The representation of manufacturing resources plays a fundamental role in engineering modeling scenarios. These are characterized in a number of ways by taking into account their physical properties, capabilities, roles, etc. In this context, notions like capability, process, and functionality are used in different manners, hence it is not clear how different approaches can interoperate. The aim of this work is to propose an ontology for manufacturing that represents assets involved in manufacturing operations, their characteristics and relations, as defined in a list of requirements. This contribution stems from the existing literature and, in particular, integrates recent works related to the modeling of manufacturing resources, engineering functions, and capabilities. The ontology takes advantage of DOLCE as foundational ontological framework. The relevant axioms are presented and commented on with respect to the identified requirements.

The COVID-19 crisis resulted in a sudden and dramatic change in how manufacturing environments operate. Safe distancing among workers plays a pivotal role in preventing the spread of viral diseases such as COVID-19. Although general purpose commercial products already help prevention, enforcing ad hoc distancing without manufacturing production optimization can significantly decrease the production performance throughput. In this paper, we highlight the intrinsic trade-off of two concepts: worker health preservation versus factory productivity. We first motivate the importance of safe distancing in manufacturing shop-floors by analyzing a worker mobility dataset in a manual assembly scenario, given the safe distancing public health recommendations. Then, we suggest the quantification of the relation of the two concepts through exploiting pervasive computing technologies. Furthermore, we provide an insightful recommendation on the need of a holistic methodological framework, specifically tailored for addressing the Industry 4.0 requirements, as well as the worker necessities.

This paper addresses the modeling of modular and reconfigurable transportation systems, aiming at developing tools to support the planning and control.Answer Set Programming (ASP) is employed to formalize rules modeling the characteristic of a transportation system and describing its dynamics.Then, automatic reasoning can be exploited to find solutions in different use cases, including the generation of optimal or alternative paths, the generation and validation of control sequences.The proposed methodology is applied to a reconfigurable industrial transportation system consisting of multiple linear conveyor modules with actuators enabling longitudinal and transversal movements of pallets.

This paper presents the extension to an ontology-based data model supporting the design and performance evaluation of production systems. This extension aims to link the modeling of the spatial representation of physical objects with the characterization of their states and behavior. Furthermore, the formalization of the performance history of a production system and its components is addressed to capture both the spatial and state evolution of the objects. Such history can be provided by simulation runs or gathered from a monitoring system. A test case is described and then used to show how different software tools can be integrated to support the integrated design and evaluation of a production system.