Advanced Predictive Quality Assessment for Ultrasonic Additive Manufacturing with Deep Learning Model

TL;DR

This paper presents a deep learning-based method using CNNs to monitor and classify the quality of Ultrasonic Additive Manufacturing processes through thermal image analysis, achieving over 97% accuracy.

Contribution

It introduces a novel CNN-based approach for real-time quality assessment in UAM, effectively classifying process conditions with high accuracy.

Findings

CNN models achieved over 97% accuracy in classifying thermal images.

The method reliably distinguishes between different process conditions.

High classification accuracy supports improved quality control in UAM.

Abstract

Ultrasonic Additive Manufacturing (UAM) employs ultrasonic welding to bond similar or dissimilar metal foils to a substrate, resulting in solid, consolidated metal components. However, certain processing conditions can lead to inter-layer defects, affecting the final product's quality. This study develops a method to monitor in-process quality using deep learning-based convolutional neural networks (CNNs). The CNN models were evaluated on their ability to classify samples with and without embedded thermocouples across five power levels (300W, 600W, 900W, 1200W, 1500W) using thermal images with supervised labeling. Four distinct CNN classification models were created for different scenarios including without (baseline) and with thermocouples, only without thermocouples across power levels, only with thermocouples across power levels, and combined without and with thermocouples across power levels. The models achieved 98.29% accuracy on combined baseline and thermocouple images, 97.10% for baseline images across power levels, 97.43% for thermocouple images, and 97.27% for both types across power levels. The high accuracy, above 97%, demonstrates the system's effectiveness in identifying and classifying conditions within the UAM process, providing a reliable tool for quality assurance and process control in manufacturing environments.