Commissioning of machines takes up a considerable share of time and money of the total cost of developing a machine. Our project aims at developing an approach to decrease the time needed to commission machines by automating parameter optimisation with the help of formalised expert knowledge. The approach will be developed on the Fused Deposition Modelling (FDM) process, which is an additive manufacturing technique. We pay particular attention to keeping the approach sufficiently abstract to be applied to machines from other domains to benefit its industrial application.
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.
Most current supervised learning systems require large quantities of labelled data, limiting their applicability in domains where labelled data is scarce and hard to obtain. We introduce a novel approach for incorporating additional, usergiven areas of interest during training by which the learning process can be guided. The provided guiding attention is incorporated in the training phase as a form of data augmentation, which ensures that input dimensions do not vary between train and test/deployment time, when no guiding attention is present. We evaluate this approach by extending the CIFAR-10 dataset with prototypical information and ascertain, that our approach reduces the required amount of samples by up to 44.89%, when combined with traditional data augmentation techniques. This would enable the use of learning systems in parts of manufacturing such as commissioning, where additional samples are scarce and costly to obtain while providing guiding attention is a matter of seconds.Index Terms-expert knowledge, artificial neural networks, data augmentation, guiding information This work is supported by the German Federal Ministry for Economic Affairs and Energy (BMWi).
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