Abstract
The monitoring of the performance of heat treatment equipment has been the subject of a number of studies. This paper proposes and explores a new study on the models—and the monitoring thereof—for predicting the energy intensity of low-pressure carburisation processes using the DeepCaseMaster Evolution soaking furnace. For research purposes, 18 carburising experiments were performed with different carbon layers, at different input parameters, such as the number of cycles, time, temperature and average carburising pressure. Based on the research experiments conducted and statistical analysis, the influence of individual parameters on the energy consumption of the pump and heating systems was determined. Moreover, the models were verified on real data of low-pressure carburising processes. The innovativeness of the proposed solution is a combination of two areas: (1) defining and measurement of the parameters of the low-pressure carburising process; and (2) predicting the energy consumption of low-pressure carburising processes using correlation and regression analyses. The possibilities of using the results of this research in practice are demonstrated convincingly.
Highlights
The data were carefully examined for linearity, equality of variance and normality
18 carburising experiments were performed with different carbon layers, at different input parameters, such as the number of cycles, time, temperature and average carburising pressure, in order to build the predictive model of energy consumption
The energy consumption of the heating system does not depend to any great extent on the heating temperature
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The implementation of predictive methods of maintenance into industrial practice is one of the key assumptions of Industry 4.0. Production resources should be integrated with systems for diagnosing possible emergency situations. This approach allows losses associated with damaged products, due to the occurrence of failures and unplanned downtime, to be reduced and reduces the cost of maintaining specialist, technical personnel, such as automatics and mechanics. Systems for monitoring the performance of complex technical systems are important when they are located in regions where access to qualified, technical personnel is especially limited
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