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2 2023

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The Role of Additive Manufacturing in Supply Chain Management

Patalas-Maliszewska Justyna, Kowalczewska Katarzyna, Rehm Matthias

Management and Production Engineering Review

2023

Vol. 14, No. 1

25--33

Nowadays, it is necessary to develop a conceptual framework for analysing the relationship between the implementation of Additive Manufacturing (AM) and Supply Chain Management (SCM). In this context, a gap in the research has been observed in the new approach to designing the importance of AM in SCM. The main contribution of this paper, therefore, is a new framework to formulate the role in adopting AM in SCM. The research methodology is based on detailed literature studies of AM in relation to the SCM process within a manufacturing company, as well on a case study, namely the COWAN GmbH manufacturing company who specialise in producing homewares for motorhome enthusiasts. As highlighted in the state-of-the-art analysis, no work, currently available, supports all the features presented.

Recognition of sports exercises using inertial sensor technology

Krutz Pascal, Rehm Matthias, Schlegel Holger, Dix Martin

Applied Computer Science

2023

Vol. 19, no 1

152--163

Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work is to implement a workflow for the automated recognition of sports exercises in the Matlab® programming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing is implemented. Realised functionalities include the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data are used for the training of classifiers and artificial neural networks (ANN). These are iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models are finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments is also displayed graphically, allowing statements to be made about potential causes of incorrect assignments. In this context, especially the transition areas between the classes are detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.