Multi-material additive manufacturing offers new design freedom for functional integration and opens new possibilities in innovative part design, for instance, a local integration of electrically conductive structures or heat radiant surfaces. Detailed experimental investigations on materials with three different fillers (carbon black (CB), carbon nanotubes (CNT) and nano copper wires) were conducted to identify process-specific influencing factors on electrical conductivity and resistive heating. In this regard, raster angle orientation, extrusion temperature, speed and flow rate were investigated. A variation of the raster angle (0 • , ±45 • , and 90 • ) shows the highest influence on resistivity. An angle of 0 • had the lowest electrical resistance and the highest temperature increase due to resistive heating. The material filled with nano copper wires showed the highest electrical conductivity followed by the CNT filled material and materials filled with CB. Both current-voltage characteristics and voltage-dependent heat distribution of the surface temperature were determined by using a thermographic camera. The highest temperature increase was achieved by the CNT filled material. The materials filled with CB and nano copper wires showed increased electrical resistance depending on temperature. Based on the experiments, solution principles and design rules for additively manufactured electrically conductive structures are derived. Appl. Sci. 2019, 9, 779 2 of 25 or casting), the designer has entirely new opportunities in product design. Consequently, there are two big challenges. On the one hand, the design engineer needs to be supported to ensure a consideration of these new design potentials in conceptual design. On the other hand, rules for designing conductive structures have to be established, for instance, to adjust the electrical resistance. The latter research gap is focused on in this contribution. Therefore, a simultaneous consideration of part design and process planning is crucial to leverage the advantages of AM's design freedom [9]. A provision of specific knowledge regarding AM's design potentials is essential for both, conceptual design (e.g., solution principles) and detail design (e.g., design rules) [10]. At present, no systematic consideration of the design potentials of multi-material AM, especially regarding electrically conductive structures, in product development is possible. There are only rudimentary frameworks [11] or general design heuristics for multi-material AM [12,13]. Moreover, design rules for MEX generally concern only geometrical restrictions [14] or consider process related influencing factors on mechanical properties [15]. However, no specific guidelines and rules for the design of additively manufactured conductive structures have been developed. Consequently, designers have no common basis on which functions such as electrical conductivity or heat radiation can be integrated in multi-material parts manufactured by MEX.Extensive experimental investigations were condu...
Additive manufacturing (AM) opens new possibilities for innovative product designs. However, due to a lack of knowledge and restrained creativity because of design fixations, design engineers do not take advantage of AM's design freedom. Especially multi-material AM provides new opportunities for functional integration that hardly considered in ideation. To overcome barriers in the development of solution ideas and utilizing such new design potentials, new design methods and tools are needed. Therefore, in this contribution, a methodological approach for a function-oriented provision of solution principles specific to material extrusion is presented. A tool is developed to facilitate effective guidance in developing solution ideas and to foster a realistic concretization by providing a combination of opportunistic and restrictive AM knowledge. Besides general levers of AM, process-specific design opportunities support the design engineers in exploiting AM's potentials, especially those who are not familiar with Design for AM. Finally, the applicability of the methodological approach is evaluated in an academic study by means of redesigning a hand prosthesis with a grab function.
To be able to use finite element (FE) simulations in structural component development, experimental investigations for the characterization of the material properties are required to subsequently calibrate suitable material cards. In contrast to the commonly used computational and time-consuming method of parameter identification (PI) by using analytical and numerical optimizations with internal or commercial software, a more time-efficient method based on machine learning (ML) is presented. This method is applied to simulate the material behavior of additively manufactured specimens made of acrylonitrile butadiene styrene (ABS) under uniaxial stress in a structural simulation. By using feedforward artificial neural networks (FFANN) for the ML-based direct inverse PI process, various investigations were carried out on the influence of sampling strategies, data quantity and data preparation on the prediction accuracy of the NN. Furthermore, the results of hyperparameter (HP) search methods are presented and discussed and their influence on the prediction quality of the FFANN are critically evaluated. The investigations show that the NN-based method is applicable to the present use case and results in material parameters that lead to a lower error between experimental and calculated force-displacement curves than the commonly used optimization-based method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.