Most of the pre-existing modeling approaches for Automatic machining line (AML) concentrate on the equipment failure, but ignore some factors greatly influencing the performance of AML in production process, like the tool life, quality abnormality etc. Considering the above complex factors, a GSPN-based modeling approach is proposed to model the AML based on the fundamental principle of generalized stochastic petri nets (GSPN), which contributes to quantitatively analyze the relations between those factors and productivity of AML. The proposed modeling approach is applied in the automatic gear hobbing and shaving line in a gearbox factory. Some factors including equipment failure, cutting tool and quality abnormality are simulated to analyze the affections on the productivity, and the results show the validity of the approach.
Production Process Object Modeling Based Petri Nets