Acoustic Monitoring Dataset for Robotic Laser Directed Energy Deposition (LDED) of Maraging Steel C300

Abstract

This dataset presents a set of acoustic signals captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz. Laser Directed Energy Deposition: This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle. Folder Structure: /sample-1: The main folder for the experiment sample. /audio_files: Contains 4624 .wav audio files, each representing a 40 ms chunk of the LDED process sound. /annotations_1.csv: A CSV file providing annotations for the audio files, labeling each as "Defect-free", "Defective", or "Laser-off". audio_features.h5: extracted acoustic features in time-domain, frequency-domain, and time-frequency representations (MFCC features). Feature extraction was conducted using Python Essentia Library. File Naming Convention: Audio files within the audio_files folder are named following the pattern sample_ExperimentID_SampleID.wav. Given that there's only one experiment and one sample, the naming will be consistent, for example, sample_1_1.wav for the first file. Annotation Details: The annotations_1.csv file contains detailed labels for each audio file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis. Experimental Parameters: The dataset reflects a controlled experiment setup with the following specifications: Geometry: Single bead wall structure Dimensions: 90 mm * 42.5 mm Number of layers: 50 Laser beam diameter: 2 mm Layer thickness: 0.85 mm Stand-off distance: 12 mm Laser profile: Gaussian Laser wavelength: 1064 nm Process Parameters: Laser power: 2.3 kW Speed: 25 mm/s Dwell time: 0 s Powder flow rate: 12 g/min This dataset aims to facilitate the development and testing of acoustic-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.