Multiagent System to Analyse Manufacturing Process Models

Afanasyev, Alexander; Voit, Nikolay

doi:10.1142/9789813146976_0072

Cited by 5 publications

(2 citation statements)

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“…Easy-to-understand graphical representation of the production process. [14,15] Neural networks modelling Using Taguchi's Design of Experiments to determine network parameters.…”

Section: Finite Element Methods (Fem)mentioning

confidence: 99%

“…In the work of [14] a complex workflow model (CWM) was proposed in order to clearly present the production process in a graphic form. In the [15] study, an intelligent agent system was proposed for the analysis of production process models. The work of [16] presents the manufacturing process modelling approach based on Taguchi's Design of Experiments, whose aim is to identify the optimum setting of NN parameters in a multilayer perceptron (MLP) network trained with the backpropagation algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Patalas-Maliszewska,

Wiśniewski,

Topczak

et al. 2022

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In the era of smart manufacturing and Industry 4.0, the rapid development of modelling in production processes results in the implementation of new techniques, such as additive manufacturing (AM) technologies. However, large investments in the devices in the field of AM technologies require prior analysis to identify the possibilities of improving the production process flow. This paper proposes a new approach to determine and optimize the production process flow with improvements made by the AM technologies through the application of the Petri net theory. The existing production process is specified by a Petri net model and optimized by AM technology. The modified version of the system is verified and validated by the set of analytic methods safeguarding against the formal errors, deadlocks, or unreachable states. The proposed idea is illustrated by an example of a real-life production process.

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“…Easy-to-understand graphical representation of the production process. [14,15] Neural networks modelling Using Taguchi's Design of Experiments to determine network parameters.…”

Section: Finite Element Methods (Fem)mentioning

confidence: 99%