Nondestructive Quality Control in Powder Metallurgy using Hyperspectral Imaging

TL;DR

This paper explores the use of hyperspectral imaging combined with AI for non-destructive, rapid quality control of metal powders in additive manufacturing, focusing on contamination detection and material characterization.

Contribution

It introduces a novel application of near-infrared hyperspectral imaging for non-destructive inspection of metal powders, including case studies and analysis methods.

Findings

HSI effectively detects contamination and material differences in metal powders.

AI techniques enhance the analysis and interpretation of hyperspectral data.

Experimental results show HSI's potential for industrial powder quality control.

Abstract

Measuring the purity in the metal powder is critical for preserving the quality of additive manufacturing products. Contamination is one of the most headache problems which can be caused by multiple reasons and lead to the as-built components cracking and malfunctions. Existing methods for metallurgical condition assessment are mostly time-consuming and mainly focus on the physical integrity of structure rather than material composition. Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture and chemical composition. Therefore, HSI can provide a unique way to tackle this challenge. In this paper, with the use of a near-infrared HSI camera, applications of HSI for the non-destructive inspection of metal powders are introduced. Technical assumptions and solutions on three step-by-step case studies are presented in detail, including powder characterization, contamination detection, and band selection analysis. Experimental results have fully demonstrated the great potential of HSI and related AI techniques for NDT of powder metallurgy, especially the potential to satisfy the industrial manufacturing environment.