Multidomain Simulation Model for Analysis of Geometric Variation and Productivity in Multi-Stage Assembly Systems

Abstract

Nowadays, the new era of industry 4.0 is forcing manufacturers to develop models and methods for managing the geometric variation of a final product in complex manufacturing environments, such as multistage manufacturing systems. The stream of variation model has been successfully applied to manage product geometric variation in these systems, but there is a lack of research studying its application together with the material and order flow in the system. In this work, which is focused on the production quality paradigm in a model-based system engineering context, a digital prototype is proposed to integrate productivity and part quality based on the stream of variation analysis in multistage assembly systems. The prototype was modelled and simulated with OpenModelica tool exploiting the Modelica language capabilities for multidomain simulations and its synergy with SysML. A case study is presented to validate the potential applicability of the approach. The proposed model and the results show a promising potential for future developments aligned with the production quality paradigm.

SysML4GDPSim: A SysML Profile for Modeling Geometric Deviation Propagation in Multistage Manufacturing Systems Simulation

“…This orientation has already been explored by our research group in previous works. In [14], the simulation of the material flow is enriched with geometric data to support the productivity and geometric quality analysis. These multidomain simulation models integrate different control logics and/or strategies for a more realistic analysis of the system, quantifying the quality improvement and the influence of measurement processes and control decisions on productivity indicators.…”

“…These multidomain simulation models integrate different control logics and/or strategies for a more realistic analysis of the system, quantifying the quality improvement and the influence of measurement processes and control decisions on productivity indicators. Specifically, the quality analysis proposed in [14] adopts of the stream of variation (SoV) technique [38,39], a mathematical model based on the state space formalism [40], to simulate the propagation of geometric deviations in multistage systems. However, the adoption of SoV has certain limitations, highlighted in [11].…”

“…Another substantial improvement over [14] is the use of SysML for the definition of a profile that considers the specific semantics of modeling simulation systems and supports consistency checking based on these semantics. Some previous works have addressed the development of SysML profiles for geometric modeling or tolerance analysis, such as [41][42][43].…”

“…In the manufacturing domain, there is a lack of developments adopting this approach, and it is hard to find works focused on geometric deviations and their propagation in multi-stage systems. In [14], SysML is used for the preliminary design of a multidomain simulation system that integrates the analysis of productivity and geometric quality to study how different control strategies influence performance measures. However, all the previously mentioned works use SysML as a modeling language only to define the descriptive models facilitating communication between engineers.…”

SYSML4TA: A SysML Profile for Consistent Tolerance Analysis in a Manufacturing System Case Application

“…In order to narrow this gap, [6] proposes a definition for workflows to connect simulation and design models; a similar solution is proposed in [7], where workflows are used to facilitate collaboration between system architects and analysis experts. Other work lines are explored in [8], where the use of descriptive models to support the design of simulation models is explored to solve some of the deficiencies that most languages and simulation tools present. Specifically, a Modelica simulation model is designed in SysML, taking advantage of the synergy between both languages to build simulation models for multistage manufacturing systems [8], where the logistic flow simulation is enriched with geometric deviation propagation based on the Stream of Variation (SoV) model [9].…”