## 1 Introduction  
The increasing complexity of current technical systems has promoted the adoption of the Model-Based Systems Engineering (MBSE) approach, focused on developing, managing, and controlling models that capture, through different views (structural, functional, behavioral, etc. ), the concerns of the involved stakeholders. This approach has been widely applied in multiple fields of engineering, including manufacturing and production systems [1], which are the main context of our proposal. In general, MBSE methodologies handle a heterogeneous set of models due to their management of concepts from different disciplines and domains that belong to different granularity levels and are created with different computer tools and languages. From this heterogeneity, different challenges emerge, for example, those concerning the consistency and completeness of models, as well as the automated transformations between different models. Many authors argue that consistency assurance is one of today's major industrial challenges [2], both for individual models and for model integration. Although this issue has been widely studied in the software domain, in recent years, it has become more relevant in other domains nearer to production systems, where different issues have gained relevance, such as cross-domain model consistency checking [3], consistency management in automated manufacturing production systems [4], dependency modeling among mechatronic design models [5], etc. Many authors agree that the selection of the language for system modeling is one of the key decisions, according to model categorization. In this sense, two fundamental categories of formal models can be identified: descriptive and analysis models. On the one hand, descriptive models support design and integration of systems, easing communication among involved stakeholders. Clear examples of these models are UML and SysML models. On the other hand, analysis models are used to mathematically quantify indicators about expected behaviors by running the simulations. Modelica models are typical examples of analysis models, specifically executable simulation models (ESM). However, relationships between these models must be established to eliminate the gap between functional architecture modeling (mainly managed in descriptive models) and physical behavior modeling (managed in analysis models), which is especially important in the use of MBSE for mechanical development, as commented in [6]. 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]. An integrated application of the MBSE can also solve some of the deficiencies that most languages and simulation tools show. The combination of heterogeneous models requires the assurance of consistency between the different involved models. However, typical languages for mathematically based analysis models lack mechanisms to support domain-specific semantics, which are necessary to assure model consistency. For example, when focusing on the tolerance analysis models, the consistency should be validated while considering concepts related to artifact geometry and its mathematical definition, both for individual geometries and their relationships.