With the increase in environmental awareness, coupled with an emphasis on environmental policy, achieving sustainable manufacturing is increasingly important. Additive manufacturing (AM) is an attractive technology for achieving sustainable manufacturing. However, with the diversity of AM types and various working states of machines’ components, a general method to forecast the energy consumption of AM is lacking. This paper proposes a new model considering the power of each component, the time of each process and the working state of each component to predict the energy consumption. Fused deposition modeling, which is a typical AM process, was selected to demonstrate the effectiveness of the proposed model. It was found that the proposed model had a higher prediction accuracy compared to the specific energy model and the process-based energy consumption model. The proposed model could be easily integrated into the software to visualize the printing time and energy consumption of each process in each component, and, further, provide a reference for coordinating the optimization of parts’ quality and energy consumption.

The manufacture of damping alloy parts with stable damping properties and high mechanical performances in the selective laser melting (SLM) process is influenced by temperature evolution and residual stress distribution. Choosing an appropriate scanning strategy, namely the specific trajectory along which the laser head scans powders within given area, is crucial, but clearly defined criteria for scanning strategy design are lacking. In this study, a three-dimensional finite element model (FEM) of the SLM process for manufacturing a WE43 alloy component was established and validated against the published experimental data. Eleven different scanning strategies were designed and simulated, considering variables such as scanning track length, direction, Out–In or In–Out strategy, start point, and interlayer variation. The results showed that scanning strategy, geometry, and layer number collectively affect temperature, melt pool, and stress outputs. For instance, starting scanning at a colder part of the powder layer could lead to a high peak temperature and low melt pool depth. A higher layer number generally results in lower cooling rate, a lower temperature gradient, a longer melt pool life, and larger melt pool dimensions. Changing the start point between scanning circulations helps mitigate detrimental residual stress. This work highlights the potential of analyzing various scanning strategy-related variables, which contributes to reducing trial-and-error tests and selecting optimal scanning strategies under different product quality requirements. This article can assist in the design of appropriate scanning strategies to prevent defects such as element loss due to evaporation, poor bonding, and deformation or cracking from high residual stress. Additionally, identifying stress concentration locations and understanding the effects of geometry and layer number on thermal and mechanical behaviors can assist in geometry design.

Additive manufacturing (AM) is widely used in fields, such as aerospace and medical treatment. However, the massive heterogeneous data generated during its production process face challenges, such as high transmission latency and large energy consumption. This article proposes a sustainable intelligent optimization strategy based on edge–cloud collaboration to enhance the intelligence and sustainability of AM. First, a hybrid model that integrates the local feature extraction of convolutional neural network (CNN) and the global dependency modeling of transformer (CNN–transformer) is designed to accurately predict the key process parameters of AM. Second, a multiobjective optimization model for surface roughness, processing time, and energy consumption is constructed. Combined with the improved Pareto set learning (PSL) algorithm, the collaborative optimization of economic and environmental sustainability is achieved. Finally, verification is carried out on selective laser melting (SLM) technology. The experimental results show that the prediction error of the CNN–transformer is lower than that of traditional models. It can reduce energy consumption and processing time while ensuring surface quality, thus providing a systematic solution for green intelligent manufacturing from Industry 4.0 to Industry 5.0.

Selective Laser Melting (SLM), as a common metal additive manufacturing (AM) technology, achieves high-precision complex part formation by layer-by-layer melting of metal powder using a laser. However, the dynamic behavior of the melt pool during the SLM process is influenced by the heat source model, which is crucial for suppressing porosity defects and optimizing process parameters, directly determining the reliability of numerical simulations. To address the issue of traditional surface heat source models overestimating the melt pool width and volume heat source models underestimating the melt pool depth, this study constructs a three-dimensional transient heat conduction finite element model based on ANSYS Parametric Design Language (APDL) to simulate the evolution of the temperature field and melt pool geometry under different laser parameters. First, the temperature fields and melt pool morphology and dimensions of four heat source models—Gaussian surface heat source, volumetric heat source models (rotating Gaussian volumetric heat source, double ellipsoid heat source), and a combined heat source model—were investigated. Subsequently, a dynamic heat source model was proposed, combining a Gaussian surface heat source with a rotating volumetric heat source. By dynamically allocating the laser energy absorption ratio between the powder surface layer and the substrate depth, the influence of this heat source model on melt pool size was explored and compared with other heat source models. The results show that under the dynamic heat source, the melt pool width and depth are 128.6 μm and 63.13 μm, respectively. The melt pool width is significantly larger compared to other heat source models, and the melt pool depth is about 17% greater than that of the combined heat source model. At the same time, the predicted melt pool width and depth under this heat source model have relative errors of 1.0% and 5.5% compared to the experimental measurements, indicating that this heat source model has high accuracy in predicting the melt pool’s lateral dimensions and can effectively reflect the actual melt pool morphology during processing.

With increasing pressure for sustainable manufacturing, reducing energy consumption while maintaining part quality has become a key challenge in fused deposition modeling (FDM). However, the collaborative optimization of process parameters and multi-part fabrication strategies remains insufficiently explored. In this study, four key process variables, namely nozzle temperature, layer thickness, infill density, and printing speed, were investigated. Regression models for dimensional error, surface roughness, and tensile strength were established, together with an energy-consumption prediction model. Based on these models, an improved multi-objective artificial hummingbird algorithm was employed to achieve collaborative optimization of quality and energy consumption for single-part fabrication. The optimal process parameters were identified as 220 °C nozzle temperature, 0.2 mm layer thickness, 38% infill density, and 58 mm s ⁻¹ printing speed. Furthermore, a dynamic energy-efficiency evaluation framework was developed for multi-part FDM fabrication. Considering the spatial arrangement and build-orientation strategies of parts with different geometric features, multiple fabrication plans were generated and assessed. The results show that the optimized strategy increased area utilization by 5.8% compared with the conventional approach. In addition, for target part A under plan-4, the energy-efficiency rating reached Grade A, while energy consumption and printing time were reduced by 11.2% and 4.8%, respectively, relative to plan-1. These results demonstrate that the proposed framework can effectively improve both quality and energy performance, and provides a practical approach for low-carbon and efficient FDM manufacturing.

Selective Laser Melting (SLM) technology has attracted considerable interest within industrial sectors due to its capability to efficiently fabricate components with intricate geometries. Nevertheless, the complexity inherent in such components frequently results in defect formation in critical regions during the SLM process, primarily attributable to non-uniform temperature distributions. This investigation, grounded in the principles of additive manufacturing (AM), decomposes complex geometric components into combinations characterized by varying scan path lengths and track numbers. Employing numerical simulations, the study examines the influence of these two parameters on the thermo-mechanical behavior and melt pool dynamics during the SLM processing of AlSi10Mg alloy. The findings indicate that an increase in scan length corresponds with a gradual rise in both melt pool temperature and size at nodes b and c. Simultaneously, the maximum cooling rate at node b exhibits a continuous decline, whereas at node c, it initially increases before subsequently decreasing. With an increasing number of tracks, the peak melt pool temperature at nodes a and c first decreases and then progressively increases, while the rates of temperature change and melt pool dimensions display stepwise fluctuations. Furthermore, residual stresses within the fabricated component demonstrate a positive correlation with scan length, with shorter scan lengths generally inducing higher compressive stresses. Across the component’s width, residual stresses are more substantially affected by the number of tracks, resulting in the emergence of multiple stress peaks. These results offer a theoretical basis for the optimization of thermo-mechanical defects in the SLM fabrication of complex components.