A 3D model system is configured to validate and optimize for 3D printing an unprocessed 3D model described by an input data file. The 3D model system is configured to detect printability issues associated with the unprocessed 3D model and automatically address these issues. Such issues can include boundary edges, non-manifold geometries, structural deficiencies, etc. Upon resolving these issues, the 3D model can be optimized for 3D printing. Optimizations can include hollowing to reduce printing cost and exterior surface smoothing. The resulting validated and optimized 3D model can be converted into an output data file which can be an input to a 3D printer or a 3D printing service for printing the object depicted by the 3D model. The 3D model system can operate as a network or cloud-based service. Users are able to interact with the 3D model system using a series of web-based user interfaces.

A molding shrinkage ratio is predicted according to molding conditions set in advance in designing a mold. A machine learning device includes an input data acquiring unit that acquires input data including any molding condition including a type of resin, a type of additive, a blending ratio of the additive, a surface temperature of a mold, and a product of a holding pressure and a holding pressure time for any article molded by any injection molding machine, a label acquiring unit that acquires label data indicating a molding shrinkage ratio in a flow direction and a molding shrinkage ratio in a vertical direction perpendicular to the flow direction of a resin measured of the article molded at the molding condition, and a learning unit that executes supervised learning using the input data and the label data, and generates a learned model.

A molding system comprises a mold, a molding machine, a computing apparatus, and a controller. The computing apparatus is programmed to perform a first simulation to generate a velocity distribution and a temperature distribution of the molding material in a first portion of a simulating domain and a second simulation to generate a melting distribution of the solid decorating film in a second portion of the simulating domain, wherein the simulating domain corresponds to the mold cavity. The first molding simulation is to performed using a molding condition of the molding machine to set a boundary condition of the first portion, and the second molding simulation is performed using the velocity distribution and the temperature distribution of the molding material to set a boundary condition of the second portion.

A system for setting injection-molding conditions and a method for setting actual molding conditions of an injection-molding machine are disclosed. The system includes a computer and an injection-molding equipment. The computer is configured to simulate, via computer-aided simulation software, a virtual molding using a plurality of design parameters to generate a plurality of provisional molding conditions. The injection-molding equipment is associated with the computer and configured to perform at least one trial molding using the provisional molding conditions to obtain a plurality of intermediate molding conditions. The computer optimizes the provisional molding conditions to obtain actual molding conditions in accordance with the intermediate molding conditions.

A system for setting injection-molding conditions and a method for setting actual molding conditions of an injection-molding machine are disclosed. The system includes a computer and an injection-molding equipment. The computer is configured to simulate, via computer-aided simulation software, a virtual molding using a plurality of design parameters to generate a plurality of provisional molding conditions. The injection-molding equipment is associated with the computer and configured to perform at least one trial molding using the provisional molding conditions to obtain a plurality of intermediate molding conditions. The computer optimizes the provisional molding conditions to obtain actual molding conditions in accordance with the intermediate molding conditions.

A computer-implemented simulation method for use in a molding process by a computer process is disclosed. The method includes steps of specifying a simulating domain comprising a mold cavity and a barrel of an injection machine, wherein the barrel is configured to connect to the mold cavity; creating at least one mesh by dividing at least part of the simulating domain; specifying boundary conditions of the mesh by taking into consideration at least one motion of a screw in the barrel; and simulating a first injection-molding process of a molding material by using the boundary conditions to generate a plurality of molding conditions.

A system may include an access engine and a foam part generation engine. The access engine may be configured to access a computer-aided design (CAD) seat surface that represents a seat surface of a seat design and access seat parameters for the seat design. The foam part generation engine may be configured to construct a CAD foam part for the seat design based on the CAD seat surface and the seat parameters.

The present invention relates to an additive manufacturing method and apparatus that is configured to construct a mold in additive layers, and a three-dimensional object therein in layers equal to or thicker than the mold construction layers. Without a powder bed needed, the mold defines the geometry, dimensions and surface finish of a three-dimensional object manufactured, so that in the process an energy source or combined sources can be selected from a large group for fusion, sintering, consolidating, joining, curing, or hardening in processing different forms and types of feedstock materials to manufacture metallic, polymeric or composite objects or parts.

The present invention relates to the field of intelligent machining, in particular to a parallel control method based on multi-period differential sampling and digital twinning technologies, the method comprising the following steps of: a. detecting machining conditions of dotting machine equipment by using a multi-period differential sampling technology; b. establishing a digital twinning control model; and c. controlling a simulation model of the dotting machine equipment according to a detection judgment result so as to perform parallel control on the dotting machine equipment. According to the parallel control method based on multi-period differential sampling and digital twinning modelling provided by the present invention, for the digital twinning model of the dotting machine equipment, the parallel control method establishes a simulation model and a detection model of the dotting machine equipment by using a virtual-real synchronization technology; simulation dotting machine equipment operates in synchronization with the physical dotting machine equipment.

A method of forming a molding tool includes performing a computer aided engineering (CAE) analysis on a molding tool design. The CAE analysis predicts flow of injection molded material and a location of gas entrapment within a molding recess of the molding tool design. At least one venting design constraint is applied to the molding tool design as a function of the gas entrapment location. Also, a biomimetic shaped passageway for venting gas away from the gas entrapment location is selected from a plurality of biomimetic shaped passageways. A computational fluid dynamic (CFD) CAE analysis of the molding tool design with the selected biomimetic shaped passageway is performed and modifications of the selected biomimetic shaped passageway are CFD CAE analyzed until a final biomimetic shaped passageway is determined and a molding tool with the final biomimetic shaped passageway is formed.