The optimization of production processes has always been one of the cornerstones for manufacturing companies, aimed to increase their productivity, minimizing the related costs. In the Industry 4.0 era, some innovative technologies, perceived as far away until a few years ago, have become reachable by everyone. The massive introduction of these technologies directly in the factories allows interconnecting the resources (machines and humans) and the entire production chain to be kept under control, thanks to the collection and the analyses of real production data, supporting the decision making process. This article aims to propose a methodological framework that, thanks to the use of Industrial Internet of Things—IoT devices, in particular the wearable sensors, and simulation tools, supports the analyses of production line performance parameters, by considering both experimental and numerical data, allowing a continuous monitoring of the line balancing and performance at varying of the production demand. A case study, regarding a manual task of a real manufacturing production line, is presented to demonstrate the applicability and the effectiveness of the proposed procedure.

Fiber-reinforced composite materials have found increasing industrial applications in the last decades, especially in the aerospace and ground transport fields, due to their high specific strength. However, even though this property allows having a lightweight and strong structure, some critical aspects still limit their use. Most of these depend on several types of undetectable defects and damages, which could affect the residual strength of the composite structures. The paper deals with failure mechanisms involving composite materials under several critic loading conditions, with the aim to assess the limits of currently used failure criteria for composite materials and to show the actual request of developing new failure criteria in order to increase the effectiveness of current design practices. Nowadays, such design practice is based on a damage tolerance philosophy, which allows a structure to tolerate the presence of damages during its in service life, only if the residual strength is kept higher than specific threshold values depending on the damage severity. Hence, the main goal of such paper is to assess the failure criteria’s capability to predict the life of composite components under several quasi-static and dynamic loading conditions. Intra-laminar and inter-laminar failure criteria have been investigated and considerations have been provided about the possibility to model the post-failure phase and to implement them within numerical predictive tools, based on finite element method.

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.

According to the international literature postures, exerted forces, manual handling and repetitive actions with upper limbs must be considered in order to estimate the workers’ exposure to biomechanical overload risk, but also a preventive ergonomic approach in the design phase is possible. Within the Industry 4.0, the digitalisation of manufacturing processes generate benefits in terms of production costs and time. Regarding the ergonomics, it is possible to set up a predictive model for the evaluation of biomechanical overload risk. This paper proposes an appraisal of a workplace design and ergonomics validation procedure based on simulation: data from assembly tasks simulation of Digital Human Models (DHM) can be used to assess the ergonomic indexes (OWAS, NIOSH, OCRA, EAWS, etc.). So, it is possible to preventively solve ergonomic risks during the design phase. A test case, regarding a real workplace of an assembly line of an important automotive Company, is also presented.

The aim of this work is to propose a numerical methodology based on the finite element (FE) method to investigate the dispersive behavior of guided waves transmitted, converted, and reflected by reinforced aluminum and composite structures, highlighting their differences. The dispersion curves of such modes can help designers in improving the damage detection sensitivity of Lamb wave based structural health monitoring (SHM) systems. A preliminary phase has been carried out to assess the reliability of the modelling technique. The accuracy of the results has been demonstrated for aluminum and composite flat panels by comparing them against experimental tests and semi-analytical data, respectively. Since the good agreement, the FE method has been used to analyze the phenomena of dispersion, scattering, and mode conversion in aluminum and composite panels characterized by a structural discontinuity, as a stiffener. The research activity allowed emphasizing modes conversion at the stiffener, offering new observations with respect to state of the art. Converted modes propagate with a slightly slower speed than the incident ones. Reflected waves, instead, have been found to travel with the same velocity of the incident ones. Moreover, waves reflected in the composite stiffened plate appeared different from those that occurred in the aluminum one for the aspects herein discussed.