Huajun Cao’s research while affiliated with Chongqing University and other places

Wire-arc directed energy deposition (DED), an important branch of additive manufacturing, offers high material utilization and deposition rates for fabricating large-scale components. Although wire-arc DED has many advantages over other additive manufacturing methods, it faces challenges such as low geometric dimensional accuracy, coarse grain structures, and high porosity rates, which hinder its widespread industrial adoption, particularly for large-scale component manufacturing. The advanced energy field-assisted wire-arc DED addresses those issues by using multiple energy fields to influence the melting and solidification behaviors in the wire-arc DED process, thereby improving its manufacturing accuracy and enhancing component performance. This paper reviews the state-of-art development of energy field-assisted wire-arc DED, and explores future research priorities, aiming to provide into wire-arc DED of large metal components. Firstly, the application and advancements of four energy fields utilized in wire-arc DED: magnetic field (MF), ultrasonic vibration (UV), laser, and deformation field, are elucidated. Subsequently, the working principles governing the interactions between different energy fields and the wire-arc DED process are analyzed. Further, the effects of energy fields on the fabricated components are summarized, focusing on macroscopic morphology, microstructure, and mechanical properties. Finally, in the aspects of exploring the unclarified physical mechanism, and the establishing more advanced numerical modeling, field-assisted systems, and the online monitoring techniques, the current challenges and future perspectives are discussed, and elaborated, with the aim of advancing the industrial application of energy field-assisted wire-arc DED.

Sustainability represents a foundational element of Industry 5.0, with low-carbon manufacturing representing a pivotal route to sustainable manufacturing. The Data-driven approach of process parameter optimization for low carbon control is an effective means to reducing carbon emissions during the manufacturing process. However, it relies on large amounts of valid historical data. To overcome the challenge of insufficient valid data within individual enterprises and the necessity for safeguarding data privacy during cross-enterprises collaboration, this paper proposed a method leveraging federated learning to enable multi-source data collaboration across enterprises for training a predictive model of hobbing carbon emissions. Addressing the issue of non- independent identically distribution (non-IID) characteristics inherent in mutil-source hobbing carbon emission data, and the inefficacy of traditional federated learning methods on such complex datasets, this paper designs an improved federated learning algorithm, Federated increment Attentive Message Passing (Fed-I-AMP), to construct the mapping model between hobbing process parameters and carbon emissions. Subsequently, a multi-objective optimization model was developed with the objective of minimizing carbon emissions and Comprehensive Evaluation Index (CEI). This model was solved using the multi-objective Coati algorithm (MOCOA). Experimental validation demonstrates that this approach significantly enhances the convergence performance and prediction accuracy of the model. Additionally, the optimized process parameters provide scientific evidence and practical guidance for low-carbon hobbing operations.

This study investigates the mechanical and tribological performance of nano-graphene (NG) modified polyamide-imide (PAI) composite coatings under different lubrication conditions. PAI/NG coatings with 0, 1, 3, and 5 wt% NG were prepared on 45# steel. Experimental results show that the 3 wt% NG coating achieves optimal performance, with a 59.14 % reduction in surface roughness, 81.13 % increase in adhesion, 31.72 % lower friction, and 99.25 % reduction in wear rate compared to unmodified PAI. Excessive NG content negatively impacts performance due to poor dispersion. The composite coatings also exhibit a significant friction-reducing effect in lubricated conditions, with a 30.65 % lower friction coefficient in engine oil. These findings demonstrate the potential of NG-modified PAI coatings for enhancing wear resistance in mechanical components.

Ink-jetting printing stands out among various conformal additive manufacturing techniques for its multi-material, digital control, and process flexibility. Ink-jetting-based conformal additive manufacturing is renowned for its adaptability to complex topological surfaces and is emerging as a critical technology for future comprehensive conformal printing systems. This review highlights the distinctiveness of four primary ink-jetting printing techniques in conformal additive manufacturing—piezoelectric jetting, thermal bubble jetting, aerosol jetting, and electrohydrodynamic jetting—and delves into how these attributes endow ink-jetting printing with unique advantages in conformal processes. Furthermore, leveraging these advantages, the review discusses potential applications in conformal electronics, energy devices, biology, and electromagnetics to bolster the ongoing development and application. Considering the current state of this technology, the review identifies critical challenges for future advancements, such as dynamic surface printing, integrated fabrication of multifunctional conformal structures, and the balance between resolution and throughput. This review summarizes the latest research and technological advancements in ink-jetting-based conformal additive manufacturing, aiding in its innovative applications and enhanced manufacturing capabilities in the future.