An example of a fusing agent includes cesium tungsten oxide nanoparticles present in an amount ranging from about 0.01 wt% active to about 1 wt% active, based on a total weight of the fusing agent, and a liquid vehicle. The liquid vehicle includes water and a co-solvent mixture including from about 0.1 wt% active to about 10 wt% active of benzyl alcohol and from about 10 wt% active to about 40 wt% active of a co-solvent to improve miscibility between the benzyl alcohol and the water, where the wt% active of the benzyl alcohol of the co-solvent are each based on the total weight of the liquid vehicle.
B29C 64/165 - Processes of additive manufacturing using a combination of solid and fluid materials, e.g. a powder selectively bound by a liquid binder, catalyst, inhibitor or energy absorber
Examples herein track a variable of interest during a printing simulation of a model of a part. Based on the tracking of the variable of interest during the printing simulation of the model of the part, it is determined whether a performance property of the part meets a design criteria threshold. Based on whether the performance property meets the design criteria threshold, it is determined whether to modify a geometry of the model of the part. The part is caused to be printed based on whether the performance property meets the design criteria threshold.
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 30/23 - Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
B29C 64/00 - Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
G06F 113/10 - Additive manufacturing, e.g. 3D printing
G06F 119/08 - Thermal analysis or thermal optimisation
G06F 119/14 - Force analysis or force optimisation, e.g. static or dynamic forces
A method is described in which a thermal signature corresponding to a warm-up stage of a 3D printer is identified; an encoding vector corresponding to the thermal signature is utilized to predict part defect for each part in a calibration bucket at each configuration setting of a plurality of configuration settings (e.g., an irradiance level of a plurality of irradiances levels) to apply to the calibration bucket; and, based on the predicting, a configuration setting of a plurality of configuration settings (e.g., an irradiance level of the plurality of irradiance levels) is recommended.
Aspects herein provide for predicting a physical characteristic of a potential green part, based upon a part design file, via a machine-learning model. A machine learning model is trained to output a green-part-quality vector based on a plurality of build vectors, representing values corresponding to a part design file and a set of manufacturing parameters relating to manufacturing a green part based on the part design file on a three dimensional (3D) printer. The machine-learning model receives a new build vector based on a new part file design and outputs a new green-part-quality vector representing one or more predicted physical characteristic of a potential green part, based on the new part design file.
G06F 30/17 - Mechanical parametric or variational design
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 113/10 - Additive manufacturing, e.g. 3D printing
G06F 119/08 - Thermal analysis or thermal optimisation
5.
GENERATING A BUILD BED LAYOUT FOR ACCEPTABLE GREEN PART QUALITY
A method is described in which a build bed layout is generated; a green part quality vector is determined for a part of the build bed layout; and, based on the green part quality vector meeting a first threshold, a recommended part layout is provided.
G06F 30/17 - Mechanical parametric or variational design
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
G06F 113/10 - Additive manufacturing, e.g. 3D printing
G06F 111/06 - Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Systems and methods for coolant flow layer exchange-based heat management are provided. In one example, a conductive heat transfer apparatus (110) comprises a housing (112) that includes a first surface (113) and a second surface (114), and an interior pathway (115) defining a fluid flow path through the housing (112) and positioned between the first surface (113) and the second surface (114). The interior pathway (115) includes a three-dimensional flow layer inverting lattice (130) comprising a first inlet (131) oriented to receive a first flow layer (124) from the interior pathway and a second inlet (133) oriented to receive a second flow layer (126) from the interior pathway, and a plurality of passages forming a first fluid channel (132) and a second fluid channel (134) distinct from the first fluid channel (132), wherein the first fluid channel (132) and the second fluid channel (134) exchange a relative position of the first flow layer (124) and the second flow layer (126) with respect to the first surface (113) and the second surface (114).
Aspects herein provide for predicting quality of physical characteristics of a sintered part via a machine-learning model. A physical characteristic of a green part is measured and a physical characteristic of a sintered part formed from the green part is measured. A training a machine-learning model is trained using a green-state vector for the green part that is measured and a sintered-state vector of the sintered part that is measured. The machine-learning model can generate a prediction vector for a new green part, wherein the prediction vectors predicts quality of a physical characteristic of a potential sintered part to be formed from the new green part.
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