Potential of machine learning algorithms for predicting the properties of medium-density fiberboard (MDF): preliminary results

  • سال انتشار: 1403
  • محل انتشار: اولین همایش بین المللی هوش مصنوعی
  • کد COI اختصاصی: IAICONF01_046
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 114
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نویسندگان

Rahim Mohebbi Gargari

Department of Wood and Paper Science and Technology, Faculty of Natural Resources, PhD student, Tarbiat Modares University, Noor, Mazandaran, Iran

Ali Shalbafan

Department of Wood and Paper Science and Technology, Faculty of Natural Resources, Associate Professor, Tarbiat Modares University, Noor, Mazandaran, Iran

Seyed Jalil Alavi

Department of Forestry, Faculty of Natural Resources, Associate Professor, Tarbiat Modares University, Noor, Mazandaran, Iran

Maryam Amirmazlaghani

Department of Artificial Intelligence, Faculty of Computer Engineering, Associate Professor, Amirkabir University of Technology, Tehran, Iran

Seyed Hamzeh Sadatnejad

Quality Manager at Kimia Choob, Golestan Co, Sari, Mazandaran, Iran

Heiko Thoemen

Institute of Building Materials and Biobased Products, School of Architecture, Wood and Civil Engineering, Professor, Bern University of Applied Sciences (BFH), Biel, Switzerland

چکیده

Traditional quality control methods in the wood-based panel industry, especially for medium-density fiberboard, are insufficient to compete in the current market. In addition, due to the rapid growth of wood-based panel production and the need to provide competitive products in the market, there is an unprecedented need to explore new methods of quality control throughout the production process. Therefore, it seems necessary to use new quality control methods based on artificial intelligence and machine learning algorithms, because they have high predictive and optimization capabilities. The aim of this research is to develop suitable model to identify the most important and effective variables in the production process of industrial fiberboards and finally to predict the properties of the final product such as the bending strength (MOR) based on industrial data. For this purpose, the R software environment was used to implement the random forest algorithm to identify important variables. The performance of the model was evaluated using the coefficient of determination (R²) and the root mean square error (RMSE). The results showed moderate accuracy with an R² value of ۰.۴۹, which means that the model explained ۴۹% of the variance of the dependent variable. The RMSE was ۱.۵۶۵, indicating a low prediction error. These metrics demonstrate the robustness and reliability of the random forest algorithm in managing complex data sets and producing accurate predictions.

کلیدواژه ها

machine learning, wood-based panel, quality control, random forest, feature selection

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