Monitoring particle fracture during material handling is essential across sectors such as pharmaceuticals, food production, and mineral recovery.

During transport, blending, sieving, or packaging, particles can crack, separate, or wear down.

resulting in altered particle size profiles, flow behavior, and final product quality.

Conventional techniques like laser diffraction and sieve analysis offer useful metrics but fail to observe breakage as it occurs.

This technique presents a breakthrough by visually tracking each particle’s journey with exceptional clarity and detail.

enabling accurate measurement of fragmentation events.

The core principle behind dynamic imaging lies in capturing high-speed images of particles in motion, typically using a high-frame-rate camera and controlled lighting conditions.

While traversing the observation area, each particle’s geometry, dimensions, and texture are captured sequentially.

Sophisticated algorithms process the visual data to determine key metrics like projected surface, diameter equivalence, elongation ratio, and roundness.

Analyzing differences in particle morphology before and after processes like chute transfers, air transport, or impact events uncovers hidden degradation cues.

A key strength of dynamic imaging is its capacity to differentiate actual fragmentation from clustering or superficial wear.

In pharma applications, granules often fracture into sub-particles or release fine dust during mixing.

Dynamic imaging can identify whether the observed size reduction results from intentional granulation or unintended degradation.

ensuring batch uniformity and adherence to quality standards.

Mineral processing benefits from precise breakage analysis, allowing adjustments that cut energy use and improve throughput.

This method links particle failure directly to operational variables.

Aligning visual data with variables like belt speed, gas flow rate, or falling distance identifies critical failure zones.

Engineers can implement specific modifications including altering descent angles, integrating padding, or fine-tuning material delivery to lessen mechanical shock.

Its granularity uncovers non-uniform failure modes that traditional averaging obscures.

uncovering hidden failure modes.

Validation of dynamic imaging results often involves cross-referencing with other analytical tools.

Cross-checking imaging data with laser scattering measurements confirms reliability.

Fracture morphology can be further confirmed via scanning electron microscopy, adding texture and structural context to size measurements.

Despite its benefits, dynamic imaging is not without challenges.

The technique requires careful calibration to account for optical distortions, particle opacity, and lighting variations.

Real-time analysis requires intensive processing capacity to handle massive image streams.

Moreover, the system must be designed to handle the specific particle size range and material properties of the application.

Nano-sized particles demand enhanced optical clarity, while semi-transparent substances require tailored lighting setups.

With evolving software and improved sensors, 粒子形状測定 dynamic imaging is now viable for widespread industrial adoption.

Its power to turn subjective observations into objective, decision-ready data renders it critical for contemporary manufacturing excellence.

By enabling a deeper understanding of how particles respond to mechanical stress, dynamic imaging empowers engineers to design gentler, more efficient handling systems that preserve product integrity and reduce waste.

To summarize, this technique offers a granular, visual, and analytically rich framework for evaluating mechanical degradation during handling.

Unlike conventional methods, it uncovers the precise mechanics behind individual particle breakdown.

offering insights that directly inform process optimization.

As industries continue to prioritize product quality and operational efficiency, dynamic imaging stands out as a transformative tool in the ongoing effort to minimize unnecessary breakage and maximize performance throughout the manufacturing lifecycle