Machine Learning Algorithms for Predicting Mechanical Stiffness of Lattice Structure-Based Polymer Foam

Polymer foams are extensively utilized because of their superior mechanical and energy-absorbing capabilities; however, foam materials of consistent geometry are difficult to produce because of their random microstructure and stochastic nature. Alternatively, lattice structures provide greater design freedom to achieve desired material properties by replicating mesoscale unit cells. Such complex lattice structures can only be manufactured effectively by additive manufacturing or 3D printing. The mechanical properties of lattice parts are greatly influenced by the lattice parameters that define the lattice geometries. To study the effect of lattice parameters on the mechanical stiffness of lattice parts, 360 lattice parts were designed by varying five lattice parameters, namely, lattice type, cell length along the X, Y, and Z axes, and cell wall thickness. Computational analyses were performed by applying the same loading condition on these lattice parts and recording corresponding strain deformations. To effectively capture the correlation between these lattice parameters and parts' stiffness, five machine learning (ML) algorithms were compared. These are Linear Regression (LR), Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). Using evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), all ML algorithms exhibited significantly low prediction errors during the training and testing phases; however, the Taylor diagram demonstrated that ANN surpassed other algorithms, with a correlation coefficient of 0.93. That finding was further supported by the relative error box plot and by comparing actual vs. predicted values plots. This study revealed the accurate prediction of the mechanical stiffness of lattice parts for the desired set of lattice parameters.

Synthesis and properties of polyimide foams containing benzimidazole units

A series of novel polyimide foams containing benzimidazole units were prepared from polyester ammonium salt (PEAS) precursor powders, which were synthesized by co-polymerization of BTDA with two diamines, BIA and ODA, in various molar ratios.

Effect of Trolamine and Dibutyltindilaurate on the Structure and Properties of Polyurethane-imide Foams

Polyurethane-imide foams are prepared from polyphenyl polymethylene polyisocyanates, pyromellitic dianhydride and castor oil in presence of trolamine and dibutyltindilaurate catalysts. FTIR spectra proves the successful preparation of polyurethane-imide foams in prepolymer method. The investigation on catalytic mechanism shows that trolamine and dibutyltindilaurate are effective in promoting blowing and gelling reaction, respectively, so trolamine and dibutyltindilaurate are identified to be blowing catalyst and gelling catalyst, respectively. With the fixed amount of trolamine, the density and compressive strength of the resulting foams are enhanced with the increase of dibutyltindilaurate, but average cellular diameter and open cell contents are reduced. Similarly, with the fixed amount of dibutyltindilaurate, open cell contents of the resulting foams are enhanced with the increase of trolamine, while the density, compressive strength and average cellular diameter decreased. Thus PUIFs with variable properties can be prepared through changing the kind and ratio of mixed catalysts.