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SummaryA fast multipole boundary element method (FMBEM) extended by an adaptive mesh refinement algorithm for solving acoustic problems in three‐dimensional space is presented in this paper. The Collocation method is used, and the Burton–Miller formulation is employed to overcome the fictitious eigenfrequencies arising for exterior domain problems. Because of the application of the combined integral equation, the developed FMBEM is feasible for all positive wave numbers even up to high frequencies. In order to evaluate the hypersingular integral resulting from the Burton–Miller formulation of the boundary integral equation, an integration technique for arbitrary element order is applied. The fast multipole method combined with an arbitrary order h‐p mesh refinement strategy enables accurate computation of large‐scale systems. Numerical examples substantiate the high accuracy attainable by the developed FMBEM, while requiring only moderate computational effort at the same time. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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