Modeling of the moving induction heating used as secondary heat source in weld-based additive manufacturing

Modelling of additive manufacturing processes: a review and classification

“…Droplet kinematics and flow phenomena have been modelled in [102,103] using analytical and in [87,98,[104][105][106][107][108][109][110][111][112][113] using numerical approaches. Finally, heat transfer related KPIs have been modelled either simultaneously with other KPIs (in [114] analytically, in [85,86] analytically-numerically and in [32,41,49,50,87,[90][91][92]94,98,106,107,112] numerically) or exclusively ( [115][116][117][118] numerically).…”

“…This is due to the crisscrossing manner used by the material extruder in order to deposit the filament [137]. Thermal modelling issues concerning the material properties and dimensional accuracy, as well as the improvement of other KPIs, namely, building speed and surface roughness, are the main areas that modelling [109] Droplet generating frequency Disturbance frequency [110] Solder bump characteristics, shape Solder solidification [111] Spreading and evaporation Droplet material [112] Droplet deformation and solidification, Heat transfer related in the substrate, maximum spread diameter [113] Maximum spread factor, solidification parameter Impact velocity and substrate temperature [114] Laser attenuation and powder temperatures [116] Heat transfer related, phase changes, and fluid flow in the molten pool [117] Thermal modelling, calibration of input parameters utilizing IR imaging [118] Thermal history, residual stresses Induction heating patterns Table 8. Classification of the of the modelling studies on the BJ AM process.…”

Challenges of additive manufacturing technologies from an optimisation perspective

“…For instance, residual stress analysis requires most of the time solving a multi-physics problems [124,125]. If numerical models are able to handle technological, physical, and geometrical constrains, design guidelines can be adopted by coupling the optimisation tool to decision making paradigms.…”

“…If numerical models are able to handle technological, physical, and geometrical constrains, design guidelines can be adopted by coupling the optimisation tool to decision making paradigms. Some studies show that AM process simulation is possible [125,126] but the ultimate goal would be to bridge such realistic tools with the optimisation paradigm. Recent works prove unfortunately that we are far from such ideal situation [127].…”

Model of depositing layer on cylindrical surface produced by induction-assisted laser cladding process

“…One prospective measure for suppressing high temperature gradients is inductive preheating or postheating of substrate to an appropriate temperature [6,7]. Inductive heat pretreatment exhibits many benefits such as environmental friendliness, excellent controllability and no influence on the chemical composition of heated material [8][9][10].…”