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Influence of machining parameters on edge quality of particleboards: modeling and optimization
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Influence of machining parameters on edge quality of particleboards: modeling and optimization
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Journal Article
Influence of machining parameters on edge quality of particleboards: modeling and optimization
2024
Overview
The principal objective of this study is to apply the response surface methodology (RSM) to modeling and optimize the cutting-edge quality of three distinct types of PBs (
Standard
M60,
Eucalyptus Bark
EU, and
fine-urea
FU) under modified sawing conditions. The experimental work was conducted using a multi-function sawing machine equipped with a circular saw that was instrumented with a pair of piezo-electric sensors, a high-performance vector AC drive, and a current transducer. The edge-profile quality was assessed by an artificial innovative vision system able to extract a virtual profile, and from this, delamination criteria were developed by applying a fast Fourier transform (FFT). RSM was used to optimize the delamination criterion (
Tw
), specific cutting energy (
Es
), and feed per tooth (
fz
) as response variables when influenced by the factors feed speed (
Vf
) and frequency (
N
). The optimization process showed that PBs with fine-urea (FU) yielded the best edge-quality performing results with lower Tw delamination factor, however, with higher specific cutting energy (Es) when compared to particleboards composed of 60% recycled wood (M60) and Eucalyptus-based composition (EU). It can be explained by the fact that the special FU composition contributes to a greater blinding contact zone of particles resulting in better aggregation and superior cutting-edge quality. Correspondingly, the numerical optimization indicates that the delamination factor (Tw) is lower for FU (representing only 9.3% higher than the desirable value, DV) when compared with the desirable values of the particleboard M60 and EU. In addition to this aspect of the FU composition, the physical–mechanical properties also influence the edge quality; these aspects are considered the ones that most affect the
Tw
response parameter. The lowest
Tw
value was obtained for
N
= 50 Hz combined with a
Vf
= 9 m/min, indicating these particular machining conditions positively affect this response parameter. In conclusion, this study demonstrated that both the experimental and prediction results correlated well and highlighted that the use of the RSM is appropriate for edge quality analysis of the specific type of PB machining conditions and response parameters considered in this study. In addition, the suggested system based on an artificial vision technique for evaluating edge quality exhibited good capabilities for implementation in an industrial environment.
Publisher
Springer London,Springer Nature B.V
