Prediction of mechanical properties of wood fiber insulation boards as a function of machine and process parameters by random forest

Schubert, Mark; Luković, Mirko; Christen, Hans M.

doi:10.1007/s00226-020-01184-3

Cited by 20 publications

(8 citation statements)

References 29 publications

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“…In terms of data selection, because the original experiment has taken the arithmetic mean of three parallel experiments for each group of experimental data to avoid large bias [15], and although data partitioning can effectively improve the performance of the model, data partitioning is of little help in explaining the model, so data partitioning is not performed on the data in this paper, which will be a point of attention to continue optimizing the model in the future. Through the existing literature data and error rate judgment, we tentatively believe that the optimization model has a higher accuracy rate than some other algorithms; for example, M. Schubert et al [24] also conducted a model error calculation of RMSE for compressive strength, and the result was 17.19, while the result of the TSSA-BP model was 3.90. This comparison result was affected by the sample size, and the processing parameters might not have been accurate, but in conditions permitting, further research can be done in the future.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Mechanical Properties of Thermally Modified Wood Based on TSSA-BP Model

Wang

2022

Forests

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In order to demonstrate whether the sparrow search algorithm can show good performance in optimization, this paper improves the prediction model by this algorithm and predicts the change data of wood mechanical properties under different conditions, which better reflects the connection between the process parameters of wood heat treatment and the change of wood mechanical properties. The article takes the five main mechanical property parameters of thermally modified wood: compressive strength along the grain, flexural strength, flexural elastic modulus, radial hardness, and tangential hardness, respectively, as the objects of study and improves the sparrow search algorithm by Tenting chaotic mapping and then optimizes the Back Propagation (BP) network model by this algorithm. The results show that the number of iterations of the optimized Tent-Sparrow search algorithm-Back Propagation network model (TSSA-BP) is only one-eighth that of the original BP network model, and the convergence speed is greatly improved, the root mean square error of the TSSA-BP model is at least one-half times that of the original BP model, and the optimized model fits the original data better in terms of predicted values; thus, this article provided a feasible prediction algorithm for the field related to the mechanical property changes of wood after heat treatment.

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Section: Resultsmentioning

confidence: 99%

Prediction of Mechanical Properties of Thermally Modified Wood Based on TSSA-BP Model

Wang

2022

Forests

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“…Random forest uses the bagging method, in which each tree is trained using a subset of data, and the model output is based on the voting scheme among weak learners [ 64 ]. Random forest was used to predict the mechanical properties of wood fiber insulation boards [ 65 ]. It is also utilized in wood machining for tool temperature prediction [ 66 ] and frozen lumber classification [ 67 ].…”

Section: Methodsmentioning

confidence: 99%

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

et al. 2023

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Monitoring the moisture content (MC) of wood and avoiding large MC variation is a crucial task as a large moisture spread after drying significantly devalues the product, especially in species with high green MC spread. Therefore, this research aims to optimize kiln-drying and provides a predictive approach to estimate and classify target timber moisture, using a gradient-boosting machine learning model. Inputs include three wood attributes (initial moisture, initial weight, and basic density) and three drying parameters (schedule, conditioning, and post-storage). Results show that initial weight has the highest correlation with the final moisture and possesses the highest relative importance in both predictive and classifier models. This model demonstrated a drop in training accuracy after removing schedule, conditioning, and post-storage from inputs, emphasizing that the drying parameters are significant in the robustness of the model. However, the regression-based model failed to satisfactorily predict the moisture after kiln-drying. In contrast, the classifying model is capable of classifying dried wood into acceptable, over-, and under-dried groups, which could apply to timber pre- and post-sorting. Overall, the gradient-boosting model successfully classified the moisture in kiln-dried western hemlock timber.

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“…The sandpaper types, sanding time and density of rice straw particleboard have a clear effect on the surface roughness of the particleboard [ 12 ]. In addition, machine learning can be applied to predict the relevant properties of wood fiber boards for improved quality control in real time [ 13 ]. Numerous studies have indicated that the performance of particleboards is positively related to paving technology, therefore, research on particleboard paving equipment is important.…”

Section: Introductionmentioning

confidence: 99%

Interaction and influence of a flow field and particleboard particles in an airflow forming machine with a coupled Euler-DPM model

et al. 2021

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Particleboards are widely used in the artificial board market, which can be constructed from a variety of raw materials and require small amounts of energy to be produced. In the particleboard production process, forming machines play an important role as the key equipment for achieving continuous production. In recent years, airflow forming machines have received increasing attention in particleboard production lines because of their strong separation ability and low price. However, the internal flow field is complex and difficult to control, which affects the surface quality and strength of the particleboard. The most pressing technical difficulty is controlling the flow field characteristics of the airflow paver. At present, the research on this subject is conducted primarily through repeated experiments, which entail long research periods and high processing costs. To reduce human and financial costs, in this study, Computational Fluid Dynamics (CFD) is employed to investigate the flow field and the gas-solid two-phase flow field coupled with particle movement of an airflow forming machine. The accuracy of the calculation model is verified by comparing characteristic point velocities obtained from experimental analysis and a simulation. The simulation results show that in practical production, the frequency of a negative pressure fan should be greater than 27 Hz. It is necessary to set the shoulder properly, and the slab smoothness can be improved by moving the shoulder back on the premise of meeting the strength requirements of the box. The distance between the shoulders of the box body should be less than 2570 mm, and particles with uniform diameter should be added to the paving box to reduce the turbulence effect, improve the quality of particle forming and provide actual particleboard production with a solid theoretical foundation.

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Prediction of mechanical properties of wood fiber insulation boards as a function of machine and process parameters by random forest

Abstract: Prediction of mechanical properties of wood fiber insulation boards as a function of machine and process parameters by random forest. Wood Science and Technology, 54(3), 703-713.

Cited by 20 publications

References 29 publications

Prediction of Mechanical Properties of Thermally Modified Wood Based on TSSA-BP Model

Prediction of Mechanical Properties of Thermally Modified Wood Based on TSSA-BP Model

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

Interaction and influence of a flow field and particleboard particles in an airflow forming machine with a coupled Euler-DPM model

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