Reliability-Based Aggregation of Heterogeneous Knowledge to Assist Operators in Manufacturing

In socio-technical systems, such as manufacturing processes, human operators are often entrusted with cognitive tasks that rely on tacit knowledge. Extracting the operators' tacit knowledge is beneficial to facilitate knowledge transfer and enable semantic machine learning. We improve upon an existing methodology that relies on operators' insights into influences on their decision making processes to extract tacit knowledge. By introducing a data-based weighting of the operators' information, we are able to control varying degrees of worker reliability and other individual biases, increasing the quality of the aggregated knowledge. We evaluate several methods to weigh and aggregate knowledge on a real-world dataset collected in the domain of fused deposition modelling (FDM) showing an improvement of 34% over a previously published baseline applied to our data. Applicability of the approach in the same domain is demonstrated by a case study, where the aggregated knowledge is utilised to shorten the time required for parametrisation.