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Electronic components formed from electrically conductive textiles require a clear characterization of properties, such as electrical resistance, to enable the design and manufacture of safe and reliable electronic textile devices. The low dimensional stability of some electroactive fabrics can present challenges to electronic characterization. In this study, an electrical resistor was formed within a fabric by sewing a highly conductive metallic coated thread into less conductive fabric. A knitted fabric treated with polypyrrole was used to explore the effect of stitch parameters on the quality of the intra-fabric connection. A 1.5—2 mm straight stitch was identified as a reliable method for intra-fabric connection. A range of fabrics with different structures was sewn in this way and the electrical resistance characterization was compared with two other methods. The interaction of materials and processing for electronic textile characterization, component design, and manufacture is discussed.

This paper compares methods for identifying determinism within graph-rewriting systems. From the viewpoint of functional decomposition, these methods can be implemented to search efficiently for distinct function structures. An additional requirement is imposed on this comparison that stems from a cooperative design application where different organizations contribute to a distributed graph-rewriting system: Inspecting the definitions of production rules is not allowed for identifying determinism because production rules are considered to be confidential corporate knowledge. Under this assumption, two approaches were selected and empirically compared with respect to random search and guided search scenarios. The results suggest that the herein proposed dynamic rule independence analysis outperforms traditional approaches in light of the above restriction.

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Configuration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.

Abstract:This dataset provides high-resolution 2D scans of 3D printed test objects (dog-bone), derived from EN ISO 527-2:2012. The specimens are scanned in resolutions from 600 dpi to 4800 dpi utilising a Konica-Minolta bizHub 42 and Canon LiDE 210 scanner. The specimens are created to research the influence of the infill-pattern orientation; The print orientation on the geometrical fidelity and the structural strength. The specimens are printed on a MakerBot Replicator 2X 3D-printer using yellow (ABS 1.75 mm Yellow, REC, Moscow, Russia) and purple ABS plastic (ABS 1.75 mm Pink Lion&Fox, Hamburg, Germany). The dataset consists of at least one scan per specimen with the measured dimensional characteristics. For this, software is created and described within this work. Specimens from this dataset are either scanned on blank white paper or on white paper with blue millimetre marking. The printing experiment contains a number of failed prints. Specimens that did not fulfil the expected geometry are scanned separately and are of lower quality due to the inability to scan objects with a non-flat surface. For a number of specimens printed sensor data is acquired during the printing process. This dataset consists of 193 specimen scans in PNG format of 127 objects with unadjusted raw graphical data and a corresponding, annotated post-processed image. Annotated data includes the detected object, its geometrical characteristics and file information. Computer extracted geometrical information is supplied for the images where automated geometrical feature extraction is possible.