Integration of Physical and Probabilistic Measures in Stochastic Measurements of Manufacturing Processes

Deterministic and probabilistic models of measured quantities, processes and fields in production process control systems, as well as physical and probabilistic measures, make it possible to form the measurement result and give it the properties of objectivity and reliability. The issue of improving and developing models and measures in measurement methodology plays an increasingly important role in achieving high measurement accuracy in control systems and the reliability of decision-making by expert systems in production processes. The measure is formed by many factors, most of which are random in nature. The stochastic approach in measurement theory is of particular importance in the measurement of physical quantities that are probabilistic in nature and in the construction of decision rules for expert systems. Probabilistic measures play a key role both in the process of measuring physical quantities and in the construction of decision rules when using a stochastic approach. The main idea of the article is to show the peculiarities of the transition from the well-known triad “model → algorithm → program” to a more meaningful methodology “model → measures → algorithm → program” and to show an example of using this approach. The methodology allows to increase the accuracy and reliability of the results obtained from measuring control systems and decision-making by expert systems of production processes. Examples of the use of this approach are considered in this work.