Understanding how particle size evolves over time in aging materials is critical across industries ranging from pharmaceuticals to advanced manufacturing and civil engineering. Traditional static imaging techniques often fall short when it comes to capturing real time changes in particle morphology due to environmental stressors, chemical reactions, or mechanical degradation. Dynamic image analysis offers a powerful solution by sequentially recording and 粒子形状測定 analyzing morphological shifts in real time with high temporal and spatial resolution. This approach leverages ultrafast optical sensors, adaptive illumination, and AI-driven pattern recognition to monitor individual particles as they undergo transformations during aging processes. Unlike conventional methods that rely on discrete measurements and post-hoc computational evaluation, dynamic image analysis enables real time feedback, allowing researchers to observe particle clustering, breakage, growth, or disintegration as they occur. The system typically operates within sealed test chambers with programmable thermal, moisture, and gas profiles to simulate aging conditions. Each frame captured by the camera is processed using morphological filtering and adaptive binarization to isolate particles from the background, followed by automated measurement of key parameters such as mean particle width, elongation factor, and projected area. Over time, these measurements are compiled into time series data, revealing trends and patterns that were previously invisible. Machine learning models are then trained to classify different types of particle behavior—such as agglomeration versus disintegration—based on historical data and known material properties. This not only increases accuracy but also reduces subjective analyst judgment. Validation is achieved through cross referencing with other analytical techniques like laser diffraction or electron microscopy, ensuring that the dynamic measurements correlate with established benchmarks. One of the most compelling applications of this technology is in the study of structural ceramics, where long-term hydration shifts porosity and grain morphology. By compressing years of aging into accelerated laboratory tests, dynamic image analysis provides actionable insights into material longevity and failure mechanisms. Similarly, in drug powder stability analysis, monitoring API crystallization or amorphous conversion during shelf life, helps predict shelf life and bioavailability. The scalability of dynamic image analysis also makes it suitable for automated manufacturing systems that require continuous particle integrity validation. As computational power increases and algorithms become more sophisticated, the ability to analyze dense particulate aggregates with volumetric resolution is becoming feasible. Future developments may integrate this technology with AI-driven simulation platforms that evolve in tandem with observed microstructural changes. Ultimately, dynamic image analysis transforms passive observation into active understanding, giving scientists and engineers the tools to proactively manage degradation pathways and optimize longevity. This capability is not merely an improvement in measurement—it is a fundamental redefinition of material aging analysis.