Characterizing the temperature-dependent mechanical properties of structural adhesives is critical for industrial applications in aerospace, automotive, and electronics. The increasing integration of artificial intelligence (AI) in material discovery has amplified the demand for large, high-quality datasets, which conventional mechanical testing methods often cannot efficiently provide. In this study, a novel micro-indentation method is introduced that enables rapid and accurate evaluation of static and dynamic mechanical properties of structural adhesives across a wide temperature range. A 3-mm spherical indenter is utilized to perform both quasi-static and dynamic loading on flat bulk samples, enabling accurate multi-modal measurement through independent and precise temperature control of both the indenter and the bulk material, thereby ensuring reliable measurements with minimal sample preparation. Static indentation tests on epoxy and acrylic samples demonstrated that the elastic modulus can be accurately obtained from unloading data, even with plastic deformation, using the Oliver–Pharr method. Dynamic testing further revealed that the epoxy exhibited higher storage and loss moduli than the acrylic adhesive, indicating superior mechanical performance at elevated temperatures. Conversely, the acrylic adhesive exhibited a lower glass transition temperature, indicating a narrower operational temperature range, and a higher loss factor, reflecting greater energy dissipation. The proposed method enhances the efficiency and accuracy of mechanical characterization, enabling the high-throughput testing necessary to generate datasets for AI-driven material development. By enabling rapid design and optimization of polymers, this technique is promising for advancing material discovery with tailored properties.
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