Vol. 3 No. 1 (2023): Issue 3
Articles

Process Parameter Optimization through Decision Tree Regression

PDF
  • Nkosi Thandiwe,
  • Mokoena Sipho,
  • Dlamini Zanele

Published 2023-06-14

Keywords

  • Process Parameters,
  • Performance Enhancement,
  • Decision Tree Regression,
  • Predictive Modeling,
  • Historical Data Analysis

How to Cite

Thandiwe, N., Sipho, M., & Zanele, D. (2023). Process Parameter Optimization through Decision Tree Regression. Optimizations in Applied Machine Learning, 3(1). Retrieved from https://ojs.mri-pub.com/index.php/OAML/article/view/38
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Optimization of process parameters is crucial for enhancing performance and efficiency across various industries. However, the current research landscape faces challenges in accurately predicting optimal settings due to the complex interaction of multiple parameters. This study addresses the need for a more effective optimization approach through the utilization of Decision Tree Regression. By leveraging this method, the research proposes an innovative framework for optimizing process parameters, which involves the development of a predictive model based on historical data analysis. The model aims to identify the most influential parameters and their respective optimal values, thus enabling improved process efficiency and performance. Ultimately, this paper contributes to advancing the field by offering a novel solution for process parameter optimization through Decision Tree Regression analysis.

PDF

References

  1. H. Y. Chia et al., "Process parameter optimization of metal additive manufacturing: a review and outlook," Journal of Materials Informatics, 2022.
  2. M. H. Ali et al., "Powder bed fusion-laser melting (PBF-LM) process: latest review of materials, process parameter optimization, application, and up-to-date innovative technologies," Progress in Additive Manufacturing, 2022.
  3. Y.-T. Zhang et al., "Application of a Bio-Inspired Algorithm in the Process Parameter Optimization of Laser Cladding," Machines, 2022.
  4. K. Kalita et al., "A Hybrid TOPSIS-PR-GWO Approach for Multi-objective Process Parameter Optimization," Process Integration and Optimization for Sustainability, 2022.
  5. S. Dharmadhikari et al., "A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing," Additive Manufacturing, 2022.
  6. A. Dey and N. Yodo, "A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics," Journal of Manufacturing and Materials Processing, 2019.
  7. N. Shamsaei et al., "An overview of Direct Laser Deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and control," Additive manufacturing, 2015.
  8. S. Wu et al., "Process parameter optimization and EBSD analysis of Ni60A-25% WC laser cladding," International Journal of Refractory Metals & Hard Materials, 2021.
  9. K. O’Dowd and S. Pillai, "Photo-Fenton disinfection at near neutral pH: Process, parameter optimization and recent advances," Journal of Environmental Chemical Engineering, 2020.
  10. P. Koirala and C. Fleming, "Solving Offline Reinforcement Learning with Decision Tree Regression," 2024.
  11. S. C. Tan, "Decision Tree Regression with Residual Outlier Detection," Journal of Data Science and Intelligent Systems, 2024.
  12. M. F. Javaid et al., "Decision tree regression algorithm-based model for prediction of traffic-induced vibrations," Innovative Infrastructure Solutions, 2023.
  13. E. Jumin et al., "Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia," Environmental science and pollution research international, 2021.
  14. M. Abdurohman et al., "A Robust Internet of Things-based Aquarium Control System using Decision Tree Regression Algorithm," IEEE Access, 2022.
  15. A. Balogun and A. Tella, "Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression," Chemosphere, 2022.
  16. E. Pekel, "Estimation of soil moisture using decision tree regression," Theoretical and Applied Climatology, 2019.
  17. R. Karim et al., "Stock Market Analysis Using Linear Regression and Decision Tree Regression," 2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA), 2021.
  18. S. Latif, "Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete," Environmental science and pollution research international, 2021.
  19. M. Rakhra et al., "Crop Price Prediction Using Random Forest and Decision Tree Regression:-A Review," Materials Today: Proceedings, 2021.
  20. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  21. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  22. Y. Liu and J. Wang, ‘AI-Driven Health Advice: Evaluating the Potential of Large Language Models as Health Assistants’, Journal of Computational Methods in Engineering Applications, pp. 1–7, Nov. 2023, doi: 10.62836/jcmea.v3i1.030106.
  23. Y. Gan and D. Zhu, ‘The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture’, Innovations in Applied Engineering and Technology, pp. 1–19, 2024.
  24. D. Zhu, Y. Gan, and X. Chen, ‘Domain Adaptation-Based Machine Learning Framework for Customer Churn Prediction Across Varing Distributions’, Journal of Computational Methods in Engineering Applications, pp. 1–14, 2021.
  25. X. Deng, L. Li, M. Enomoto, and Y. Kawano, ‘Continuously frequency-tuneable plasmonic structures for terahertz bio-sensing and spectroscopy’, Scientific reports, vol. 9, no. 1, p. 3498, 2019.
  26. X. Deng, M. Simanullang, and Y. Kawano, ‘Ge-core/a-si-shell nanowire-based field-effect transistor for sensitive terahertz detection’, in Photonics, MDPI, 2018, p. 13. Accessed: Feb. 01, 2025. [Online]. Available: https://www.mdpi.com/2304-6732/5/2/13
  27. H. Zhang, D. Zhu, Y. Gan, and S. Xiong, ‘End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection’, Journal of Information, Technology and Policy, pp. 1–17, 2024.
  28. D. Zhu, X. Chen, and Y. Gan, ‘A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction’, Journal of Electronic & Information Systems, vol. 4, no. 1.
  29. X. Deng and Y. Kawano, ‘Surface plasmon polariton graphene midinfrared photodetector with multifrequency resonance’, Journal of Nanophotonics, vol. 12, no. 2, pp. 026017–026017, 2018.