Knowledge Center

Sieve correlation has been a well-established method in particle sizing for many years, but the development of automated dynamic image analysis is beginning to revolutionize the process. In this blog post, we will discuss how automated dynamic image analysis is becoming a complementary method to sieve correlation and how it provides more accurate and detailed results.

Dynamic image analysis (DIA) is a digital method of particle characterization that enables a researcher to identify the shape, size, and number of particles in a sample. Due to its high accuracy and reproducibility, DIA is a widely used image analysis method across most industries to support quality control, compliance, and research applications.

The behavior of raw materials can typically be predicted by properly studying a sample and identifying physical characteristics such as particle size. Features most commonly analyzed include distribution, size, and shape. Particle size measurement is a crucial process in various industries, as understanding specific particles and their properties can enhance productivity, reduce waste and ensure product quality.

Particle size is a crucial property to measure when dealing with particulate raw materials. It underlies key manufacturing parameters like flowability, packing, and wetting–each of which can have a demonstrable impact on the end quality of a part or product.

Creating parts using metal powders requires exceptional high-quality raw materials. Many analytical tools are used to pre-screen metal powders to ensure proper powder flowability, prevent clogging, and ensure adequate particle deposition to guarantee well-made parts. 

Particle size reduction is a routine method of increasing surface area and making downstream processing more efficient. It is well documented that particle size distribution (PSD) affects powder flowability. However, studies have demonstrated the limitations of particle size analysis – particularly in applications with irregular particles. Ignoring particle shape can lead to significant under-estimation of the mean particle size which leads to errors in PSD results.

Particle size is a critical factor in qualifying abrasive powders. Larger particles typically produce an aggressive cutting action with fast material removal rates, albeit with a poor surface finish. Smaller particles, by contrast, offer a smoother finish with significantly reduced rates of removal.

Material identification is a crucial part of the additive manufacturing process, particularly when dealing with metal powder feedstocks. Although metal powder 3D printing is increasingly common, there are various quality assurance and control (QA/QC) challenges that must be considered.