The neural network as a part of decision support system for quality management for production objects in machining process

The research discusses the use of artificial neural networks (ANN) as components of a decision support system (DSS) to automate quality control manufacturing facilities machining business at the production, which should be focused on the analysis of large amounts of heterogeneous information. The necessity to use ANN as a part of DSS is justified by the fact that quality control during production is multistage and time-consuming process that is formalized difficult, and moreover requires considerable information and material costs for the efficiency of manufacturing operations performed. Taking into account the existing experience of successful use of ANN to solve difficult formal problems associated with handling large volumes of diverse and rapidly changing information, the authors synthesized ANN for automated determination of the causes deterioration of the quality of production objects (PO) in the performance of manufacturing operations application. Particular attention is paid to the definition of the dimension of the hidden layer ANN synthesized due to the fact that today still there is no analytical expression to determine the dimension of the hidden layer ANN and size of the latter is determined only by the experimental results of ANN several different structures by comparison the results, in particular the value of mean square error.