A prediction method for mold breakout based on feature vectors and hierarchical clustering is disclosed, which comprises: respectively extracting temperature feature vectors of historical data under sticking breakout and normal conditions and on-line actually measured data to establish a feature vector sample set; performing normalization and hierarchical clustering on the sample set; and checking and judging whether the feature vectors extracted on line belong to a breakout cluster, and then identifying and predicting mold breakout. The method avoids the steps of tedious adjustment and modification of alarm threshold and other parameters, overcomes the artificial dependence of the previous breakout prediction method, has good robustness and mobility; and through temperature feature extraction, achieves accurate identification of sticking breakout temperature patterns, avoids missing alarms and significantly reduces the number of times of false alarms, and greatly reduces the data calculation amount and calculation time, guaranteeing the timeliness of on-line prediction.

The invention relates to a method for computationally designing a number of re-usable two-pieced molds for the reproduction of an object, wherein each mold is fillable with filling material, in particular resin, to form the object or a part of the object to be reproduced, wherein each mold consists of rigid material.

An information processing apparatus includes a first extraction unit and a second extraction unit. The first extraction unit extracts a pouch-shaped portion from drawing information on a mold. The second extraction unit extracts a site from which heat is not easily released from the pouch-shaped portion.

The present disclosure provides a method of producing a molded product. The method comprises steps of performing, via computer-assisted engineering simulation software, a first simulation process to generate a plurality of molding conditions comprising a default injection velocity profile and a default packing pressure profile; conducting, via an injection-molding apparatus, a trial molding to inject a molding material into a mold using the default molding conditions and sensing a plurality of in-mold pressures at different sites in a mold cavity of the mold; and conducting, via an injection-molding apparatus, an actual molding to produce the molded product using the default molding conditions if a deviation of the in-mold pressures at an endpoint of a packing stage is less than a target value.

The present invention is directed to systems and methods for automatically generating mechanical part designs and manufacturing specifications/instructions that account for geometric distortions that may occur during manufacturing or post-processing.

In order to provide an efficient and favorable simulation method for obtaining the disappearance of a number of air bubbles in one entire shot or the like, for example, there is provided a simulation method for simulating a film forming process of forming a film by coating an upper part of a substrate with a curable composition in a form of a plurality of droplets and bringing a mold into contact therewith. The simulation method includes a first step of computing a size of each air bubble at a time when air bubbles become trapped due to the mold brought into contact therewith and a time of becoming trapped, and a second step of obtaining a disappearing process of each of the air bubbles on the basis of information of the size of each of the air bubbles and the time.

A method of setting a shaping machine includes identifying values for setting parameters, which setting parameters at least partially establish control of controllable components of the shaping machine during the shaping process. A plurality of simulations of the shaping process are performed on the basis of a first parameter and a second parameter. The first parameter describes physical factors of the shaping process. The second parameter is suitable as a basis for at least one of the setting parameters of the shaping machine. The simulation is carried out on the basis of various combinations of values of the first parameter and the second parameter. Values of at least one quality parameter are calculated from results of the simulations for the various combinations of values of the first parameter and the second parameter.

An inspection method using an automatic inspection system comprises the following: (a) a step of loading a 3D file from which the program has been selected; (b) a step of inputting into the program information on one or more selected components of the mold assembly of the 3D file loaded into the program; (c) a step of checking a setting value of the selected component according to a mode selected from a check list preset in the program; and (d) a step wherein, if the set value of the component selected in step (c) is out of the range of the value set in the selected mode, the color of the component out of the range of the set value among the selected one or more components is changed to a preset color and monitored.

A method for geometric modelling method performed by a data processing system on a geometric model including a kernel and associated applications, the method includes receiving data for an object to be processed by the kernel, generating a standalone object for a user interface application of the geometric model, and storing the standalone object. A data processing system is configured for modelling a product according to the method.

A prediction method for mold breakout based on feature vectors and hierarchical clustering is disclosed, which comprises: respectively extracting temperature feature vectors of historical data under sticking breakout and normal conditions and on-line actually measured data to establish a feature vector sample set; performing normalization and hierarchical clustering on the sample set; and checking and judging whether the feature vectors extracted on line belong to a breakout cluster, and then identifying and predicting mold breakout. The method avoids the steps of tedious adjustment and modification of alarm threshold and other parameters, overcomes the artificial dependence of the previous breakout prediction method, has good robustness and mobility; and through temperature feature extraction, achieves accurate identification of sticking breakout temperature patterns, avoids missing alarms and significantly reduces the number of times of false alarms, and greatly reduces the data calculation amount and calculation time, guaranteeing the timeliness of on-line prediction.