Thermomechanical surface instability at the origin of surface fissure patterns on heated circular MDF samples

TL;DR

This study investigates thermomechanical surface instability as the origin of crack patterns on heated MDF samples, demonstrating that different heat fluxes produce distinct crack patterns explained by a 3-D instability model.

Contribution

The paper introduces a new experimental analysis of circular MDF samples under radiant heat and validates a 3-D thermomechanical model to predict crack patterns, extending previous work on square samples.

Findings

Different crack patterns observed at 20 and 50 kW/m2 heat fluxes.

Qualitative agreement between observed patterns and model predictions.

Model can be refined for better quantitative accuracy.

Abstract

When a flat sample of medium density fibreboard (MDF) is exposed to radiant heat in an inert atmosphere, primary crack patterns suddenly start to appear over the entire surface before pyrolysis and any charring occurs. Contrary to common belief that crack formation is due to drying and shrinkage, it was demonstrated for square samples that this results from thermomechanical instability. In the present paper, new experimental data are presented for circular samples of the same MDF material. The sample was exposed to radiant heating at 20 or 50 kW/m2, and completely different crack patterns with independent Eigenmodes were observed at the two heat fluxes. We show that the two patterns can be reproduced with a full 3-D thermomechanical surface instability model of a hot layer adhered to an elastic colder foundation in an axisymmetric domain. Analytical and numerical solutions of a simplified 2-D formulation of the same problem provide excellent qualitative agreement between observed and calculated patterns. Previous data for square samples together with the results reported in the present paper for circular samples confirm the validity of the model for qualitative predictions, and indicate that further refinements can be made to improve its quantitative predictive capability.