This injection molding method includes the steps of: constructing a prediction model on the basis of an input parameter including a molding condition for a molding product and an objective variable value including a quality value that quantifies a required quality of the molding product with respect to the input parameter; inferring a predictive distribution of the objective variable value with respect to the input parameter, using the prediction model; and deriving such a molding condition that satisfies the required quality of the molding product, by a Bayesian optimization method utilizing a regression model for obtaining the input parameter that yields a quality value highest in evaluation of the objective variable value as compared to an initial quality value, on the basis of the predictive distribution.

Methods, systems, and apparatus, including medium-encoded computer program products, for injection molding warp prediction include: obtaining a mold model and measured shrinkage data for at least one material, predicting an amount of warpage for a part manufactured using the mold by computational simulation of an injection molding process, where the computational simulation uses an internal residual stress model for the part that uses calibrated values for both a coefficient of thermal expansion and an elastic modulus and/or a Poisson's ratio of the at least one material, in at least one direction, for at least thermal stress due to cooling and pressure compensation during and/or after a packing phase of the injection molding process, in accordance with the measured shrinkage data for the at least one material, and providing the amount of warpage predicted for the part manufactured using the mold.

A method for operating a molding machine includes specifying a simulating domain corresponding to a genuine domain in a mold disposed on the molding machine. The method proceeds to perform a virtual molding by using a setting packing pressure profile to generate a simulated state waveform, generating a designed state waveform including an isobaric phase and an isochoric phase while taking into consideration the simulated state waveform, and obtaining an updated packing pressure profile for applying a molding pressure to a portion of the genuine domain while taking into consideration a difference between the simulated state waveform and the designed state waveform. Subsequently, the method proceeds to set the molding machine while taking into consideration the updated packing pressure profile to perform an actual molding by applying an actual molding pressure to the at least a portion of the genuine domain to prepare the molding product.

A one-time injection molding preparation method to imitate human bone samples is proposed, and a material database and a bone simulation model library are established that can effectively determine materials and the design process through the finite element method. In the polymer thermoplastic material, the filler with a higher density than the polymer material is first added, After granulation, it is mixed with a foaming agent or a filler with a lower density than the polymer thermoplastic materials. A human bone-like sample is obtained through one-time injection molding technology. This method saves experimental and labor costs and can be produced with traditional processing equipment. The sample is close to the density, hardness, and strength of real human bones, and is more consistent with the morphology and properties of real human bones than samples composed of multiple materials.

There is provided an analysis method whereby, when a proposed plan for deforming a resin molded article is created, it is possible to create a more effective proposed plan in a short time. In an deformation state analysis method of analyzing, for a resin molded article to be deformed, a deformation state of the resin molded article by optimizing an objective function under a prescribed restraint condition and a prescribed limiting condition, using a topology optimization method dividing the resin molded article into micro regions, the restraint condition is a trend of the amount of deformation of the resin molded article, the prescribed limiting condition is a contribution rate expressing an extent to which deformation of each micro region contributes to deformation of the resin molded article, and in the optimization of the objective function optimization is performed so as to minimize rigidity of the resin molded article.

A method may comprise generating a first electronic model of a workpiece to be manufactured using an injection-molding process. The first electronic model may then be analyzed and the workpiece in the first electronic model may then be selectively distorted according to the analysis. A second electronic model of the distorted workpiece may then be generated. The workpiece may then be injection-molded within a mold generated using the second electronic model. Thereafter, the workpiece may be cooled such that the injection-molded workpiece gradually assumes the shape or substantially the shape of the workpiece in the first electronic model.

A method for preparing a fiber-reinforced composite article initially performs a trial molding by a molding machine to prepare a trial composite article of a composite molding material including a polymeric material having a plurality of fibers, wherein the trial composite article has a trial fiber orientation distribution. The method further generates a predicted fiber orientation distribution fitting with the trial fiber orientation distribution, wherein the predicted fiber orientation distribution is generated by performing a first molding simulation for the trial composite article by using physical rheology parameters and physical fiber orientation parameters. The method further performs a second molding simulation for a real composite article by using the physical rheology parameters and the physical fiber orientation parameters to obtain molding conditions for the molding machine, and performs a real molding process by the molding machine by using the molding conditions to prepare the real composite article.

A method for operating a molding machine includes specifying a simulating domain corresponding to a genuine domain in a mold disposed on the molding machine. The method proceeds to perform a virtual molding by using a setting packing pressure profile to generate a simulated state waveform, generating a designed state waveform including an isobaric phase and an isochoric phase while taking into consideration the simulated state waveform, and obtaining an updated packing pressure profile for applying a molding pressure to a portion of the genuine domain while taking into consideration a difference between the simulated state waveform and the designed state waveform. Subsequently, the method proceeds to set the molding machine while taking into consideration the updated packing pressure profile to perform an actual molding by applying an actual molding pressure to the at least a portion of the genuine domain to prepare the molding product.

In order to injection mold parts at a constant flow front velocity in a mold cavity of an injection molding system, particularly where the mold cavity has a varying thickness along its length, mold modeling software is used to calculate the cross-sectional area as a function of the distance from the gate, percentage of fill, or length of the mold cavity. Based on that cross-sectional area, the mold modeling software determines an appropriate recommended ram force profile and/or melt pressure profile that would result in filling the mold cavity at a constant flow rate. An injection molding system is then operated according to the recommended ram force profile and/or melt pressure profile.

A method for controlling processes on at least one plastics-processing machine. The method comprises the following steps:-performing a simulation so as to produce at least one component with generation of simulation datasets (SD) that relate to an outline of the component and/or material properties,-determining a process image so as to operate the machine at an idealized operating point based on the simulation datasets (SD),-generating a design of experiments (DoE) matrix,-iteratively simulating the DoE matrix with computing of remaining variations of process parameters while reducing the DoE matrix and obtaining a trained process model for the machine, using which a component is able to be produced on the machine,-verifying the remaining variations of process parameters through real tests (40), in which components are produced on the machine and assessed, so as to generate a process parameter dataset (PPD) for subsequent operation of the machine at an operating point (AP), by virtue of an operator communicating interactively with a software communication robot, in particular chatbot, and the method steps comprise at least two artificial intelligences that interact