Method and apparatus for monitoring a quality of an object of a 3D-print-job series of identical objects

An apparatus for monitoring a quality of an object of a 3D-print job series of same objects, each object built from a multitude of stacked 2D-layers printed by a 3D-printer in an additive manufacturing process, the apparatus comprising:

• one or more processors configured to:
• determine a layer quality indicator of a currently printed layer of an object, wherein the layer quality indicator of the currently printed layer is calculated under same conditions during former print jobs of identical objects having a same shape and a number of layers, for which a completely manufactured object was evaluated;
• comparing the determined layer quality indicator of the currently printed layer with a predetermined lower confidence limit of the corresponding layer, the predetermined lower confidence limit being calculated depending on layer quality indicators of previously completely manufactured objects complying with predefined quality requirements;
• and
• perform a trend analysis taking into account the layer quality indicator of the currently printed layer and a subset of preceding layer quality indicators of preceding layers in a sequence of layer quality indicators for all layers of the object;
• generate an early warning signal before a value of the layer quality indicator of the currently printed layer is equal to or lower than a lower quality limit, in response to the trend analysis showing that the subset of preceding layer quality indicators is trending toward the predetermined lower confidence limit;
• in response to the early warning signal, automatically adjust a setting of the 3D-printer to raise a layer quality of subsequent layers, or stopping the additive manufacturing process before completion of the object;
• generate a warning signal, in response to the layer quality indicator of the currently printed layer having a value equal or lower than the lower quality limit or in response to layer quality indicators of subsequent layers showing a common trend towards the lower confidence limit;
• and
• in response to the warning signal, automatically adjust parameter settings of the additive manufacturing process to raise the layer quality of subsequent layers;
• determine the layer quality indicator of the currently printed layer by machine learning means.