Abstract

The concept known as the fourth industrial revolution (Industry 4.0) has developed a wide field of study, such as the Internet of Things (IoT), Cyber-Physical Systems (CPS) and Information and Communication Technology (ICT), with the objective to achieve enhancing the processes performance of automated systems. This work focuses on the field of CPS for an in situ consolidation machine, which handles pre-impregnated, in this case unidirectional carbon fiber tapes with polyamide 6, in order to create composite laminates. The physical model involves an unwinding system that feeds the tape into a turning table through a pressing mechanism, which has a radiation heater before and refrigeration after the presser to guarantee the composite temperature. The objective is to model and simulate the heating process before, during and after the pressing mechanism to feed an optimization algorithm in order to calculate the optimal working parameters such as heaters power, heater positioning and process speed taking into account the temperatures of the tape, to avoid overheating and to improve process time. The results are processed, so that they can send and receive data constantly, to an optimization system to alter parameters, without making programing code changes and avoiding the need to stop the consolidation. The CPS can handle different classes of pre-impregnated composites, by learning during the consolidation process, in order to improve the overall result, without the need of fully or partially reprograming the code, which a main characteristic of CPS.

Abstract

Abstract

Additive Manufacturing (AM) has been a subject of significant attention from both industrial manufacturers and research communities. However, several challenges hinder the widespread implementation of this technology in the industry. Powder recoating is a crucial step in powder-bed AM process that involves achieving a uniformly packed bed of powder particles that are later melted by an energy source, such as a laser or electron beam. One of the main challenges is calibrating the contact model parameters accurately to match the flowability and spreadability of specific powder alloys. This paper proposes a Discrete Element Method (DEM) model calibration framework based on surrogate model optimisation. The study utilises a Revolution Powder Analyser (RPA) as the experimental reference system. The proposed method is demonstrated with two AM powder samples, Ti64 and Inconel 718. The results indicate that particle-particle friction, rolling resistance, and van der Waals (vdW) surface energy significantly affect the system responses. Furthermore, the validation results show good correspondence between the simulation with calibrated parameters and experimental data. Overall, proposed calibration framework has the potential to optimise powder recoating and to improve the accuracy and effectiveness of the additive manufacturing.