A method for making dies for die casting, and relative die, includes designing moulding parts of the die as plurality of sub-inserts. Each sub-insert of the plurality is bordered by boundary lines defined on the basis of a simulation of thermo mechanical behaviour of the die in operation. The simulation is performed by a processor of a computer and the behaviour is the behaviour of the die if the die were a single piece. The method also includes producing the plurality of sub-inserts and assembling the sub-inserts of the plurality with attachment means, so as to form the die.

Deep learning approaches and systems are described to control the process of casting physical objects. A neural network, operating on one or more processors of a server or distributed computing resources and maintained in one or more data storage devices, is trained to recognize relationships between the target digital representation and the resulting metal parts that are cast, and a number of specific approaches are described herein to overcome technical issues in relation to misalignments between reference points, among others. These deep learning approaches are then used for generation of command or control signals which modify how the casting process is conducted. Command or control signals can be used to modify how a cast mold is made, to modify environmental variables, to modify manufacturing parameters, and combinations thereof.

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.

Embodiments relate to an aligner breakage solution that tests probability of aligner breakage at weak points. A method includes gathering a digital model representing an aligner for a dental arch of a patient, receiving material property information for a material to be used to manufacture the aligner, and analyzing one or more regions of the aligner. Analyzing a region of the aligner comprises simulating application of a load around the region, determining at least one of a stress, a strain or a strain energy density at the region, evaluating a strength of the aligner at the region, and determining whether the region satisfies a damage criterion based on the strength of the aligner at the region.

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.

Embodiments relate to an aligner breakage solution. A method includes obtaining a digital design of a polymeric aligner for a dental arch of a patient. The polymeric aligner is shaped to apply forces to teeth of the dental arch. The method also includes performing an analysis on the digital design of the polymeric aligner using at least one of a) a trained machine learning model, b) a numerical simulation, c) a geometry evaluator or d) a rules engine. The method may also include determining, based on the analysis, whether the digital design of the polymeric aligner includes probable points of damage, wherein for a probable point of damage there is a threshold probability that breakage, deformation, or warpage will occur. The method may also include, responsive to determining that the digital design of the polymeric aligner comprises probable points of damage, performing corrective actions based on the probable points of damage.

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.

A method for improving a hemispherical dome test includes calculating a forming limit diagram (FLD) based on a plurality of simulated data associated with a sheet metal transformation technique. The method also includes performing zero friction analysis on the sheet metal transformation technique. The method also includes shifting the FLD based on the zero friction analysis.

A device, a system and a method for modeling fiber orientation distribution related to an injection molding object are provided. The device of the system obtains an aspect ratio of a non-cylindrical fiber, and inputs the aspect ratio and fiber data of the non-cylindrical fiber into an orientation distribution generation model for outputting a non-cylindrical fiber orientation distribution. The device determines whether the non-cylindrical fiber orientation distribution corresponds to a predetermined orientation distribution.

The present disclosure provides a method for operating a molding system. The molding system includes a molding machine and a mold disposed on the molding machine, wherein the mold has a mold cavity for being filled with a molding material from the molding machine. The method comprises a step of obtaining a predetermined state waveform expressing a predetermined volumetric variation of the molding material. Next, the method further comprises obtaining a measured pressure and a measured temperature of the molding material in the mold cavity while performing a molding process for filling the molding material into the mold cavity. Next, the method includes obtaining a detected volumetric property of the molding material corresponding to the measured pressure and the measured temperature. Subsequently, the method comprises displaying the detected volumetric property of the molding material with the predetermined state waveform.