Empirical analysis of dust and noise pollutants produced in the wood-CNC machining process.

Autor: Rabiei F; Department of Mechanical Engineering, Faculty of Engineering, Ilam University, Ilam, 69315-516, Iran., Seidi M; Department of Computer Engineering, Faculty of Engineering, Ilam University, Ilam, Iran., Yaghoubi S; Department of Mechanical Engineering, Faculty of Engineering, Ilam University, Ilam, 69315-516, Iran. S.yaghoubi@ilam.ac.ir.
Jazyk: angličtina
Zdroj: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Jul; Vol. 31 (35), pp. 47584-47597. Date of Electronic Publication: 2024 Jul 13.
DOI: 10.1007/s11356-024-34368-6
Abstrakt: In the manufacturing processes, consideration of sustainability is of particular importance. The current study is concerned with the influences of changing the process variables on the reduction of pollutions in the wood-CNC machining operation. Noise and dust are the studied pollutants in the present research work. Process variables include feed rate, spindle speed, step-over, and depth of cut, and the aim is to predict the behavior of aforementioned pollutants variations in the current process. The amounts of these harmful factors are measured based on existing standards. In order to analyze the findings, adaptive neuro-fuzzy inference system (ANFIS) and regression analysis methods have been employed, separately. The effects of process parameters on response variables have been comprehensively studied. The research findings demonstrated that for the present problem, ANFIS outcomes are more accurate. According to the mean absolute error (MAE) criterion, the prediction errors of ANFIS for noise and dust factors were computed to, in turn, 0.50 and 14.89. Meanwhile, the error values for prediction of noise and dust responses using regression analysis were calculated as 1.54 and 34.62, respectively.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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