A method to utilize the results from a series of FEM models to develop a master derivative of compliance curve. The use of the unique master curve resolves the test data variability issue caused by fitting the compliance curve individually. The analytically derived derivative of compliance curve eliminates the needs to take the derivative of compliance and therefore the derivative computation error no longer exists. By applying the existing solution and the solution as disclosed and claimed herein to the same set of the raw test data, it is found that data scatter is significantly reduced.

A prediction device for predicting a thermodynamic equilibrium state of a target material, includes a processor, and a memory storing program instructions that cause the processor to generate training data including inputs related to predetermined design conditions and outputs related to the thermodynamic equilibrium state that may occur based on the predetermined design conditions, for a model that outputs target variables related to the thermodynamic equilibrium state based on input explanatory variables related to design conditions of the target material, perform machine learning using the training data so that an input-output relation of the model approaches an input-output relation of the training data, set explanatory variables used to predict the thermodynamic equilibrium state of the target material, and output predictive target variables, being predicted results of the thermodynamic equilibrium state, from the model, based on the explanatory variables input into the model on which the machine learning has been performed.

A material design device derives the optimal solution for a design condition satisfying a desired material property. A design condition setting unit for setting a specified range of a design condition of a material to be designed. A comprehensive prediction point generation unit generates a plurality of comprehensive prediction points within the specified range set by the design condition setting unit. A design condition-material property table stores data sets in which each point of the comprehensive prediction points is associated with a material property value calculated by inputting the comprehensive prediction points generated by the comprehensive prediction point generation unit, to a learned model. A required property setting unit sets a specified range of a required property of the material. A design condition extraction unit extracts, from the design condition-material property table, a data set satisfying the required property set by the required property setting unit.

A method of planning fiber paths for a composite ply of a composite layup includes determining a first unit vector field. The first unit vector field represents a first approximation of target directions to be followed by tow centerlines of the composite ply. The first unit vector field is determined based on a specified rosette direction, a surface approximation of a nonplanar contoured surface of an object to be formed, and a fiber angle distribution. The method also includes determining, based on specified angle deviation bounds and the first unit vector field, a second unit vector field. The second unit vector field represents a second approximation of the target directions. The second approximation has reduced in-plane curvature relative to the first approximation. The method further includes planning a fiber placement head path for forming the composite ply of the composite layup based on the second unit vector field.

An optimized Gamma-prime (γ′) strengthened austenitic transformation induced plasticity (TRIP) steel comprises a composition designed and processed such that the optimized γ′ strengthened austenitic TRIP steel meets property objectives comprising a yield strength of 896 MPa (130 ksi), and an austenite stability designed to have M.sub.s.sup.σ(sh)=−40° C., wherein M.sub.s.sup.σ(sh) is a temperature for shear, and wherein the property objectives are design specifications of the optimized γ′ strengthened austenitic TRIP steel. The optimized γ′ strengthened austenitic TRIP steel is Blastalloy TRIP 130.

A method for adjusting a workpiece-supporting module includes: setting initial support position information of a workpiece, the initial support position information including positions of support devices and a spacing value for separating the support devices; according to the initial support position information, applying a finite element method to analyze a CAD file of the workpiece to obtain workpiece deformation information; according to the workpiece deformation information and target workpiece deformation information, realizing support position information corresponding to each support device, the support position information including X-axis coordinates and Y-axis coordinates; according to the support position information and a conversion program, obtaining a Z-axis coordinate and a normal vector of each support devices; and, according to the support position information, the Z-axis coordinate and the normal vector, adjusting the position and the angle of each support device. In addition, a variable system for adjusting the same workpiece-supporting module is also proposed.

Examples described herein relate to apparatuses and methods for predicting strength of a coupon of composite material. A first critical damage event and strain and stress distributions of the coupon is determined by performing a finite element analysis (FEA) of a finite element model of the coupon. The first critical damage event is associated with the strain and stress distributions. The strain and stress distributions are received as inputs to at least one surrogate model. A final load corresponding to a final failure of the coupon is determined using the at least one surrogate model.

Provided is a method of designing a composite material laminated structure, the method including: a machine learning step of performing machine learning on a plurality of pieces of data each of which includes a pair of a physical property value of the composite material laminated structure and a laminate configuration of the composite material laminated structure, to obtain a relational expression depicting a relationship between the physical property value and the laminate configuration, the composite material laminated structure including a plurality of layers that are laminated; and a laminate configuration information calculation step of calculating, based on the relational expression and an objective value of the physical property value, laminate configuration information which is information of the laminate configuration that enables the objective value to be obtained.

A composite material design device using a genetic algorithm includes: a first generation generating unit that generates, as a first-generation group of individuals, a plurality of individual models using each of laminate member models having strength directionalities designed on the basis of a load condition; an evaluating unit that segments each individual model in the generated group of individuals into predetermined cells, and evaluates a lamination pattern in each cell using at least one of indices including symmetry, adjacent directionality, and continuous laminability; and a next generation generating unit that selects an individual model from the group of individuals through ranked selection, generates a new individual model through crossover, replication, and mutation, and updates the group of individuals as a next generation.

Systems and methods for actualizing simulated scarfing and patching for repair of composite laminates. A virtual environment is provided that enables engineers to optimize a repair design and provide the most robust repair solution that meets structural requirements, while minimizing the material removal and the impact to the composite structure. An optimization algorithm is configured to adjust contour offsets for pad-up plies and adjust scarf taper ratios in any direction to reduce the amount of material removed or avoid underlying structures. Scarf repair designs are subsequently transmitted to repair technicians for manual scarfing via printed templates or automated/robotic scarfing using converted computer-readable code. The technology also provides digital data for automated repair ply cutting.