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Modeling manufacturing process of complex products like electronic chips is crucial to maximize the quality of the production. The Process Mining methods developed since a decade aims at modeling such manufacturing process from the timed messages contained in the database of the supervision system of this process. Such process can be complex making difficult to apply the usual Process Mining algorithms. This paper proposes to apply the TOM4L Approach (Timed Observations Mined for Learning) to model large scale manufacturing processes. A series of timed messages is considered as a sequence of class occurrences and is represented with a Markov chain from which models are deduced with an abductive reasoning. Because sequences can be very long, a notion of process phase is introduced. Sequences are cut based on the concept of class of equivalence and information obtained from an appropiate alignment between the sequences considered. A model for each phase can then be locally produced. The model of the whole manufacturing process is obtained from the concatenation of the models of the different phases. This paper presents the application of this method to model STMicroelectronics' manufacturing processes. STMicroelectronics' interest in modeling its manufacturing processes is based on the necessity to detect the discrepancies between the real processes and experts' definitions of them.

MODELING LARGE SCALE MANUFACTURING PROCESS FROM TIMED DATA - Using the TOM4L Approach and Sequence Alignment Information for Modeling STMicroelectronics’ Production Processes