Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or 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 fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to 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 composite manufacturing system includes a lamination tool, including a lamination surface, first locating targets extending the lamination surface, and second locating targets extending the lamination surface. The system includes a computer-aided measurement system to measure the first locating targets, the second locating targets, and line profiles of the lamination surface and to generate master surface-data and master target-data. The master target-data represents target positions of the first locating targets and the second locating targets. The master surface-data represents profile positions of the line profiles. The line profiles are associated with target pairs. Each one of the target pairs includes one of the first locating targets and an opposing one of the second locating targets. The system includes a computing device to generate a master file that establishes a spatial relationship between the master target-data and the master surface-data and to associate the master file with the lamination tool.

A causal relationship model building system includes a computer which processes information for building a causal relationship model relating to a manufacturing flow of an object to be controlled. The computer builds the causal relationship model by using monitor data representing a state of each of a plurality of steps of the manufacturing flow, and quality data as a result of an inspection step, and specifies an allowable range of the monitor data so as to satisfy a target value of the quality data, by using the causal relationship model and the target value, from prediction based on a causal relationship between a plurality of pieces of the monitor data. The computer graphically displays information including the causal relationship model and the allowable range of the monitor data on a display screen.

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.

A card manufacturing system includes a locating device and a separation device. The locating device is configured to identify a position of one or more information carrying cards formed integrally with a laminate sheet and generate information corresponding to the position of the one or more information carrying cards. The separation device includes at least one singulation mechanism configured to separate each of the one or more information carrying cards from the laminate sheet. The singulation mechanism separates the one or more information cards based on the information corresponding to the position of the one or more information carrying cards.

A hybrid manufacturing apparatus utilizing both additive and subtractive manufacturing processes has a cutting mechanism, a magazine, and a deposition nozzle. A cutting mechanism engages and modifies material fed from a magazine, the finished material of this process to be positioned by a deposition nozzle. More specifically, the cutting mechanism provides a chamber supporting an inlet, an outlet, and at least one cutting head. The inlet receives fresh material, the cutting head is positioned within the chamber to create the desired contours in or on the material, and the material then exits the chamber via the outlet to be deposited according to instructions extracted from a digital model in a form of numerical control (NC) programming language. This model is natively subdivided into individual constructs each defining a set of values to guide creation of corresponding physical sections, and the placement thereof.

Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or 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 fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to 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 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.

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.

A system is provided for processing slice data representing a slice of a three-dimensional object to be generated by an additive manufacturing system. The system includes a processor to perform, when the additive manufacturing system is to generate the slice, a transformation on the slice data based on characteristic data of the additive manufacturing system, the slice data derived from three-dimensional object design data.