In blown powder additive manufacturing technologies the geometrical stability of the built parts is more complex with respect to more conventional powder bed processes. Because of this reason, in order to select the most suitable building parameters, it is important to investigate the shape and the properties of the single metal bead formation and the effect that a scan track has on the nearby ones. In the present study, a methodology to identify an appropriate laser metal deposition process window was introduced, and the effect of the building parameters on the geometry of circular steel samples was investigated. The effect of the scanning strategy on the deposited part was also investigated. This work draws the attention to the importance of the obtainment of the most suitable melt pool shape, demonstrating that the laser power and the scanning strategy have a strong influence not only on the shape but also on the mechanical properties of the final component.

In the last years, powder-based Laser Metal Deposition (LMD) has been attracting attention as a disruptive Additive Manufacturing (AM) technique for both the fabrication and restoration of Inconel 718 components, enabling to overcome current limitations faced by conventional manufacturing processes in terms of manufacturing costs, tool wear, and lead time. Nevertheless, the uncertainty related to the final mechanical performance of the as-built LMD parts limits a wider adoption of such technology at industrial level. This research work focuses on the mechanical characterization of as-built Inconel 718 specimens through split Hopkinson tensile bar tests performed at different strain rate conditions. The influence of laser power on the final mechanical behavior of the as-built tensile samples is discussed and compared with the mechanical response of as-cast ones. The as-built specimens exhibit a high internal density (i.e., 99.92% and 99.90% for 300 W and 400 W, respectively) and a more ductile behavior compared to the as-cast ones for every evaluated strain rate condition. The strain hardening capacity of the as-built samples increases with the laser power involved in the LMD process, reaching an average Yield Strength of 703 MPa for specimens realized at 400 W and tested at 800/s.

Directed energy deposition is an additive manufacturing technology which usually relies on prototype machines or hybrid systems, assembled with parts from different producers. Because of this lack of standardization, the optimization of the process parameters is often a mandatory step in order to develop an efficient building process. Although, this preliminary phase is usually expensive both in terms of time and cost. The single scan approach allows to drastically reduce deposition time and material usage, as in fact only a stripe per parameters combination is deposited. These specimens can then be investigated, for example in terms of geometrical features (e.g. growth, width) and microstructure to assess the most suitable process window. In this work, Ti-6Al-4V single scans, produced by means of directed energy deposition, corresponding to a total of 50 different parameters combinations, were analyzed, focusing on several geometrical features and relative parameters correlations. Moreover, considering the susceptibility of the material to oxygen pick-up, the necessity of an additional shielding gas system was also evaluated, by comparing the specimens obtained with and without using a supplementary argon flow. A process window, which varies according to the user needs, was found together with a relationship between microstructure and process parameters, in both shielding scenarios.Graphic

In order to improve the process efficiency of a direct energy deposition (DED) system, closed loop control systems can be considered for monitoring the deposition and melting processes and adjusting the process parameters in real-time. In this paper, the monitoring of a new deposition nozzle solution for DED systems is approached through a simulation-experimental comparison. The shape of the powder flow at the exit of the nozzle outlet and the spread of the powder particles on the deposition plane are analyzed through 2D images of the powder flow obtained by monitoring the powder depositions with a high-speed camera. These experimental results are then compared with data obtained through a Computational Fluid Dynamics model. Preliminary tests are carried out by varying powder, carrier, and shielding mass flow, demonstrating that the last parameter has a significant influence on the powder distribution and powder flow geometry.

The reconfigurability feature represents an instrumental characteristic for manufacturing systems that are required to frequently adapt the architecture and functionalities to match evolving production environment where changes of product variants and demand volumes frequently occur. Transportation systems embrace a major industrial application of the reconfiguration concept. Reconfigurable transportation systems (RTSs) are conceived as multiple independent modules to implement alternative inbound logistic systems’ configurations. Together with mechatronic interfaces and distributed control solutions, the full exploitation of reconfigurability strategies for transportation systems relies upon flexible production management policies. This enables the dynamic computation of part routings in RTSs after every reconfiguration and change in the way transportation modules are exploited. The current work proposes an innovative agent-based algorithm that combines global and local optimisation criteria to manage the part flow in RTS. The proposed approach is designed as fully distributed across transportation modules; based on current RTS’ topology and status, it ensures the autonomy in selecting routing decisions while embracing global and local evolving optimisation strategies. The benefits of the approach have been investigated with reference to a set of realistic RTS topologies exhibiting different routing options, in order to assess the algorithm under different part-routing conditions.

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.