Evaluation of Mechanical and Flame Retardant Properties of Medium Density Fiberboard Using Artificial Neural Network

Nazerian, Morteza; Shirzaii, Sakinneh; Mohebbigargari, Rahim; Vatankhah, Elham

doi:10.1590/01047760202026022725

Cited by 2 publications

(4 citation statements)

References 30 publications

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“…MAPE values of artificial networks estimating the surface roughness of different materials under different machining conditions were reported as 3,866 for solid wood material (Pinus sylvestris) Gurgen et al (2022) and 20,18 for massive wooden edge-glued panels (Sofuoglu, 2015). If the (MAPE) values are less than 10%, it is considered acceptable for a prediction with high accuracy (Nazerian et al, 2020). In this study, MAPE value was calculated as 6.61, which can be considered a good prediction.…”

Section: Resultsmentioning

confidence: 99%

The use of an artificial neural network for predicting the machining characterizing of wood materials densified by compressing

Tosun

Sofuoğlu

2023

Bilge International Journal of Science and Technology Research

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In this study, an approach for artificial neural network (ANN) was presented to predict and control arithmetical mean surface roughness value (Ra), machining properties of wood materials densified by compressing in a computer numerical control (CNC) machine. Black poplar (Populus nigra L.) tree species were used as the experimental material. After specimens were densified by Thermo-Mechanical (TM) method at 0%, 20%, and 40% ratios, machining process of specimens were performed at 1000, 1500, and 2000 mm/min feed speeds and in 12000, 15000, 18000 rpm rotation speed on a CNC vertical wood machining center by using two different cutters. Data used for the training and testing of an ANN. Cutter type, compression ratio, feed rate, and spindle speed were selected as Four parameters. While hidden layer of the Ra model has ten neurons, one hidden layer was used, Compression ratio is the most significant parameter, followed by feed speed for Ra values. surface roughness increases with increased feed rate. Ra values in training, validation, and testing the data set for Ra were 0.97122, 0.8538, and 0.76685, respectively. The Mean Square Error (MSE) value was determined as 0.0019914 test of the network. The proposed ANN model came to agreement with the measured values in predicting surface roughness Ra values of MAPE. The MAPE value was calculated as 6.61, which can be considered a very good prediction (MAPE< 10 % = very good prediction). The study showed that obtained ANN prediction model is a practical and efficient tool to model the Ra of wood. For reducing energy, time and cost in the wood industry (densification and CNC wood machining), current research results can be implemented.

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Section: Resultsmentioning

confidence: 99%

The use of an artificial neural network for predicting the machining characterizing of wood materials densified by compressing

Tosun

Sofuoğlu

2023

Bilge International Journal of Science and Technology Research

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“…In a study conducted by Nazerian et al [ 98 ], an ANN demonstrated its prediction capability in the estimation of the modulus of rupture (MOR) and mass loss (MLoss) of flame-retardant fiberboard. The researchers applied the response surface methodology and central composite rotatable design to prepare the experimental design.…”

Section: Application Of Ai/ml For Flame-retardant Materialsmentioning

confidence: 99%

“…The structure of the ANN model to compute the fiberboard properties under fire conditions. Adapted with permission from ref [98]…”

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Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Nguyên

Nguyen

et al. 2021

Molecules

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The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

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Evaluation of Mechanical and Flame Retardant Properties of Medium Density Fiberboard Using Artificial Neural Network

Cited by 2 publications

References 30 publications

The use of an artificial neural network for predicting the machining characterizing of wood materials densified by compressing

The use of an artificial neural network for predicting the machining characterizing of wood materials densified by compressing

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

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