Using data mining methods for manufacturing process control

Abstract

The Industry 4.0 concept assumes that modern manufacturing systems generate huge amounts of data that must be collected, stored, managed and analysed. The case study is focused on predicting the manufacturing process behaviour according to production data. The paper presents the way of gaining knowledge about the future behaviour of manufacturing system by data mining predictive tasks. The proposed simulation model of the real manufacturing process was designed to obtain the data necessary for the control process. The predictions of the manufacturing process behaviour were implemented varying the input parameters using selected methods and techniques of data mining. The predicted process behaviour was verified using the simulation model.The authors analysed different methods. The neural network method was selected for deploying new data by PMML files in the final phases. The objectives of the research are to design and verify the data mining tools in order to support the manufacturing system control by aiming at improving the decisionmaking process. Based on the prediction of the goal production outcomes, the actual control strategies can be precisely modified. Then they can be used in real manufacturing system without risks.