AI Augmented Digital Metal Component

TL;DR

This paper introduces an AI-enabled digital metal component that uses real-time data and CNNs to classify its status, enabling self-cognition and early fault detection in additive manufacturing applications.

Contribution

It presents a novel integration of AI with metal additive manufacturing, enabling metal parts to recognize their own state and detect faults autonomously.

Findings

Achieved 89.1% accuracy in recognizing partial screw loosening and other faults.

Demonstrated real-time classification of metal part status during mechanical testing.

Showed potential for enhanced connectivity and autonomous monitoring in digital metal systems.

Abstract

The aim of this work is to propose a new paradigm that imparts intelligence to metal parts with the fusion of metal additive manufacturing and artificial intelligence (AI). Our digital metal part classifies the status with real time data processing with convolutional neural network (CNN). The training data for the CNN is collected from a strain gauge embedded in metal parts by laser powder bed fusion process. We implement this approach using additive manufacturing, demonstrate a self-cognitive metal part recognizing partial screw loosening, malfunctioning, and external impacting object. The results indicate that metal part can recognize subtle change of multiple fixation state under repetitive compression with 89.1% accuracy with test sets. The proposed strategy showed promising potential in contributing to the hyper-connectivity for next generation of digital metal based mechanical systems