Method and apparatus for additive manufacturing of a workpiece using statistical learning model

A method for training a statistical learning model, the method comprising:

• obtaining a plurality of training images that show a plurality of particulate material layers, wherein the plurality of particulate material layers shown in the plurality of training images contain a plurality of possible layer defects;
• marking the plurality of possible layer defects in the plurality of training images in order to obtain a plurality of annotated versions of the plurality of training images;
• defining a stop criterion based on the plurality of annotated versions of the plurality of training images;
• repeatedly feeding the plurality of training images to the statistical learning model as input data and repeatedly obtaining output data from the statistical learning model in response to the plurality of training images, wherein:
• the output data includes at least one defect vector representing a plurality of individual defect probabilities, and
• each individual defect probability from the plurality of individual defect probabilities represents an individual indicator as to whether or not a defined layer defect from the plurality of possible layer defects is present in a respective particulate material layer;
• and
• iteratively optimizing the statistical learning model using the plurality of training images and the stop criterion.

A method for additive manufacture of a workpiece, the method comprising:

• obtaining a dataset that defines the workpiece in a plurality of workpiece layers arranged one on top of another,
• producing a material layer having a defined surface formed from a particulate material,
• recording at least one image of the material layer and inspecting the material layer based on the at least one image in order to determine individual properties of the material layer,
• selectively solidifying the particulate material on the defined surface using a structuring tool based on the dataset and based on the individual properties of the material layer, such that a defined workpiece layer from the plurality of workpiece layers is produced from the material layer, and
• repeating the producing, the recording, and the selectively solidifying such that further defined workpiece layers from the plurality of workpiece layers are produced, wherein:
• the at least one image of the material layer is inspected using a previously trained statistical learning model,
• the previously trained statistical learning model is used to determine at least one defect vector that represents a plurality of individual defect probabilities,
• each individual defect probability from the plurality of individual defect probabilities is an individual indicator as to whether or not a defined layer defect from a plurality of possible layer defects is present in the material layer, and
• the selectively solidifying is carried out based on the at least one defect vector.

An apparatus for additive manufacture of a workpiece, the apparatus comprising:

• a memory configured to store a dataset defining the workpiece in a plurality of workpiece layers arranged one on top of another;
• a production platform;
• a layer-forming tool;
• a structuring tool;
• a camera oriented toward the production platform;
• and
• an evaluation and control unit configured to:
• produce a material layer having a defined surface from a particulate material on the production platform using the layer-forming tool,
• record at least one image of the material layer using the camera,
• feed the at least one image to a previously trained statistical learning model in order to determine at least one defect vector representing a plurality of individual defect probabilities, wherein each individual defect probability from the plurality of individual defect probabilities represents an individual indicator as to whether or not a defined layer defect from a plurality of possible layer defects is present in the respective material layer,
• in response to the plurality of individual defect probabilities indicating that no defined layer defect from the plurality of possible layer defects is present in the respective material layer, selectively solidify the particulate material on the defined surface using the structuring tool such that a defined workpiece layer from the plurality of workpiece layers is produced from the material layer, and
• in response to the plurality of individual defect probabilities indicating that at least one defined layer defect from the plurality of possible layer defects is present in the respective material layer, rework or reproduce the respective material layer.