Laminate flooring selection is an important phase in the design and construction of a building. Owing to the subjectivity and conflicting factors, the fuzzy multicriteria decision-making technique can be used for handling the problem. In this study, it is aimed to prioritize laminate flooring selection criteria from experts’ perspectives. Five main criteria and twenty subcriteria are determined with the help of experts. A three-level hierarchy is devised for comparisons. The spherical fuzzy analytic hierarchy process is used to determine the importance of each criterion. According to the results, ‘safety and health properties’ (29.0%) and ‘durability properties’ (20.6%) are the most significant main criteria. Furthermore, ‘walking safety’ (10.7%), ‘free of harmful substances’ (8.6%), and ‘scratch resistance’ (6.7%) are found as the most important subcriteria. The proposed approach presents a different view because it contributes to analyzing the key attributes of laminate flooring. The findings of this study will help building owners, architects, and designers in making informed choices.

Wooden outdoor furniture is a worthwhile investment. When evaluating outdoor furniture, it is important to weigh up decision factors. In situations where there are many conflicting factors, decision-makers become undecided about determining the best furniture option. Hence, this study proposes an interval-valued Pythagorean fuzzy analytic hierarchy process-based model to prioritise the key factors influencing wooden outdoor furniture selection. In light of the aim, five main factors were determined: “safety and health properties”, “comfort and appearance properties”, “durability and mechanical properties”, “economic aspects”, and “environmental aspects”. Each main factor was then subdivided into various subfactors. Pairwise comparisons were performed to obtain the priorities of the factors. According to the results, “safety and health properties” was the most significant main factor. The most important subfactors were found as “tipping resistance”, “environmental friendliness”, and “free of harmful substances”.

In this study, the fuzzy analytic hierarchy process was employed to prioritize some factors affecting the surface roughness of wood and wood-based materials in CNC machining. Within the model, four main factors and eighteen subfactors were defined. After constructing the hierarchical structure, the factors were analyzed through experts’ opinions. The results demonstrated that wood properties and machining parameters were the most significant main factors. Furthermore, density was found to be the most important subfactor. Consequently, the findings of this study will help the wood industry in enhancing the surface quality of final products.

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. 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. 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. 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 costs.