Embodiments relate to an aligner breakage solution that tests damage to an aligner using machine learning. A method includes processing data from a digital design for an orthodontic aligner by a trained machine learning model and outputting, by the trained machine learning model, a probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner. The method further includes making a comparison of the probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner to a probability threshold and determining whether the orthodontic aligner is a high risk orthodontic aligner based on a result of the comparison. Responsive to determining that the orthodontic aligner is a high risk orthodontic aligner, the method includes performing at least one of a) a corrective action or b) selecting a manufacturing flow for high risk orthodontic aligners.

Embodiments relate to an aligner breakage solution that tests progressive damage to an aligner. A method includes gathering a digital model representing an aligner for a dental arch of a patient, and simulating progressive damage to the aligner. Simulating progressive damage for a region of the aligner comprises simulating, using at least the digital model, a sequence of loads on the aligner, determining an amount of damage to the region of the aligner for each load, and after each simulation of a load on the aligner, updating the digital model based on the amount of damage to the region of the aligner. The method further includes determining whether a damage criterion is satisfied for at least one region of the aligner and determining whether to implement one or more corrective actions for the aligner.

A network system can optimize 3D models for 3D printing. A smoothing operation can be performed for a 3D model that comprises a plurality of voxels by identifying exterior voxels of the 3D model. For a first exterior voxel of the 3D model, an exterior surface orientation can be determined and a smoothing operation can be performed based on the determined exterior surface orientation. The smoothing operation can include performing a triangulation operation based on the determined exterior surface orientation of the first exterior voxel. Furthermore, in response to determining that a dimension of a set of voxels is below a threshold limit, one or more voxels can be added to the set of voxels to satisfy the threshold limit.

Disclosed herein are a computer-implemented method and a design system for designing a molding process for manufacturing at least one component. The computer-implemented method includes a) retrieving three-dimensional geometrical data describing a candidate shape of a mold cavity; b) analyzing the geometrical data; c) automatically interpreting at least one analysis result generated in step b) by subjecting the analysis result to at least one target specification; and d) outputting at least one interpretation result generated in step c), the interpretation result describing at least one quality of one or both of the molding process and a part design using the candidate shape of the mold cavity.

The invention relates to changing a model geometry of an object by providing a target geometry and a model geometry for the object; using the model geometry to provide an actual geometry of the object; determining whether there is a deviation between the target geometry and the actual geometry; and changing the model geometry into a modified model geometry on the basis of the determined deviation; wherein determining results in a first non-rigid mapping, if there is a deviation, that associates two geometries with each other by using a parameter set and describing the determined at deviation, or at least the changing is carried out by a second non-rigid mapping that associates two geometries with each other by using a parameter set. Thus, correct values are provided in the event of deviations and deformation, and the corrected values reduce the effort in finding a model geometry for producing an object.

A resin behavior analysis apparatus configured to analyze behavior of a fiber when molding a sheet material of a fiber reinforced resin including a fiber bundle which is an assembly of a plurality of the fibers. The apparatus includes: a CPU and a memory connected to the CPU. The CPU is configured to perform: generating a sheet model which is a model of the sheet material; generating a fiber bundle model which is a model of the fiber bundle in the sheet model; generating a fiber model which is a model of the fiber in the fiber bundle model; and analyzing behavior of the fiber model based on a condition for molding the sheet material.

A method for designing fiber-composite parts in which part performance and manufacturing efficiency can be traded-off against one another to provide an “optimized” design for a desired use case. In some embodiments, the method involves generating an idealized fiber map, wherein the orientation of fibers throughout the prospective part align with the anticipated load conditions throughout the part, and then modifying the idealized fiber map by various fabrication constraints to generate a process-compensated preform map.

A model-based machine learning system for calculating optimum molding conditions includes a data storage device providing a set of training data; an injection molding process emulator producing a set of emulated sensing data according to molding conditions as inputted; an injection molding process state observation unit, determining an injection molding process state from molding conditions, sensing data and a quality state, wherein the quality state at least includes an acceptance state; and an injection molding process optimization unit including an injection molding condition optimizer, wherein a molding condition optimization model constructed in the injection molding condition optimizer is trained according to the injection molding process state as determined, and the molding condition optimization model after training is introduced into an injection molding production line.

A method of designing a cooling circuit inside a mold that includes therein the cooling circuit that passes through an inlet and an outlet includes a control plane setting step, a reference plane setting step, an intersection line extraction step, and a circuit setting step. The control plane setting step sets a control plane that is perpendicular to the mold surface on the side which comes in contact with a material and that passes through the inlet and the outlet. The reference plane setting step sets a reference plane that is offset by a fixed distance from the mold surface to the inside of the mold. In the intersection line extraction step, an intersection line at which the control plane and the reference plane intersect is extracted. In the circuit setting step, the cooling circuit is set inside the mold along the intersection line.

An information processing apparatus includes a processor configured to specify a design element of a product, which affects cost of the product, from three-dimensional shape data of the product and a production requirement for the product and output a cost reduction measure for the product related to the design element of the product and an amount of reduced cost of the product that is obtained in a case where the cost reduction measure for the product is executed.