Polynomial Chaos-Based Procedural Generation of Synthetic Training Data in Machine Learning for Automated Acoustic Monitoring

Abstract

In additive manufacturing such as powder bed fusion the acoustic monitoring taking care of timely process termination in case of failure is commonly achieved by ear and therefore highly susceptible to human bias. Solutions based on machine learning algorithms need large datasets for training purposes which are not readily available. Additionally, capturing high-quality audio samples and providing respective material parts are expensive both in terms of time and cost. To overcome this problem, this work proposes a method by which the required synthetic datasets are obtained by way of procedural generation. Here, synthetic data implies the substitution of measured audio data by equivalent virtual and artificial samples from 3D acoustic simulations. In order to cover process variations as well as consider the variability of multiple input parameters, a design-of-experiments based on the theory of generalized polynomial chaos is conducted. Additionally, the polynomialchaos method is extended through use of a decision tree so that the prevalence of specific critical events may be accounted for.