Process monitoring in additive manufacturing (AM), i.e. in laser powder bed fusion (LPBF) of metal parts, has been identified as the crucial bottleneck in accelerating the AM industrialization process. To reduce the cost and time needed to produce and qualify an AM part, an online monitoring system of the manufacturing process is desirable. While the currently available systems capture a large amount of process data, they still lack the ability to interpret the acquired data adequately. In this work we present the first steps towards an automated evaluation of online monitoring data i.e. melt pool data. It is shown that a well-trained convolutional neural network (CNN) is able to detect artificially induced process deviations on the basis of melt pool characteristics.      «

Process monitoring in additive manufacturing (AM), i.e. in laser powder bed fusion (LPBF) of metal parts, has been identified as the crucial bottleneck in accelerating the AM industrialization process. To reduce the cost and time needed to produce and qualify an AM part, an online monitoring system of the manufacturing process is desirable. While the currently available systems capture a large amount of process data, they still lack the ability to interpret the acquired data adequately. In this...     »