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This paper aims to gain an understanding of the behaviour of iron ore when melted by a laser beam in a continuous manner. This fundamental knowledge is essential to further develop additive manufacturing routes such as production of low cost parts and in-situ reduction of the ore during processing.

Blown powder directed energy deposition was used as the processing method. The process was observed through high-speed imaging, and computed tomography was used to analyse the specimens.

The experimental trials give preliminary results showing potential for the processability of iron ore for additive manufacturing. A large and stable melt pool is formed in spite of the inhomogeneous material used. Single and multilayer tracks could be deposited. Although smooth and even on the surface, the single layer tracks displayed porosity. In case of multilayered tracks, delamination from the substrate material and deformation can be seen. High-speed videos of the process reveal various process phenomena such as melting of ore powder during feeding, cloud formation, melt pool size, melt flow and spatter formation.

Very little literature is available that studies the possible use of ore in additive manufacturing. Although the process studied here is not industrially useable as is, it is a step towards processing cheap unprocessed material with a laser beam.

Bulk material-handling equipment development can be accelerated and is less expensive when testing of virtual prototypes can be adopted. However, often the complexity of the interaction between particulate material and handling equipment cannot be handled by a single computational solver. This paper aims to establish a framework for the development, verification and application of a co-simulation of discrete element method (DEM) and multibody dynamics (MBD).

The two methods have been coupled in two directions, which consists of coupling the load data on the geometry from DEM to MBD and the position data from MBD to DEM. The coupling has been validated thoroughly in several scenarios, and the stability and robustness have been investigated.

All tests clearly demonstrated that the co-simulation is successful in predicting particle–equipment interaction. Examples are provided describing the effects of a coupling that is too tight, as well as a coupling that is too loose. A guideline has been developed for achieving stable and efficient co-simulations.

This framework shows how to achieve realistic co-simulations of particulate material and equipment interaction of a dynamic nature.