This paper focuses on the prioritization of factors having substantial effects on the surface roughness of wood and wood-based materials in the sawing process. Within the model, four main factors were defined: cutting tool properties, machining parameters, wood structure and properties, and cutting phenomena. Furthermore, each main factor was subdivided into various subfactors. The analytic hierarchy process method was proposed to obtain the priorities of the factors. The results showed that feed speed, tooth shape and geometry, and cutting speed are the most important factors. Based on the obtained results, it can be said that the most important factors can be easily determined by the proposed method. Consequently, this study presents a road map for the wood industry to achieve a high quality surface.
Aim of study: The power consumption of machining operations is an important part of the total production cost. Therefore, in this study, an artificial neural network (ANN) model was developed to model the effects of treatment, rotation speed, cutting depth, and feed rate on power consumption in the wood milling process. Material and methods: A multilayer feed-forward ANN was employed for the prediction of power consumption. The accuracy of the model was assessed by performance indicators such as MAPE, RMSE, and R². Main results: It has been observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R 2 values were obtained as 7.533, 0.027, and 0.9737 %, respectively, in the testing phase. Furthermore, it was found that power consumption decreased with decreasing of feed rate and cutting depth. Research highlights: The findings of this study can be used effectively in the forest industry to reduce the experimental time and costs.
In this study, the hybrid approach of the analytic hierarchy process (AHP) and the multi-objective optimization on the basis of ratio analysis (MOORA) was used in order to select the most suitable softwood timber for constraction. Douglas fir (Pseudotsuga menziesii), Lodgepole pine (Pinus concorta), Red pine (Pinus resinosa), Redwood (Sequoia sempervirens), Engelmann spruce (Picea engelmannii), Eastern hemlock (Tsuga canadensis), Western larch (Larix occidentalis) and Western redcedar (Thuja plicata) were evaluated in terms of economic, physical, mechanical, thermal and durability properties. According to the results, the most suitable timbers for structural and non-structural applications were determined western larch and redwood, respectively.
Background: Noise produced during machining of wood materials can be a source of harm to workers and an environmental hazard. Understanding the factors that contribute to this noise will aid the development of mitigation strategies. In this study, an artificial neural network (ANN) model was developed to model the effects of wood species, cutting width, number of blades, and cutting depth on noise emission in the machining process.
Methods: A custom application created with MATLAB codes was used for the development of the multilayer feed-forward ANN model. Model performance was evaluated by numerical indicators such as MAPE, RMSE, and R2.
Results: The ANN model performed well with acceptable deviations. The MAPE, RMSE, and R2 values were 0.553%, 0.600, and 0.9824, respectively, in the testing phase. Furthermore, this study predicted the intermediate values not provided from the experimental study. The model predicted that lower noise emissions would occur with decreased cutting width and cutting depth.
Conclusions: ANNs are quite effective in predicting the noise emission. Practitioners relying on the ANN approach for investigating the effects of various factors on noise emission can save time and costs by reducing the number of experimental combinations studied to generate predictive models.
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