When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)

Abstract

Acoustic Emission (AE) is an effective method to monitor and control the quality in different technical processes and phenomena, including tribology and fracture mechanics. However, in highly dynamic processes such as Laser Additive Manufacturing (LAM) of metal, the processing of AE signals is very burdensome. At the same time, artificial intelligence (AI) has been considered as a new and powerful tool to overcome the complexity of the large data processing with a reasonable computational time. In this contribution, we have summarized our prior works. To start with, we demonstrated that the combination of AE with state-of-the-art signal processing including AI makes it possible to differentiate several process conditions, workpiece quality and materials. Then, we present some limitations when changing process parameters and materials. Finally, we also introduce alternative AI methods to reduce the amount of data needed to train the AI algorithms as well as transfer the knowledge from one material to another one. This will give the reader an overview of the advances of monitoring the LAM process combining AE with AI techniques to make a significant step forward in in situ and real-time process monitoring and quality control. In particular,

Laser welding monitoring with multisensory data fusion: A brief review

“…In this way, the frequency range is not constrained by membranes as in conventional mechanical microphones. Subsequently, the acoustic emission signals can be analyzed by filtering and correlation, or convolutional neural network, which is a machine learning algorithm designed for pattern recognition [33].…”

New Input Factors for Machine Learning Approaches to Predict the Weld Quality of Ultrasonically Welded Thermoplastic Composite Materials

“…Choi and Takahashi describe how crack information in short-fiber-reinforced thermoplastics can be mapped to different failure mechanisms [49], while Garrett et al show that an AI algorithm is able to binary-classify crack length in sheet-metal structures (98.4% accuracy in binary classification) with AE signals as input data [50]. Not only for crack evaluation but also for the monitoring of the quality of additive manufacturing, AE can be used [47] because processes such as additive manufacturing or laser welding carry information about material changes [51]. Arul et al mentioned that the changes in an AE signal during drilling processes can be used to monitor the sharpness of the drill and evaluate the optimal point of time for a tool change [1].…”

Combined Use of Acoustic Measurement Techniques with X-ray Imaging for Real-Time Observation of Laser-Based Manufacturing

“…Real-time monitoring of industrial processes with AE involves collecting and analyzing AE signals to identify and assess defects throughout the structure [35]. AE signals are valuable tools due to their cost-effectiveness and ability to achieve high temporal resolution (attributed to their 1D signal nature) [36,37]. This high temporal resolution allows AE sensors to detect and record rapid changes in the material during the laser melting process.…”