Multi-material additive manufacturing of polymers has experienced a remarkable increase in interest over the last 20 years. This technology can rapidly design and directly fabricate three-dimensional (3D) parts with multiple materials without complicating manufacturing processes. This research aims to obtain a comprehensive and in-depth understanding of the current state of research and reveal challenges and opportunities for future research in the area. To achieve the goal, this study conducts a scientometric analysis and a systematic review of the global research published from 2000 to 2021 on multi-material additive manufacturing of polymers. In the scientometric analysis, a total of 2512 journal papers from the Scopus database were analyzed by evaluating the number of publications, literature coupling, keyword co-occurrence, authorship, and countries/regions activities. By doing so, the main research frame, articles, and topics of this research field were quantitatively determined. Subsequently, an in-depth systematic review is proposed to provide insight into recent advances in multi-material additive manufacturing of polymers in the aspect of technologies and applications, respectively. From the scientometric analysis, a heavy bias was found towards studying materials in this field but also a lack of focus on developing technologies. The future trend is proposed by the systematic review and is discussed in the directions of interfacial bonding strength, printing efficiency, and microscale/nanoscale multi-material 3D printing. This study contributes by providing knowledge for practitioners and researchers to understand the state of the art of multi-material additive manufacturing of polymers and expose its research needs, which can serve both academia and industry.

Visual inspection of civil infrastructure has traditionally relied on manual operations, characterized by high labor intensity, low efficiency, and limited scalability, which significantly constrains its effectiveness in modern maintenance and management scenarios. Although deep learning technologies have demonstrated remarkable potential for automation and precision, their practical implementation in real-world engineering contexts remains hindered by the scarcity of large-scale, high-quality annotated datasets. To address this challenge, this study constructs a UAV-based dataset of building surface defects, comprising 14,471 high-resolution images captured across six structural types and five representative defect categories from both urban and rural environments. The dataset includes various defect types such as cracks, abscission, leakage, corrosion, and bulging, recorded under diverse illumination and environmental conditions. Each image is annotated with standardized bounding boxes and systematically divided into training, validation, and testing subsets. This dataset provides a comprehensive, diverse, and publicly accessible benchmark for advancing multi-task research in defect detection, segmentation, and automated visual assessment of building surfaces.

The commonly used aluminum–copper alloys in industry are mainly rolled plates and extruded or drawn bars. The aluminum–copper alloys’ anisotropy generated in the manufacturing process is unfavorable for subsequent applications. Its underlying mechanism shall be interpreted from a microscopic perspective. This paper conducted the loading simulation on Al–4%Cu alloy crystals at the microscopic scale with molecular dynamics technology. Uniaxial tension and compression loading were carried out along three orientations: X-<$\overline{1}12$>, Y-<$1\overline{1}1$>, and Z-. It analyzes the micro-mechanisms that affect the performance changes of aluminum–copper alloys through the combination of stress–strain curves and different organizational analysis approaches. As shown by the results, the elastic modulus and yield strength are the highest under tension along the <$1\overline{1}1$> direction. Such is the case for the reasons below: The close-packed plane of atoms ensures large atomic binding forces. In addition, the Stair-rod dislocation forms a Lomer–Cottrell dislocation lock, which has a strengthening effect on the material. The elastic modulus and yield strength are the smallest under tension along the direction, and the periodic arrangement of HCP atom stacking faults serves as the main deformation mechanism. This is because the atomic arrangement on the plane is relatively loose, which tends to cause atomic misalignment. When compressed in different directions, the plastic deformation mechanism is mainly dominated by dislocations and stacking faults. When compressed along the direction, it has a relatively high dislocation density and the maximum yield strength. That should be attributed to the facts below. As the atomic arrangement of the plane itself was not dense originally, compression loading would cause an increasingly tighter arrangement. In such a case, the stress could only be released through dislocations. This research aims to provide a reference for optimizing the processing technology and preparation methods of aluminum–copper alloy materials.

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated.