Extraction of Fault Patterns on SLS Part Surfaces Using the Karhunen-Loeve Transform
To gain a thorough understanding of the fault mechanisms in SLS machines, we decompose SLS profile signals into independent features using a novel tool called Karhunen-Loeve (KL) transform. These individual features can then be studied separately to monitor the occurrence of fault patterns on manufactured parts and determine their nature. Analytical signals with known fault patterns, simulating profile measurement signals from SLS parts, are used to determine the suitability of the proposed method. Multi-component patterns are assumed to manifest on SLS part surfaces, resulting from faults in the machine, for example, the roller mechanism. The results of this work determine the suitability of the KL transform for condition monitoring and extraction of fault-indicating patterns.