This paper mainly analyzes the typical thermodynamic response (thermal history, thermal strain and residual stress) in a conventional continuous-wave (CW) laser during Directed Energy Deposition (DED). The influence of process parameters (laser power and scanning speed) on the temperature gradient in the heat-affected zone, thermal strain and residual stress are studied, and the corresponding relationship are established. The results show that a reduction in residual stress can be obtained by decreasing the temperature gradient. However, the method of reducing the temperature gradient by changing process parameters leads to low forming quality and low density. A pulse-wave laser (PW) is proposed to actively control the residual stress of the deposited sample. This laser mode can reduce not only the temperature gradient in the process of DED but also the in situ release of thermal stress, correspondingly greatly reducing the residual stress.

In this work, both numerical simulations and experimental characterization were used to obtain a broad understanding of the thermo-mechanical history, residual stress, and microstructure of the directed energy deposition (DED) process of austenitic stainless steels. To investigate the effect of process factors on residual stress, the global sensitivity analysis approach based on D-MORPH-HDMR was utilized. The results of the research reveal that the amplified effect of the influence of the three input variables (layer thickness, L; laser power, P; and scanning speed, v) on the transverse residual stress and thickness-direction residual stress is L > P > v; in contrast, the influence of longitudinal residual stress is P > L > v. We also found that general tendencies in local plastic strain accumulation are analogous to the relative distribution of geometrically necessary dislocations (GNDs). Additionally, we investigated post-solidification structures connected to residual stress, such as submicron dislocation cells and dynamic recrystallization (DRX) in austenitic stainless steels during DED. The investigation revealed that the DDRX and CDRX phenomena were caused by the bulging of initial grain boundaries and progressive sub-grain rotation (PSR). The fact that the sample bottom had more thermo-mechanical cycles than the top led to a higher dislocation density and hence more DDRX. This study presents a unique perspective on the link between residual stress and microstructure in additive manufacturing.

The influence of annealing at various temperatures on the phase stability and microstructure of pulsed-wave laser mode (PW) 316L stainless steel fabricated via Directed Energy Deposition (DED) was systematically investigated. The microstructural alterations resulting from heat treatment were examined to clarify their influence on the mechanical properties of the specimens subjected to tensile loading. The results showed that cell size increased with annealing temperature, with the cellular microstructure disappearing at higher temperatures (T ≥ 1000 °C). A decrease in the mechanical strength of the specimens was observed as annealing temperature increased. Additionally, the influence of different laser pulse frequencies and duty cycles on residual stresses was examined, revealing that moderate laser frequencies and duty cycles effectively reduced residual stress levels.

Predicting carbon emissions is important in various sectors, including environmental management, economic planning, and energy policy. Traditional forecasting models typically require extensive training data to achieve high accuracy. However, carbon emission data are usually available on an annual basis, which is insufficient for effectively training conventional forecasting models. To address this challenge, this paper introduces an innovative carbon emissions prediction model that integrates Fibonacci attenuation particle swarm optimization (FAPSO) with the gated recurrent unit (GRU). The FAPSO algorithm is used to optimize the hyperparameters of the GRU, thereby alleviating the decline in prediction accuracy that conventional recurrent neural networks often face when dealing with limited training data. To evaluate the effectiveness of the FAPSO-GRU model, we tested it using carbon emission data from Hainan Province. Compared to the conventional GRU model, the FAPSO-GRU model achieved a significant reduction in the mean absolute error (42.27%), root mean square error (42.38%), and mean absolute percentage error (43.06%). Furthermore, we validated the FAPSO-GRU model with real data from Beijing, Guangdong, Hubei, Hunan, and Shanghai. The experimental results convincingly demonstrate that the proposed model provides a highly accurate solution for carbon emission prediction tasks, effectively addressing the limitations posed by limited training data.