Clay-Based Brick Porosity Estimation Using Image Processing Techniques

1 Introduction

The structural ceramic tiles and clay materials industry is an important regional industrial segment consuming an important quantity of raw materials, especially clay. Morocco is among the world’s top 20 producers and consumers of clayey building materials. The industry of ceramic tiles and bricks is most predominant in northern central Morocco. Clayey materials are plentifully used in this region; they take an important part in the national economy, with a production of about 45% for building materials (bricks, ceramic tiles and refractories).

Many factors can affect the quality of the clay-based material, for example density, porosity, pore size, permeability and mechanical strength. The effects of porosity on the physical properties of clay have been demonstrated in many studies on sintering and in development programs designed to establish production.

Physics have provided various techniques to calculate the void. For example, porosity can be estimated by computing volumetric measurements of core samples, or from geophysical logs, which calculate a property of a rock and infer its porosity, or from petrographic image analysis, which evaluates the pore size of a small sample. This field is conducted towards the measurement of porosity in clay samples, because it affords the basic concepts for understanding.

The sophisticated structures of porous materials bring important challenges to characterization techniques. In the field of computer science, due to its non-destructive and deep investigating capability, numerous projects have used imaging methods and image analysis to characterize the behaviour of porous media [7], [11], [14], [22], [31], [38]. These works mainly used computed tomography (CT) to describe and analyse some of the most important features of porous media, such as conductivity, permeability and porosity. An example of an application using images is structure quantification [13], [36], [39], where the main focus is to demonstrate the utility of industrial-quality CT scanning and density scaling for driving fractal parameters, including the ability of two-dimensional (2D) scans to represent 3D structures. The results of flow modelling and behaviour analysis inside porous media [19], [33], [38], where relative permeability is computed directly from a digitised image of the fluid phase, are in agreement with experimentally measured values. The mechanics of porous media [1], [34] are also studied via imaging using X-ray tomography, such as shear zone, curved shape, and varying degrees of dilatancy and crack opening (in time and space). The properties of 2D porosity [37] are experimentally investigated to detect the penetration of pores by introducing a new concept, homogeneous dimension minimum, which essentially exploits the difference of the greyscale values between two images at the same pixel i. Pore diameter is considered one of the challenging tasks in material characterization. She et al. [30] introduced a new parameter called effective pore diameter calculated from a scanning electron microscopy (SEM) image, the algorithm of which is based on Euclidean distance transform. Another interesting field is 3D representation and reconstruction [4], [8], [29] to identify and analyse the microstructure of materials, and direct simulation of 3D flow in the reconstructed pore space.

In this paper, we consider image-processing techniques and physical experiments together in order to measure the porosity of clay materials. The performances of some of the described methods will be demonstrated by comparing the imaging results with the experimental calculations.

In the rest of the paper, we present the physical analysis method that we have used in order to measure porosity for the clay samples. Next, we describe the imaging method we have used to estimate porosity. Later, we demonstrate the utility of imaging techniques by comparing our results with those found by physical analysis. Finally, we discuss and interpret the obtained results and provide our conclusion.

2 Physical Analysis Method

In this work, the porosity of the test pieces is determined by the method of impregnation in boiling water according to ISO 10545-3. It is calculated by following the works presented in Refs. [2], [27]. The method can be summarized in the following steps:

1. Dry the sample in an oven at 150 °C for 2 h or at 110 °C for 24 h.

2. Determine the dry mass (M_sec).

3. Put the samples in a bath of distilled or filtered water and boil them for 5 h, taking in consideration that the specimens are covered with water.

4. After boiling, leave the samples for an additional 24 h in the bath.

5. Determine the mass (M_h) of each sample suspended in water. Perform weighing by placing the specimen in a basket attached to the scale.

6. After determining the hanging mass, lightly wipe the sample with a lint-free tissue or cotton tissue to remove any excess water from the surface.

7. Determine the saturated mass (M_sat).

The measurement procedure should be performed as quickly as possible to minimize errors caused by water evaporation from the sample.

Finally, we calculate the porosity by applying Eq. (1):

In total, we have four samples of clay in three different firing temperatures: S₀ is brut clay; S₅, S₁₀ and S₁₅ are, respectively, three clay samples with 5%, 10% and 15% of organic matter. The principal objective of adding organic matter is to see its impact on porosity in terms of the firing temperature along with the high mechanical resistance at high temperatures.

3 SEM Images

Here, we describe a non-destructive process using computer vision for porosity estimation. This technique is basically used for characterizing materials such as ceramic. Its basic principle is to give a shot of electron beam to the studied surface. The atoms of the sample release secondary electrons with lower energy, which are processed by a detector that provides the surface micrograph of the sample on a control screen. To observe non-conductive ceramic samples, carbon or platinum metallization is required to make them conductive.

The scanning electron microscope used was FEI QUANTA 200 (FEI, Hillsboro, OR, USA).

4 Tests and Results

In Figure 1, we give the results of segmenting clay SEM images using the method mentioned above. In the raw (B–E), the segmented pores are coloured green, red or white.

For the 12 images, we calculate the percentage of pores existing compared to the whole surface of the image. We obtained a noteworthy result, described in Figure 2.

Figure 2:

Porosity Estimation-Based VD versus Physical Process for the Sample S₀.

Figure 3:

Porosity Estimation-Based VD versus Physical Process for the Sample S₁₀.

An important remark to take into consideration is measurement scale; the measurement from the scale bar is respected, as the instruments have been calibrated when it was commissioned.

Figure 2 shows the evolution of porosity in terms of firing temperature. By analysing the results, we can confirm that the sample’s porosity is very important towards the lowest temperatures (800 °C) for the different pieces elaborated because the sintering phase is not done well at this temperature [24].

From Figure 2, the estimation using VD is close enough to the physical analysis. We can explain the increase in porosity estimation using the imaging method according to the fact of having different types of pores (cavernous) and also fracture samples. Consequently, the SEM image shows them as pores.

Clustering and thresholding methods as seen in Figure 3 give the furthest estimation from the experimental results we can rely on. This can be explained by the fact that K-mean segments are not connected and can be scattered very widely. This effect can be reduced by using pixel coordinates as features, an approach that tends to result in large regions being broken up. The thresholding method gives a binary image with a pore/non-pore image, whereas in the SEM image a pore can possess different greyscales.

Comparing Figures 2 and 3, we can notice an important increase in porosity with the introduction of organic matter, as it increases from 13.5% for the support with 0% organic matter to 23.5% with 10% organic matter at 1000 °C. In parallel, the volume goes from 13.63% for the support with 0% organic matter to 35.94% with 15% organic matter at 1000 °C. At this point, we made sure to keep the porosity as high as possible despite the high firing temperature by adding organic matter.

For the imaging methods we have tested, VD exploitation seems to give a better estimation. The choice of segmentation method should not be done randomly but with suited processing. Recall that the main purpose of any segmentation method is to have an optimal partition based on a given criterion. This requires the definition of the criterion as well as a rule for assigning each point to the appropriate class.

As the pores have an undefined geometric shape, we calculate the pore diameter by using the minimal Feret diameter (Figure 4), which is a measure of an object size along a specified direction. In general, it can be defined as the distance between the two parallel tangents on opposite sides of the image of a randomly oriented particle [23].

Figure 4:

Illustration of Horizontal and Vertical Feret Diameters of a Particle, Fh and Fv, respectively (Wikipedia).

In Figure 5, we present the pore distribution of the three temperatures in order to see the effect of firing on the behaviour of the pore.

Figure 5:

Pore Size Distributions in Raw Clay.

Analysing the behaviour of the figure, we can notice a decrease in micropore (<2 μm) size as long as the firing temperature increases. Some of the particles begin to melt and form a new component, glass, between the others that pull them even closer. A crucial observation is the decrease of pore size from 900 °C, which is in agreement with the expected trend in refractory clay materials in which new crystalline phases develop [3], [10], [12]. The decrease in pore size strengthens the results of low porosity towards high temperature (Figures 2 and 3).

5 Conclusion

In this work, we have presented different imaging methods for porosity estimation in clay-based bricks along with physical analysis. The segmentation phase is seen to be crucial for a correct quantitative image analysis of the microstructure. Classical and direct segmentation methods may lead to an inaccurate result, whereas the hybrid method can reform the flaws of the classical method. The hybrid method is seen to be more objective, reliable and economical either in time execution or in physical analysis requirements. An example of the hybrid method we have presented is VD-based segmentation, which seems to give good results compared with other segmentation techniques for porosity estimation. From porosity estimation to pore size distribution, the imaging method can be used widely to describe clay-based materials, such as correlating the imaging porosity estimation with some other physical characteristics like mechanical strength and transport properties.