A zero-Poisson-ratio honeycomb structure and an interlocking assembly manufacturing method thereof are provided, and belong to the field of light structure design and manufacturing. The honeycomb structure is formed by combining a four-pointed star shaped structure and horizontal and vertical honeycomb wall arrays at star corners. The zero-Poisson-ratio honeycomb structure not only has the zero-Poisson-ratio characteristic, but also can achieve respective design of in-plane and out-of-plane mechanical properties. Meanwhile, due to the existence of the horizontal honeycomb walls and the vertical honeycomb walls, the connection of multiple honeycomb walls at angular points in the honeycomb structure is avoided. Moreover, a novel manufacturing mode is provided for the honeycomb structure in addition to prepare the honeycomb structure by utilizing a 3D printing process. The honeycomb structure can be manufactured by combining an interlocking assembly process with resin matrix composites. The performance of the honeycomb structure is further improved at the material level.

The present disclosure relates to a method of designing a spherical microstructure mold (604-614) module to be incorporated into a calendering roller (600) for generating a microstructure (108) on a planar surface (104), comprising calculating a first curvature (204) on a cross-sectional planar surface (202) for a first microstructure point of the spherical microstructure mold module, calculating a second curvature (210) of a spherical surface (102) of the spherical microstructure mold module, measuring a radius (214) of the spherical surface (102), the radius (214) being from the center of the spherical surface (102) to the first microstructure point, and determining a location of the microstructure (108) on the planar surface (104), the location being derived from the first curvature (204), the second curvature (210), and the radius (214).

An implant shredder includes a base and a cutting member. The base includes a first chamber and a second chamber intercommunicating with the first chamber. The first chamber includes an inlet. The second chamber includes an outlet. The cutting member is received in the second chamber. The cutting member is driven by a driving member to rotate. The cutting member includes a plurality of cutting edges located on a circumference of a same radius. The plurality of cutting edges is rotatably disposed adjacent to a location intercommunicating with the first chamber. An implant forming method includes creating data of an outline of an implant; producing a shaping mold based on the data; and cutting a to-be-processed object with the implant shredder, mixing the cut to-be-proceed object with a biological tissue glue to obtain a raw material, and filling the raw material into the shaping mold to form the implant.

Embodiments relate to an aligner breakage solution that tests damage to an aligner using machine learning. A method includes of training a machine learning model to predict damage to an orthodontic aligner includes gathering a training dataset comprising digital designs for a plurality of orthodontic aligners, wherein each digital design is associated with a respective orthodontic aligner of the plurality of orthodontic aligners, and wherein each digital design comprises metadata indicating whether the associated respective orthodontic aligner was damaged during manufacturing of the associated respective orthodontic aligner. The method further includes training the machine learning model using the training dataset, wherein the machine learning model is trained to process data from a digital design for an orthodontic aligner and to output a probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner.

An information processing apparatus includes a shape data acquisition unit configured to acquire shape data indicating a three-dimensional shape of a mold for forming a molded product, a mold release direction acquisition unit configured to acquire a mold release direction in separating the molded product from the mold, a processing parameter acquisition unit configured to acquire a processing parameter for processing to be applied to a surface of the mold, a calculation unit configured to calculate, based on the shape data, the mold release direction and the processing parameter, a difference between a plurality of processing parameter maps each indicating a correspondence between a position on the surface of the mold and the processing parameter, and a notification unit configured to notify information about the difference.

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 method is provided for fitting a curved surface model in a three-dimensional space to a plurality of object points in the three-dimensional space by causing a computer to perform free-form deformation (FFD). Embodiments include the steps of setting closest points corresponding to measurement points on a curved surface model, determining the movement amounts of control points by executing a least squares method to minimize the sum of squares of the distances between the closest points and the measurement points, and fitting the curved surface model to the measurement points by executing FFD based on the movement amounts.

A multi-part compression mold for forming a complex part having a desired fiber alignment, and methods therefor, are disclosed. The multi-part mold comprises at least three sections. Specific arrangements of fiber-bundle-based preforms are introduced to more than one of the mold sections of the multi-part mold, and subjected to compression molding. The arrangements of preforms, in conjunction with the multi-part mold, result in a complex part having fibers that substantially align with anticipated principle stress vectors that arise in the complex part, when in use.

Systems and methods for forming 3D plastic parts that are cost effective in low volume, have excellent fit and finish, and use many components from 2D construction are disclosed. The systems and methods involve selecting a design and modelling the design. The design comprises 2D and 3D components of plastic parts. A 3D forming buck corresponding to the 3D component is manufactured. At least one of a 2D part and the 3D forming buck may be heated. The 2D part may be loaded onto the 3D forming buck for a predefined period of time. The 3D part formed after the loading may be separated from the 3D forming buck. The 3D part is the 2D part generally having taken the shape of the 3D forming buck. The 3D part may be cooled to obtain an end product.

An information processing apparatus includes a processor configured to display a relationship object indicating a positional relationship of at least two or more measurement spots in a molded product at a position corresponding to the relationship on a three-dimensional model of the molded product.