtools.2021.103718
  2. Krajnik, P., Hashimoto, F., Karpuschewski, B., da Silva, E.J. and Axinte, D., 2021. Grinding and fine finishing of future automotive powertrain components. CIRP Annals, 70(2), pp.589-610. DOI: https://doi.org/10.1016/j.cirp.2021.05.002
  3. Kumar, R., Channi, A.S., Kaur, R., Sharma, S., Grewal, J.S., Singh, S., Verma, A. and Haber, R., 2023. Exploring the intricacies of machine learning-based optimization of electric discharge machining on squeeze cast TiB2/AA6061 composites: Insights from morphological, and microstructural aspects in the surface structure analysis of recast layer formation and worn-out analysis. Journal of Materials Research and Technology, 26, pp.8569-8603. DOI: https://doi.org/10.1016/j.jmrt.2023.09.127
  4. Sharma, A.K., Bhandari, R., Aherwar, A., Rimašauskienė, R. and Pinca-Bretotean, C., 2020. A study of advancement in application opportunities of aluminum metal matrix composites. Materials Today: Proceedings, 26, pp.2419-2424. DOI: https://doi.org/10.1016/j.matpr.2020.02.516
  5. Zhang, F., Spies, B.C., Vleugels, J., Reveron, H., Wesemann, C., Müller, W.D., van Meerbeek, B. and Chevalier, J., 2019. High-translucent yttria-stabilized zirconia ceramics are wear-resistant and antagonist-friendly. Dental Materials, 35(12), pp.1776-1790. DOI: https://doi.org/10.1016/j.dental.2019.10.009
  6. Kumar, D., Idapalapati, S., Wang, W. and Narasimalu, S., 2019. Effect of surface mechanical treatments on the microstructure-property-performance of engineering alloys. Materials, 12(16), p.2503. DOI: https://doi.org/10.3390/ma12162503
  7. Alajmi, M.S. and Almeshal, A.M., 2020. Prediction and optimization of surface roughness in a turning process using the ANFIS-QPSO method. Materials, 13(13), p.2986. DOI: https://doi.org/10.3390/ma13132986
  8. Sarker, I.H., 2022. Ai-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), p.158. DOI: https://doi.org/10.1007/s42979-022-01043-x
  9. Bhanuprakash, L., Thejasree, P., John, F. and Prabha, R., 2023. Study on mechanical and micro-structural properties of aluminium matrix composite reinforced with graphite and granite fillers. Materials Today: Proceedings. DOI: https://doi.org/10.1016/j.matpr.2023.06.243
  10. Zhou, G., Xu, C., Ma, Y., Wang, X.H., Feng, P.F. and Zhang, M., 2020. Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks. Advances in Manufacturing, 8, pp.486-507. DOI: https://doi.org/10.1007/s40436-020-00326-x
  11. Jha, P., Shaikshavali, G., Shankar, M.G., Ram, M.D.S., BANDHU, D., SAXENA, K.K., BUDDHI, D. and AGRAWAL, M.K., 2023. A hybrid ensemble learning model for evaluating the surface roughness of AZ91 alloy during the end milling operation. Surface Review and Letters, p.2340001. DOI: https://doi.org/10.1142/S0218625X23400012
  12. Sekhar, R., Singh, T.P. and Shah, P., 2022. Machine learning based predictive modeling and control of surface roughness generation while machining micro boron carbide and carbon nanotube particle reinforced Al-Mg matrix composites. Particulate Science and Technology, 40(3), pp.355-372. DOI: https://doi.org/10.1080/02726351.2021.1933282
  13. Li, Z., Zhang, Z., Shi, J. and Wu, D., 2019. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing, 57, pp.488-495. DOI: https://doi.org/10.1016/j.rcim.2019.01.004
  14. Eser, A., AşkarAyyıldız, E., Ayyıldız, M. and Kara, F., 2021. Artificial intelligence-based surface roughness estimation modelling for milling of AA6061 alloy. Advances in Materials Science and Engineering, 2021, pp.1-10. DOI: https://doi.org/10.1155/2021/5576600
  15. Nguyen, A.T., Nguyen, V.H., Le, T.T. and Nguyen, N.T., 2022. Multiobjective Optimization of Surface Roughness and Tool Wear In High-Speed Milling of AA6061 by Machine Learning and NSGA-II. Advances in Materials Science and Engineering, 2022. DOI: https://doi.org/10.1155/2022/5406570
  16. Ramesh, P. and Mani, K., 2022. Prediction of surface roughness using machine learning approach for abrasive waterjet milling of alumina ceramic. The International Journal of Advanced Manufacturing Technology, 119(1-2), pp.503-516. DOI: https://doi.org/10.1007/s00170-021-08052-9
  17. Xue, J., Huang, J., Li, M., Chen, J., Wei, Z., Cheng, Y., Lai, Z., Qu, N., Liu, Y. and Zhu, J., 2023. Explanatory Machine Learning Accelerates the Design of Graphene-Reinforced Aluminium Matrix Composites with Superior Performance. Metals, 13(10), p.1690. DOI: https://doi.org/10.3390/met13101690
  18. Ulas, M., Aydur, O., Gurgenc, T. and Ozel, C., 2020. Surface roughness prediction of machined aluminum alloy with wire electrical discharge machining by different machine learning algorithms. Journal of Materials Research and Technology, 9(6), pp.12512-12524. DOI: https://doi.org/10.1016/j.jmrt.2020.08.098
Ahead of Print Open Access Original Research
Volume
Received April 28, 2024
Accepted July 9, 2024
Published July 30, 2024
Views: 18