Additive manufacturing is a technology rapidly expanding on a number of industrial sectors. It provides design freedom and environmental/ecological advantages. It transforms essentially design files to fully functional products. However, it is still hampered by low productivity, poor quality and uncertainty of final part mechanical properties. The root cause of undesired effects lies in the control aspects of the process. Optimization is difficult due to limited modelling approaches. Physical phenomena associated with additive manufacturing processes are complex, including melting/solidification and vaporization, heat and mass transfer etc. The goal of the current study is to map available additive manufacturing methods based on their process mechanisms, review modelling approaches based on modelling methods and identify research gaps. Later sections of the study review implications for closed-loop control of the process.

This paper presents a methodology to calculate the Remaining Useful Life (RUL) of machinery equipment by utilising physics-based simulation models and Digital Twin concept, in order to enable predictive maintenance for manufacturing resources using Prognostics and health management (PHM) techniques. The resources and the properties of them are first modelled in a digital environment able to simulate the real machine’s behaviour. Data are gathered by machines’ controllers and external sensors to be used for the synchronous tuning of the digital models and their simulation. The outcome of the simulation is then used to assess the resource’s condition and to calculate RUL. In this way, the condition and the status of the machines can be monitored and predicted as a result from the simulation of physics-based models, without invasive techniques of common predictive maintenance solutions. A case study is presented in this paper where the proposed methodology is validated by predicting the RUL of an industrial robot.

This is a study of a Human-Robot Collaboration (HRC) framework for the execution of collaborative tasks in hybrid assembly cells. Robots and humans coexist in the same cell and share tasks according to their capabilities. An intelligent decision-making method that allows human-robot task allocation is proposed and is integrated within a Robot Operating System (ROS) framework. The proposed method enables the allocation of sequential tasks assigned to a robot and a human in separate workspaces. The focus is rather given to the human–robot coexistence for the execution of sequential tasks, in order for the automation level in manual or even hybrid assembly lines to be increased. Body gestures are the means of a human’s interaction with a robot for commanding and guiding reasons. The proposed framework is implemented into a case coming from the manual assembly lines of an automotive industry. A preliminary design of a hybrid assembly cell is presented, focusing on the assembly of a hydraulic pump by robots and humans.

Production systems empowered by digital simulation tools can be improved in a time and cost-effective approach. The enrichment of digital simulations with sensor data, can enhance their realism and improve the accuracy of their results. Hence, this study proposes an implementation of the digital twin approach as part of a wider cyber-physical system (CPS) to enable the optimisation of the planning and commissioning of human-based production processes using simulation-based approaches. This is achieved through a) sensor data fusion and motion recognition of human activities in a factory floor and b) knowledge management mechanism for capturing the implicit knowledge of the task execution. A case study from the intra-factory logistics operations in the white goods industry, demonstrates the feasibility of the proposed approach, by enriching the simulation of manual assembly operations with the operator’s knowledge in the form of spatiotemporal constraints.

The safe and reliable operations in industrial manufacturing processes play a crucial role in the economic productivity. Machining process disturbances such as collision, overload, breakdown, and tool wear tend to cause production system failures. The current study aims at investigating the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals. Tool wear prediction has been accomplished, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems. These results indicate that predictability is affected by the mean signal energy, acquired from the vibration acceleration signals.