Approaches are presented in this work, which contribute to the
development of knowledge-based systems, firstly, to assess and improve the
quality of production, secondly, to determine the parameters of a control
process. These approaches can be classified as a further development of the
research in the field of artificial intelligence.The first system controls
the evaluation of the quality within a production process using a camera.
The colors in the image will be segmented with the help of an image
processing tool (black and white or color image). Each segment corresponds
to an object or sub-object in the data set (film). The properties of the
objects are presented in an object-matrix. These properties characterise
quality, which will be evaluated later.The system requires knowledge of the
existing objects in order to assign a meaning to these objects in the
evaluation process. This knowledge is provided in the form of rules. If
knowledge-based systems consist only of rules, they are called "rule-based
systems" (Rajendra & Priti, 2010).Since in this work only rules are used,
the concepts of knowledge-based system, rule-based system and expert system
are synonymous. The strategy of evaluation is based on these rules (rule
base) in the following steps:• Filtering of objects• Splitting of connected
objects • Cleanup of properties of object• Determination of the number of
existing objectsIn this way objects are located and prepared for the
evaluation-phase. A reference quality was determined by asking experts.
Based on these expert data, the rule-based system evaluates the quality of
the objects by comparison with the reference quality and proposes a plan of
correction to improve quality.The quality of the production is assessed
through the combination of image processing and knowledge-based system. The
knowledge-based system is suitable for the use in such existing production
systems in which the evaluation of quality is possible. For this purpose,
only a new appropriate evaluation strategy (rule interpreter) is required.
The performance of the developed system is shown in the first example in
the field of quality management. The rule-based system may also be used to
determine the parameters of a control process, to improve the quality of
productions.First, the parameters of a control process can be measured by
several tests. Then, the knowledge of this process is illustrated in rules
by machine learning. There are important parameters of a process
established to create a predictive system for that. These rule-based system
inverse functions for these parameters are implemented.The performance of
the developed inverse functions is tested in the second example in this
thesis.