An Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processes


In progressive stamping processes, condition monitoring primarily based on tonnage signals is of nice sensible significance. One typical fault in progressive stamping processes could be a missing part in one in every of the die stations because of malfunction of part transfer within the press. One challenging question is a way to detect the fault due to the missing half in certain die stations as such a fault typically ends up in die or press damage, but solely provides a little change within the tonnage signals. To handle this issue, this article proposes a novel automatic method monitoring technique using the recurrence plot (RP) technique. Along with the developed methodology, we have a tendency to conjointly give a close interpretation of the representative patterns in the recurrence plot. Then, the corresponding relationship between the RPs and also the tonnage signals beneath different process conditions is fully investigated. To differentiate the tonnage signals beneath traditional and faulty conditions, we tend to adopt the recurrence quantification analysis (RQA) to characterize the essential patterns in the RPs. A parameter learning algorithm is developed to set up the appropriate parameter of the RP methodology for progressive stamping processes. A true case study is provided to validate our approach, and therefore the results are compared with the present literature to demonstrate the outperformance of this proposed monitoring technique.

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