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This paper addresses the way a simulated annealing-based fitting strategy can be enhanced by leveraging a sensitivity analysis able to characterize the impact of the variations in the parameters of a CAD model on the evolution of the deviation between the CAD model itself and the point cloud of the digitized part to be fitted. The principles underpinning the adopted fitting algorithm are briefly recalled. The applied sensitivity analysis is described together with the comparison of the resulting sensitivity evolution curves with the changes in the CAD model parameters imposed by the simulated annealing algorithm. This analysis suggests several possible improvements that are discussed. The overall approach is illustrated on the fitting of single mechanical parts but it can be directly extended to the fitting of parts’ assemblies. It is particularly interesting in the context of the Industry 4.0 to update digital twins of physical products and systems.

International audience; Digital Twin (DT) is one of the pillars of modern information technologies that plays an important role on industry's digitalization. A DT is composed of a real physical object, a virtual abstraction of the object and a bidirectional data flow between the physical and virtual components. This paper presents a DT-based tool, called TaS, to easily test and simulate IoT environments. The objective is to improve the testing methodologies in IoT systems to evaluate the possible impact of it on the physical world. We provide the conditions to test, predict errors and stress application depending on hardware, software and real world physical process. The tool is based on the DT concept in order to detect and predict failures in evolving IoT environments. In particular, the way to prepare the DT to support fault injection and cybersecurity threats is analyzed. The TaS tool is tested through an industrial case study, the Intelligent Transport System (ITS) provided by the INDRA company. Results of experiments are presented that show that our DT is closely linked to the real world.

Recent initiatives such as the Industrial IoT, or Industry 4.0, as it has been dubbed, are fundamentally reshaping the industrial landscape by promoting connected manufacturing solutions that realize a “digital thread” which connects all aspects of manufacturing including all data and operations involved in the production of goods and services. This paper focuses on Industry 4.0 technologies and how they support the emergence of highly-connected, knowledge-enabled factories, referred to as Smart Manufacturing Networks. Smart Manufacturing Networks comprise an ecosystem of connected factory sites, plants, and self-regulating machines able to customize output, and allocate resources over manufacturing clouds optimally to offer a seamless transition between the physical and digital worlds of product design and production.