Advanced Eco-Friendly Non-Destructive Testing in Powder Metallurgy through AI-Based Acoustic Methodology

This study develops an eco-friendly non-destructive testing method for powder metallurgy, aiming to improve inspection techniques like magnetic particle inspection, given environmental impacts. The primary goal is to establish an intelligent manufacturing-based AI system for detecting internal cracks in powder metallurgy components after sintering. Acoustic data is collected using an electric hammer and microphone after sintering and processed to create spectrograms. An autoencoder extracts features, followed by cluster analysis to select samples for destructive testing. By integrating audio recognition technology and a convolutional neural network (CNN), unique sound features of sintered materials are identified, achieving a remarkable 99.60% accuracy in identifying specific types of powder metallurgy products. This eco-friendly testing method aligns with green manufacturing and showcases AI's potential in powder metallurgy manufacturing. This study demonstrates a significant advancement in enhancing quality control and propelling the powder metallurgy industry towards environmentally friendly manufacturing practices.