Research progress on artificial intelligence-driven design and manufacturing of high-performance titanium-based materials：opportunities and challenges

Due to the sensitivity and complexity of the composition-process-microstructure-performance relationship, the research and development of high-performance titanium-based materials have long been constrained by the dual challenges of high-dimensional nonlinear optimization and high trial-and-error costs. As a highly pervasive disruptive technology, artificial intelligence (AI) is introducing a new research and development paradigm for the strategic field of high-performance titanium-based materials, shifting from experience-driven modes to dual-driven approaches supported by models and data. This review summarizes the latest research advances in artificial intelligence-enabled high-performance titanium-based material technology (AI+Ti), focusing on how AI provides innovative solutions targeting the inherent characteristics of high-performance titanium-based materials, including complex compositions, diverse phase transitions, narrow thermal processing windows, and strong path dependence of microstructure evolution. The main contents include breakthroughs achieved by AI in constructing high-precision phase diagram and performance prediction models, as well as realizing the inverse design from performance objectives to microstructures and further to composition and processing parameters; the intelligent upgrading from forming control to active regulation of microstructures and properties in key processes such as additive manufacturing and heat treatment; and the establishment of an in-service behavior prediction framework based on digital twins. On this basis, this paper further analyzes the core challenges in the AI+Ti field regarding data, models, verification and integration, and prospects future development directions such as physics-informed machine learning and autonomous experimental platforms. Finally, it discusses controversial issues involving knowledge representation, human-machine collaboration modes and engineering trust establishment, and elaborates on the future development trends of this field: (1) material performance prediction and multi-scale coupling under complex service environments; (2) intelligent coordination of full-process processing parameters; (3) the construction and iteration of specialized physics-informed perception models for titanium alloys. Beyond simple tool application, AI+Ti has evolved into a transformative revolution that enables in-depth understanding and ultimate mastery of the cognition and research paradigm for high-performance titanium-based materials.