An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer–metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

In this study, the temperature and thermal stress fields of an internal combustion hot blast stove were calculated and analysed. Turbulent, species transport, chemical reaction, radiation, and porous media models were implemented in a computational fluid dynamics model. Thermal boundary conditions on the structure of the hot blast stove were calculated based on the analytic adiabatic Y-plus method. A method to interpolate the thermal boundary conditions to a finite element mesh was developed, and the boundary conditions were mapped through the proposed method. In the on-gas period, the vortex was generated in the dome, and it made the variation of the temperature field in the checker chamber. The maximum temperature of the flue gas reached 1841 K in the on-gas period. In the on-blast period, the flow was considerably even compared to the on-gas period, and the average blast temperature reached 1345 K. The outer region of the checker chamber is shown to be continuously exposed to a higher temperature, which makes the region the main domain in managing the deterioration of the refractory linings. The shell temperature did not change during the operation due to the lower thermal diffusivity of the refractory linings, where the inner surface of the refractory had a maximum temperature change from 1441 K to 1659 K. The maximum temperature of the shell was 418.4 K at the conical region of the checker chamber side. The conical region had the higher maximum and middle principal thermal stresses due to the presence of a large temperature gradient around the conical region, where the largest maximum and middle principal stresses were 300.6 MPa and 192.0 MPa, respectively. The conical region was found to be a significant area of interest where it had a higher temperature and thermal stress.

The stress corrosion cracking during the operation of the internal combustion hot blast stove was analysed. The computational fluid dynamics and finite element analysis models were established to analyse the temperature, stress and other variables related to the condensation of the water and acids. The corrosion characteristics of condensation of acid and the stress corrosion cracking of the metallic shell of the hot blast stove during the operation were predicted by applying the fluid temperature and mapping it to the solid temperature. The stress corrosion cracking surface mobility mechanism was adopted and modified with a weight concept to consider the effect of the acid condensation and its concentration. The regions that have higher crack propagation rates were analysed. The influence of the increase in the blast temperature on the crack propagation rate was studied with the increase in the blast temperature by 45 K and 90 K from the reference blast temperature. The maximum temperature of the refractory linings was 1847 K in the on-gas period, and the maximum change in the shell temperature was 5.2 K when the blast temperature was increased by 90 K. The maximum crack propagation rate for the reference blast temperature was evaluated as $7.61\times {10}^{-7}$ m/s. The maximum value of the crack propagation rate was increased by 16.7% when the blast temperature increased by 90 K. The conical region was found to have higher crack propagation rates, which means that the conical region should be the region of interest for managing the internal combustion hot blast stoves.