A 3D printing method adapted to a 3D printing apparatus is provided. The 3D printing apparatus is configured to edit a plurality of sliced images, and execute a 3D printing operation according to the edited sliced images. The 3D printing method includes: analyzing a plurality of sliced objects of the sliced images, so as to draw a plurality of sliced object casings according to individual contours of the sliced objects, where the sliced object casings respectively include a part of the sliced objects; and respectively deleting the other parts of the sliced objects outside the sliced object casings, and integrating the sliced object casings of the sliced images to obtain a 3D model casing. Moreover, the 3D printing apparatus applying the 3D printing method is also provided.

Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.

The invention provides a sliced image processing method including following steps: analyzing a sliced object in a sliced image to determine whether the sliced object has a first contour line segment and a nearest second contour line segment, where the second contour line segment is located within a region encircled by the first contour line segment; determining whether vector directions of the first contour line segment and the second contour line segment are opposite when the sliced object has the first contour line segment and the second contour line segment, and correcting the vector direction of at least one of the first contour line segment and the second contour line segment when the vector directions of the first contour line segment and the second contour line segment are not opposite.

The present invention relates to a system and a method for optimizing printing parameters, such as slicing parameters and tool path instructions, for additive manufacturing. The present invention comprises a property analysis module that predicts and analyses properties of a filament object model, representing a constructed 3D object. The filament object model is generated based on the tool path instructions and user specified object properties. Analysis includes comparing the predicted filament object model properties with the user specified property requirements; and further modifying the printing parameters in order to meet the user specified property requirements.

The present invention relates to a system and a method for optimizing printing parameters, such as slicing parameters and tool path instructions, for additive manufacturing. The present invention comprises a property analysis module that predicts and analyses properties of a filament object model, representing a constructed 3D object. The filament object model is generated based on the tool path instructions and user specified object properties. Analysis includes comparing the predicted filament object model properties with the user specified property requirements; and further modifying the printing parameters in order to meet the user specified property requirements.

The method for digitally designing and manufacturing made-to-measure packaging for an object includes recognizing the object; automatically determining the family of objects to which the object belongs; automatically determining a sub-family to which the object belongs according to the morphology of the object; evaluating the dimensions of the object; determining the packaging model of which the internal volumetric space is capable of containing the object; identifying, locating, defining and quantifying the preferred wedging areas; creating a 3D digital design of the packaging and its preferred wedging areas; digitally decomposing the packaging and its preferred wedging areas by creating different complementary elementary layers by means of digital slicing; reproducing the different layers by cutting a suitable material in sheet form in order to obtain strata, and subsequently stacking and/or juxtaposing, positioning, assembling and securing the different strata to form the packaging.

Process control parameters are predicted to fabricate an object using deposition. An input design geometry is provided for the object. A training data set includes past post-build physical inspection data for a plurality of objects that comprise at least one object that is different from the object to be physically fabricated; and training data generated through a repetitive process of randomly choosing values for each of multiple process control parameters and scoring adjustments to the multiple process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments. A machine learning algorithm is trained using the provided training data set and a predicted optimal set of the multiple process control parameters is generated for initiating and performing the deposition process to fabricate the object.

Process control parameters are predicted to fabricate an object using deposition. An input design geometry is provided for the object. A training data set includes past post-build physical inspection data for a plurality of objects that comprise at least one object that is different from the object to be physically fabricated; and training data generated through a repetitive process of randomly choosing values for each of multiple process control parameters and scoring adjustments to the multiple process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments. A machine learning algorithm is trained using the provided training data set and a predicted optimal set of the multiple process control parameters is generated for initiating and performing the deposition process to fabricate the object.