This research work, first, briefly describes the educational offer of University of Calabria (UNICAL) related to Modeling & Simulation (M&S) and, secondly, presents a case study, developed by UNICAL students, as attempt to transfer the theoretical knowledge on M&S, gained over the university studies, into a real industrial application. As case study, the development of a new racing car front wing, from the mechanical design to the manufacturing process design and optimization, is presented. The manufacturing process design and optimization has been achieved by proposing an approach based on the combination of M&S tools, multiple design parameters and multiple performance measures. In particular, a simulation model has been used to assess the effect of different design parameters on several performance measure. The quantitative evaluation of the effects of the multiple design parameters on the multiple performance measures is achieved by using the Design of Experiments (DOE) being validated by means of the analysis of variance (ANOVA).
Modeling & Simulation as Industry 4.0 enabling technology to support manufacturing process design: a real industrial application
Cimino, Antonio;Gnoni, Maria Grazia;
2023-01-01
Abstract
This research work, first, briefly describes the educational offer of University of Calabria (UNICAL) related to Modeling & Simulation (M&S) and, secondly, presents a case study, developed by UNICAL students, as attempt to transfer the theoretical knowledge on M&S, gained over the university studies, into a real industrial application. As case study, the development of a new racing car front wing, from the mechanical design to the manufacturing process design and optimization, is presented. The manufacturing process design and optimization has been achieved by proposing an approach based on the combination of M&S tools, multiple design parameters and multiple performance measures. In particular, a simulation model has been used to assess the effect of different design parameters on several performance measure. The quantitative evaluation of the effects of the multiple design parameters on the multiple performance measures is achieved by using the Design of Experiments (DOE) being validated by means of the analysis of variance (ANOVA).File | Dimensione | Formato | |
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