Analysis of the MDF production process by means of statistical process modelling

The production of wood-based panels is a complex process which is influence d by various inter-relations between process parameters, such as pressure, temperature, moisture content. The relationships between process parameters and panel quality characteristics could not be fully described yet. One me thod of describing such relationships is the statistical process modeling. The goal of the project presented was to develop process models for the pro duction of MDF and to evaluate the quality of the models with regard to pre diction of panel properties. The object of the study was the production of 7.8 and 19 mm thick MDF panels on a continuously operating forming and pres sing line. Most of the data were obtained from inline data loggers. Some ad ditional variables, such as fibre length, were recorded and incorporated in the data sets by hand. 49 data sets were recorded to develop process model s for the 7.8 mm panels, 43 data sets were recorded for the 19 mm panels (o bservation range). Samples of the panels studied were tested for density, i nternal bond and bending strength (MOR) as well as thickness swelling durin g 2 and 24 hours soaking in water, respectively. Univariate, linear regress ion analysis was employed to develop the process models. Process variables were selected by two statistical and two technology-based selection methods to form the model function. The evaluation of the models was performed on the basis of 10 additional data sets each, for 7.8 and 19 mm panels (predic tion range). The model-based predictions of panel properties were compared with the actual test values of the prediction range and the standard deviat ion within the observation range. Modelling results differ considerably for the two panel types and the different panel properties investigated with r egard to the number of variables in the models and the prediction accuracy. Models of satisfactory prediction quality could be developed on the basis of all four selection methods for process variables while the combination o f methods did not lead to meaningful selections of variables. Statistical p rocess models based on extensive data collections are valuable tools for se nsitivity analyses and optimisation of normal MDF production processes. The identification of weak points in the production process, i. e. extensively varying process variables, must primarily lead to technical solutions of t he respective problem and also to a continuous visualisation and control of the relevant process data. Furthermore, the collection of data needed for the process model already allows detailed process control and documentation as well as the integrated control of all panel production steps.