Abstract
To address the problem of how to identify the best time to implement reconfiguration for the reconfigurable manufacturing system (RMS), a dynamic complexity-based RMS reconfiguration point decision method is proposed. This method first identifies factors that affect RMS dynamic complexity (including both positive and negative complexity) at the machine tool and manufacturing cell levels. Next, based on information entropy theory, a quantitative model for RMS dynamic complexity is created, which is solved via state probability analysis for processing capability and the processing function. This model is combined with cusp catastrophe theory to establish an RMS reconfiguration decision model. Both positive and negative complexity are control variables for cusp catastrophe. Cusp catastrophe’s state condition is used to identify RMS state catastrophe at the final stage of production. This catastrophe point is the RMS reconfiguration point. Finally, the case study result shows that this method can effectively identify the RMS state catastrophe moment so that system reconfiguration is implemented promptly to improve RMS’s responsiveness to the market.
Highlights
In 2010, the German government proposed the concept of Industry 4.0 in its report “High-Tech Strategy (2010)”
Based on information entropy theory, a system complexity quantitative model is created to analyze the effect of the system complexity component on the system state
That is combined with the cusp catastrophe theory to analyze system state variation under the effect of various complexity components
Summary
In 2010, the German government proposed the concept of Industry 4.0 in its report “High-Tech Strategy (2010)”. Based on information entropy theory, state probability is converted to system complexity data, which are combined with a cusp catastrophe to analyze the RMS reconfiguration decision.
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