Name
Title | ||
|---|---|---|
Discovering Large Scale Manufacturing Process Models From Timed Data Application To Stmicroelectronics' Production Processes |
Abstract | ||
|---|---|---|
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 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 based on a concept of class of equivalence is defined to cut the sequences so that a model of a phase can be locally produced. The model of the whole manufacturing process is then 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. |
Year | Venue | Keywords |
|---|---|---|
2010 | ICSOFT 2010: PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES, VOL 1 | Process model discovery, Temporal knowledge discovering, Markov processes, Sequence alignment |
Field | DocType | Citations |
Data mining,Computer science,Markov chain,Computer-integrated manufacturing,Equivalence (measure theory),Concatenation,Abductive reasoning,Manufacturing process,Process mining | Conference | 1 |
PageRank | References | Authors |
0.40 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pamela Viale | 1 | 3 | 1.91 |
| Nabil Benayadi | 2 | 10 | 4.11 |
| Marc Le Goc | 3 | 52 | 14.03 |
| Jacques Pinaton | 4 | 19 | 12.98 |