Artificial Neural Networkebased Prediction Model to Minimize Dust
Emission in the Machining Process

Artificial Neural Networkebased Prediction Model to Minimize Dust
Emission in the Machining Process

Artificial neural network
Dust emission
Ergonomics
Forest industry
Material processing

Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause
respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission
is important for devising effective mitigation strategies, ensuring a safer working environment, and
minimizing environmental impact. This study focuses on developing an artificial neural network (ANN)
model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech
(Fagus orientalis L.), and medium-density fiberboards.
Methods: The multilayer feed-forward ANN model is developed using a customized application built
with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades,
and cutting depth, whereas the output is the dust emission. Model performance is assessed through
graphical and statistical comparisons.
Results: The results reveal that the developed ANN model can provide adequate predictions for dust
emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study
predicts intermediate dust emission values for different cutting widths and cutting depths, which are not
considered in the experimental work. It is observed that dust emission tends to decrease with reductions
in cutting width and cutting depth.
Conclusion: This study introduces an alternative approach to optimize machining-process conditions for
minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission
values without the need for additional experimental activities, thereby reducing experimental time and
cost

Hilal Singer 1,
*, Abdullah C. _
Ilçe 1
, Yunus E. S¸ enel 2
, Erol Burdurlu 3

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1Department of Industrial Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey
2Nevzat Hüseyin Tiryaki Vocational and Technical Anatolian High School, Ankara, Turkey
3Department of Wood Products Industrial Engineering, Gazi University, Ankara, Turkey

5 July 2024

FAJAR BAGUS W

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