Binder Jetting Additive Manufacturing: Principles, Applications, Challenges, and Future Prospects: A Short Review
摘要
Binder Jetting (BJ) is a powder-bed additive manufacturing (AM) process distinguished by its capacity to fabricate parts from a broad spectrum of feedstocks metals, ceramics, refractory sands, and polymers without subjecting the powder to a high-energy thermal source during printing. This review provides a critical, quantitatively grounded analysis of BJ that organizes the literature around the process–microstructure–property chain and benchmarks BJ against fusion-based AM technologies across thirteen comparable criteria. In contrast to fusion-based processes, BJ relies on the selective deposition of a liquid binder onto successive powder layers, producing a porous green body whose final density and mechanical performance are determined by a downstream sequence of curing, debinding, sintering, and where required, infiltration or hot isostatic pressing. The roles of layer thickness (50–200 μm), binder saturation (40–80% of pore volume), particle size distribution (D50 = 15–45 μm), and sintering schedule on density, tensile strength, fatigue behaviour, and dimensional accuracy are critically analyzed using data from peer-reviewed experimental studies. Industrial adoption across aerospace, automotive, biomedical, tooling, and foundry sectors is assessed against current Technology Readiness Levels. Although BJ confers advantages in throughput, scalability, and design freedom, residual porosity of 1–8% after standard sintering, anisotropic linear shrinkage of 15–20%, and fragile green parts remain unresolved technical hurdles. A dedicated critical synthesis of research gaps spanning binder chemistry, in-situ recoating-force sensing, AI/ML-assisted sintering compensation, and digital-twin process control is provided to move beyond descriptive reporting and identify where quantitative evidence is still lacking. The review closes with a roadmap covering multi-material printing, in-situ recoating-force sensing, machine-learning-assisted sintering compensation, and nano powder-enabled low-temperature consolidation.
Graphical Abstract