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 computer-implemented method to create a model used for fabrication of a device configured for placement in an anatomical cavity of a wearer may include first selecting training data and/or testing data by obtaining feedback and data for a plurality of devices fabricated for a plurality of subjects, wherein each of the plurality of devices are fabricated based on a three-dimensional scan of an anatomical cavity of one of the plurality of subjects. The training data set is used to train a machine learning model for transforming a three-dimensional scan of an anatomical cavity to a three-dimensional representation of a device for fabrication.

A method is described for processing moulding parameters (P.sub.i+1) for an injection moulding machine (10) obtained by CAE.

The CAE simulation generates simulation results (A.sub.i), first machine parameters (P.sub.i) are generated by electronically processing the simulation results (A.sub.i), second machine parameters (P.sub.i+1) are obtained, different from the first ones, from the execution of another moulding process for the same object; and in an electronic database (M) accessible by a user the first and second machine parameters are saved associating them in a common collection.

An information processing apparatus includes a provision unit configured to provide a screen via which a plurality of condition items indicating features of a three-dimensional object can be specified, a reception unit configured to receive, via the screen, a specification of a condition item indicating an feature of an object desired to be molded by a user, and a determination unit configured to determine settings to be used for molding of the object desired to be molded by the user, based on the received specification of the condition item, wherein the settings to be used for the molding determined by the determination unit include molding settings for molding specified with respect to a molding apparatus.

A system, process, manufacturing technique and platform are herein provided for designing and/or manufacturing orthopedic devices, custom orthotics or personalized footwear based on computerized design software adapted to adjust the scanned information into a 3D model of the device substantially ready to production, wherein the system comprises an imaging module which uses image recognition to identify different anatomic parts of the foot to allow the design of the orthotics, and a human interface that allows showing the original scan of the foot at an opaque or semitransparent manner to allow visualization of the way the foot is going to fit and be supported by the designed custom orthotic.

A system and method for customizing a machined product comprising: establishing, in a networked server, a standard product metadata repository and a customized item metadata repository; based on a type of a standard product being selected by a customer through a remote browser client, displaying via the server a 2D visualized image of the selected type of the standard product on the client, and providing one or more customized item options available for the selected type of the standard product; based on the one or more customized item options being selected by the customer through the remote browser client and for one or more post-processing item options, extracting alternative data corresponding to the selected customized items; and combining the product data of the standard product of the type selected by the customer and the extracted alternative data of the post-processing items to obtain metadata of the customized machining product.

The present disclosure provides a molding system for preparing an injection-molded article. The molding system includes a molding machine; a mold disposed on the molding machine and having a mold cavity for being filled with a molding resin; a processing module configured to generate an extension rate distribution and a shear rate distribution of the molding resin in the mold cavity based on a molding condition for the molding machine; and a controller coupled to the processing module. The processing module is configured to generate the extension rate distribution and the shear rate distribution of the molding resin based in part on consideration of a geometry variation of the mold cavity. The controller is configured to control the molding machine with the molding condition using the generated extension rate distribution and the generated shear rate distribution of the molding resin to perform an actual molding process for preparing the injection-molded article.

A system for programming a protection device for a molding machine includes a controller for actuating a plurality of molding machine actuators in an actuation sequence, each distinct actuation constituting a respective machine component actuation of an associated machine component. An HMI is operable to: present a GUI specific to a chosen machine component actuation; and for each of a plurality of other machine component actuations, define within the GUI, based on operator input, a rule specifying a state of the chosen machine component actuation relative to a state of the other machine component actuation for preventing interference between the two machine component actuations. The controller is configured, based on the rules defined within the GUI, to trigger an action, upon violation of any one of the rules, for reducing a risk of interference between the chosen machine component actuation and a respective one of the other machine component actuations.

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.

An optimization method based on feature extraction and machine learning is provided. At least one input parameter is received. Multiple first historical mold data are retrieved. A similarity calculation is performed according to the input parameter and the first historical mold data. Multiple candidate mold data are selected according to the similarity calculation. The mold design parameters of the candidate mold data corresponding to each input parameter are replaced by the input parameter, and multiple first representative mold data for performing a first simulation analysis are generated. Multiple key feature parameters are found, and multiple second historical mold data are retrieved according to the multiple key feature parameters. An expected data is found, and the mold design parameters of the expected data are filtered and optimized to find multiple second representative mold data for performing a second simulation analysis. At least one set of mold production parameters is generated.