DEIS - Università di Bologna - L I A - Laboratorio d'Informatica Avanzata

Brite-EuRam II Programme
CRAFT action


Paint manufactoring process optimization for the building industry, using original bases and pigments formulation

Contract n. BRE2.CT94-1417 Proposal n. CR-1331-91

Project description DEIS tasks EC servers (Luxembourg)

Project Description

The purpose of this project is to develop the next generation of automatic paint production systems. Up to now, the most advanced way of producing paints consists in using Tinting Systems. With these systems the paint producers supply white paints to the sales points, while the pigments are added at the sales point according to customer requirements. In this way the customer can choose among an almost infinite number of colours but only among a small number of paint bases, thus reducing the stocking costs. The purpose of the next generation of paint production systems is to synthesize the white paint at the sales point by mixing basic components (modules), so that the customer can choose the color and the quality. In this way, the customer requirements will be much better satisfied and the paint producer will further reduce their stock costs, having to stock only the modules.

Eleven partners are involved in this project: four industrial proposers and seven R&D performers.

DEIS tasks

The paint production system will include a software package that supports the user in the choice of paint and eventually in the design of a new one. The task of DEIS is to develop four subsystems of this package:


Knowledge Based System

The Knowledge Based System (or Expert System) has to select, from a database of already known paints, the one which fits best the customerÔs requirements regarding the paint quality. If the paint found does not exactly satisfy the specifications of the customer, it is used as the starting point for a second phase in which a new paint is designed: the composition of the initial paint is changed until the resulting characteristics match the required ones. This second phase has to be performed by a software package which is under development by SOFT16. The system has to choose the paint by asking the customer questions about: As regards the architecture of the Expert System, two different Artificial Intelligence approaches have been investigated: the former is based on classification by learning from a set of examples, while the latter is based on constraint satisfaction techniques. In the first approach the paints in the database are considered as classes and the customer requests are satisfied by associating them to the right class. The rules used to perform the classification are automatically inferred starting from the description of the already known paints in the database. We have experimented the application of an existing system which performs learning from examples: the C4.5 system, developed by Quinlan. The other approach considered makes use of constraint satisfaction techniques. In this approach the system starts with the complete set of already known paints and successively eliminates the paints whose requirements do not fit with those asked by the customer. A prototype system has been written using the SEPIA language that extends logic programming for the treatment of constraints. The results of our experimentation have highlighted some limits of the former approach, while the latter has proved to be more interesting for our problem. This is due to the fact that the constraint satisfaction prototype has been tailored to the problem, while C4.5 is a very powerful and general system but does not fully satisfy the peculiar requirements of our problem. We thus decided to develop the constraint satisfaction approach. Its features are: We have then ported the prototype system from SEPIA to C++: SEPIA was very useful for quickly developing a prototype but it would be too expensive to incorporate it in the final system and it has been designed for UNIX systems.


Architect Advising System

Besides the problem of helping a customer to choose a single paint, another problem has emerged which consists in giving advice to an architect on how to paint a building: this involves how to prepare the support before the painting, how many layers of paint are necessary and which paint to use for each layer in order to get the desired result. This problem is currently solved by an expert with the aid of a dossier in which for each possible combination of type of support and characteristics required by the architect, there is a page with the description of the kind of paint work needed. The page is found by using an index. After having analyzed this problem with the experts from JEFCO, we decided to solve this problem by using an hypertext, because it resembles very much the way the problem is actually solved by human experts. We are currently analyzing various systems for producing and reading hypertexts.


Realistic Paint Viewer

The aim of the Realistic Paint Viewer is to show to the user how the paint will exactly look. To this purpose, we have used rendering methods to construct a realistic tri-dimensional scene with simulated painted objects. From this scene the user could appreciate the four characteristic of the paint aspect: Opacity, Gloss, Roughness, Levelling. There exists various algorithms for rendering and each of them contains some specific parameters that control the final aspect of the painted surfaces. These parameters do not directly express the physical characteristics of the paint but have to be derived from them. Therefore the Realistic Paint Viewer has to be composed by two phases: in the first the physical parameters of the paint are mapped into the visual parameters of the rendering algorithm while in the second the actual rendering is done. Of the two main algorithms for rendering, Ray Tracing and Radiosity, we have chosen the first one because, is a good trade-off between speed and quality of the image produced. The key-factor in any ray tracing algorithm is the lighting model: this is the mathematical model that describes how the light interacts with the objects. Among the many models available in literature, we have experimented two of these: Phong's model and Ward's model. Phong's model has the advantage to be particularly simple, thus not requiring too much computing time. The main drawback of Phong's equations is that they are empirical, therefore it is very hard to find the relation between the parameters of the model and the physical characteristics of the painted object obtained as results. Ward's model, instead, is a physically based model. The equations are more complex and refined than Phong's equations, and all the parameter have an exact physical meaning. Building the mapping functions, in this case, should be easier. Obviously we have to pay all this advantages in terms of rendering speed. In order to appreciate the gloss of a paint, we need background images that can be reflected on the painted objects. Actually our software uses both the algorithms described above: Phong's model is used for the background objects, in order to improve the speed of the calculation, whereas the painted object is rendered with the more accurate Ward's model. As regards the rugosity, we extended the pure ray-tracing algorithm in order to get a realistic result: we wrinkle the surface of the painted object using a special technique known as "Bump Mapping". In order to render the levelling, we had to simulate the hollows generated by the bristles of the brush.

Here is an example of the results we have achieved with our rendering software:


User Friendly Interface

It has to provide an easy interaction of the user with all the subsytems of the package. This interface will be graphic and will use the mouse and visual items such as buttons, icons, tick boxes, etc.

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