Abstract
The printing process is a final stage of both designing and pre-press processing of publications. Implementation of this process materializes the author’s design in the form of final-formed printing products. On the one hand, it accumulates the quality metrics (or shortcoming) of the original, on the other hand, it reflects the level and quality of the print process. Existing techniques, models and methods of printing process quality evaluation ensure the proper quality of products. However, implementation of the existing techniques is very expensive. In this paper we present the results of research aimed to the development of a priori evaluation of the quality of the printing process that determines the quality of the original product.
Within the framework of our research, this task is solved at the informational level, since forecasting process is based on the techniques, the main paradigm of which is the difference between factors (linguistic variables) in their pairwise comparison and the priority of the influence of the appropriate factor to investigated process. This approach is more universal because it provides a possibility to apply a general methodology not only for the factors given by numerical characteristics but also covers poorly formalized requirements described using natural language. In this research, we have designed the knowledge matrices and fuzzy logic equations for linguistic variables (factors) of the printing process. Moreover, we have proposed both the calculation algorithm and the technique of defuzzification process implementation in order to obtain the crisp numerical value of the integral index of the quality of the printing process implementation.
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Senkivskyy, V., Pikh, I., Senkivska, N., Hileta, I., Lytovchenko, O., Petyak, Y. (2021). Forecasting Assessment of Printing Process Quality. In: Babichev, S., Lytvynenko, V., Wójcik, W., Vyshemyrskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2020. Advances in Intelligent Systems and Computing, vol 1246. Springer, Cham. https://doi.org/10.1007/978-3-030-54215-3_30
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