Feature-based approaches have been profusely used in the last decades to incorporate domain-specific knowledge in the design and development of technical systems that, according to the new Concurrent Engineering approaches, involves not only the definition of the product, but also of the required manufacturing/inspection/assembly process and the corresponding production system. Although the ability of feature-based modeling to ease and integrate knowledge intensive processes has always been recognised, in practise the different feature-based modeling proposals are strongly dependent on the domain and on the development stage of the solution (conceptual, detailed, etc.). On the other hand, inspection process planning, including the design and selection of the technical system to realize the dimensional and geometrical verification of the manufactured artefacts, has been traditionally considered separately from the rest of the manufacturing process planning, and even also from the product functional specification tasks. In this work, a feature-based framework for inspection process planning, based on a similar approach to the one applied in GD&T (Geometrical Dimensioning & Tolerancing) specification, analysis and validation of product artefacts, is presented. With this work, the proposed framework and feature concept ability to model interaction components belonging to both the product and the inspection system (inspection solution) is proved. Moreover, to facilitate the Collaborative and Integrated Development of Product-Process-Resource, the Inspection Feature has been conceived as a specialization of a generic Feature previously proposed by the authors.

Tool condition monitoring (TCM) systems are key technologies for ensuring machining efficiency. Despite the large number of TCM solutions, these systems have not been implemented in industry, especially in small- and medium-sized enterprises (SMEs), mainly because of the need for invasive sensors, time-consuming deployment solutions and a lack of straightforward, scalable solutions from the laboratory. The implementation of TCM solutions for the new era of the Industry 4.0 is encouraging practitioners to look for systems based on IoT (Internet of Things) platforms with plug and play capabilities, minimum interruption time during setup and minimal experimental tests. In this paper, we propose a TCM system based on low-cost and non-invasive sensors that are plug and play devices, an IoT platform for fast deployment and a mobile app for receiving operator feedback. The system is based on a sensing node by Arduino Uno Wi-Fi that acts as an edge-computing node to extract a similarity index for tool wear classification; a machine learning node based on a BeagleBone Black board that builds the machine learning model using a Python script; and an IoT platform to provide the communication infrastructure and register all data for future analytics. Experimental results on a CNC lathe show that a logistic regression model applied on the machine learning node can provide a low-cost and straightforward solution with an accuracy of 88% in tool wear classification. The complete solution has a cost of EUR 170 and only a few hours are required for deployment. Practitioners in SMEs can find the proposed approach interesting since fast results can be obtained and more complex analysis could be easily incorporated while production continues using the operator’s feedback from the mobile app.

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.

Tolerance analysis is a key engineering task that is usually supported by domain-specific analysis models and tools that are generally not connected to the system functionality. The model-based system engineering (MBSE) approach is a potential solution to this limitation, but it has not yet been deeply explored in this type of mechanical analysis, for which some problems need to be explored. One of these issues is the capacity of languages such as SysML to describe solution principles based on active surfaces that participate in functionality and are present for tolerance analysis. Thus, this study explored the possibilities that enable SysML to represent these geometries and their mathematical relationships based on Topologically and Technologically Related Surfaces (TTRS) theory and aligned with Geometric Dimensioning and Tolerancing (GD&T) standards. Additionally, the capacity of SysML to assure the consistency of tolerance analysis models is also explored, due to the limitations identified in analysis languages like Modelica. In this context, this paper presents a SysML profile for tolerance analysis modeling (SysML4TA), containing domain-specific semantics (concepts and constraints) to assure the completeness of the analysis models and consistency between the different models considered in the integrated model of the system. Finally, a case study applied to a manufacturing context is presented to validate the capacity of SysML to solve the identified problems.