# US 2025/0094672 A1 · Optimizing method for molding process of fiber-reinforced polymer composite parts

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- Number: US 2025/0094672 A1
- Document ID: US20250094672A1
- Status: Published application
- Kind: A1
- Application number: 18/970,895
- Filed: 2024-12-06
- Published: 2025-03-20
- Earliest claimed: 2024-06-19
- Assignees: North University of China; Shanxi Zhongbei New Material Technology Co., Ltd.
- Applicants: North University of China; Shanxi Zhongbei New Material Technology Co., Ltd.
- Inventors: Yaqing LIU; Qihui CHEN; Chaoyang WU; Yong GAO; Guizhe ZHAO
- Web page: https://patentgrep.com/patent/US20250094672A1

## CPC

- G06F 30/23: using finite element methods [FEM] or finite difference methods [FDM]
- G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30)
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06F 2111/06: Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
- G06F 2111/10: Numerical modelling
- G06F 2113/22: Moulding
- G06F 2113/26: Composites
- G06F 2119/08: Thermal analysis or thermal optimisation
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

An optimizing method for a molding process of a part made of a fiber-reinforced polymer composite is provided. A finite element method is adopted to perform thermochemical analysis of a macroscopic model of the part and thermomechanics analysis of a microscopic model of the part, so as to simulate temperature gradient, curing degree, and residual stress of the part during the molding process. A genetic algorithm is executed, with simultaneous minimization of temperature gradient, molding time, and residual stress as an optimization goal and a curing process curve as a parameter variable, to achieve the simultaneous multi-objective optimization of molding process parameters, so as to obtain an optimized molding process.

## Claims

### Claim 1

An optimizing method for a molding process of a part made of a fiber-reinforced polymer composite, comprising:
(S1) establishing a Python library required for calculation; wherein the Python library comprises an Abaqus library and a genetic algorithm library, and the Abaqus library comprises an abaquesConstants library, a caeModules library, and an odbAccess library;
(S2) setting simultaneous minimization of molding time, temperature gradient, and residual stress as an optimization goal;
(S3) setting a variable interval for holding temperature, heating rate, and cooling rate among molding process parameters followed by assigning values to the holding temperature, heating rate, and cooling rate using a random function;
(S4) establishing a macroscopic thermochemical model of the part according to dimensions and molding environment of the part by using a finite element method, and assigning a material property to the macroscopic thermochemical model; analyzing evolution of temperature and curing degree of the fiber-reinforced polymer composite during the molding process of the part by using a heat transfer analysis module; rewriting the macroscopic thermochemical model into a thermochemical model function of the molding process parameters, wherein a temperature boundary is applied by transmitting curing process parameters to a first subroutine, and the curing process parameters are randomly generated; importing the first subroutine followed by job submission to output calculation results; extracting a minimum curing degree in the macroscopic thermochemical model from the calculation results; calculating a maximum temperature gradient during the molding process; and exporting temperature-time data at a center of the part;
(S5) establishing a two-dimensional representative volume element (RVE) model by using a Python script from the Python library based on a proportion of individual components and size of a fiber in the fiber-reinforced polymer composite; analyzing an elastic modulus change of a resin of the fiber-reinforced polymer composite during the molding process by using a cure hardening instantaneous linear elastic (CHILE) model; analyzing stress and strain transformations during the molding process by using a static general module; rewriting a microscopic thermomechanics model in the two-dimensional RVE model into a thermomechanics model function of the curing process parameters and simultaneously incorporating the temperature-time data exported in step (S4) as a predefined field into the two-dimensional RVE model; importing a second subroutine followed by job submission, wherein the second subroutine is an Abaqus user subroutine; and extracting the residual stress from calculation results of Abaqus;
(S6) calculating a total time of the molding process; wherein the total time comprises a heating time, a holding time, and a cooling time;
(S7) writing a dynamic penalty function, and evaluating a violation-constrained molding process parameter among the molding process parameters using the dynamic penalty function;
(S8) writing a main function of a genetic algorithm, defining a population size, the number of generations, a crossover distribution index, and a mutation distribution index; and calling the thermochemical model function, the thermomechanics model function, and the dynamic penalty function to evaluate individual fitness; and
(S9) plotting a multi-objective optimized Pareto optimal solution set according to the individual fitness, and selecting a set of molding process parameters satisfying actual needs from multiple sets of molding process parameters provided by the multi-objective optimized Pareto optimal solution set, so as to generate an optimized molding process.

### Claim 2 (depends on 1)

The optimizing method of claim 1, wherein in step (S4), the first subroutine is DISP, HETVAL, and USDFLD.

### Claim 3 (depends on 1)

The optimizing method of claim 1, wherein in step (S5), the Abaqus user subroutine is UMAT, USDFLD, and UEXPAN.

### Claim 4 (depends on 1)

The optimizing method of claim 1, wherein in step (S8), the genetic algorithm is a non-dominated sorting genetic algorithm II (NSGA-II).

### Claim 5 (depends on 1)

The optimizing method of claim 1, wherein the resin is selected from the group consisting of an epoxy resin, a furan resin, a cyanate ester resin, a silicone resin, a phenolic resin and a combination thereof.

### Claim 6 (depends on 1)

The optimizing method of claim 1, wherein the fiber is selected from the group consisting of a carbon fiber, a quartz fiber, an aramid fiber, a metal fiber and a combination thereof.

### Claim 7 (depends on 1)

The optimizing method of claim 1, wherein the molding process is a compression molding process or a resin transfer molding process.