Additive Manufacturing for Industrial Applications

Development of a Methodology for Integrating Added Value into Products by Additive Manufacturing

Thomas Papke, Dominic Bartels, Michael Schmidt, Marion Merklein, Daniel Gerhard, Jonas Baumann, Indra Pitz
JournalIndustrie 4.0 Management
Issue Volume 36, 2020, Edition 4, Pages 50-54
Open Accesshttps://doi.org/10.30844/I40M_20-4_S50-54
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Abstract

Additive Manufacturing has become more important for industrial applications. The technology offers the opportunity of high geometric flexibility and no need of product specific tools including short time to market. The aim is to integrate added value into products by exploiting these possibilities. Therefore, in this work a methodology focusing on these aspects is developed and applied to a structural component.

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Bibliography

[1] Schmidt, M.; Merklein, M.; Bourell, D.; Dimitrov, D.; Hausotte, T.; Wegener, K.; Overmeyer, L.; Vollertsen, F.; Levy, G. N.: Laser based additive manufacturing in industry and academia. In: CIRP Annals 66 (2017), S. 561-583.
[2] Rosen, D. W.: Research supporting principles for design for additive manufacturing. In: Virtual and physical prototyping 9 (2014) 4, S. 225-232.
[3] Lindemann, C.; Reiher, T.; Jahnke, U.; Koch, R.: Towards a sustainable and economic selection of part candidates for additive manufacturing. In: Rapid Prototyping Journal 21 (2015) 2, S. 216-227.
[4] Mancanares, C. G.; Zancul, E. de S.; da Silva, J. C.; Miguel, P. A. C.: Additive manufacturing process selection based on parts’ selection criteria. In: Int. Journal of Advanced Manufacturing Technologies 80 (2015), S. 1007-1014.
[5] Wortmann, N.; Jürgenhake, C.; Seidenberg, T.; Dumitrescu, R.; Krause, D.: Methodical approach for process selection in additive manufacturing. In: Int. Conf. on Engineering Design ICED19 (2019), S. 779- 788.
[6] Gokuldoss, P. K.; Kolla, S.; Eckert, J.: Additive manufacturing processes: Selective Laser Melting, Electron Beam Melting and Binder Jetting – Selection Guidelines. In: materials 10 (2017) 672, S. 1-12.
[7] Kumke, M.; Watschke, H.; Hartogh, P.; Bavendiek, A.-K.; Vietor, T.: Methods and tools for identifying and leveraging additive manufacturing design potentials. In: Int. Journal of Interactive Design and Manufacturing 12 (2018), S. 48-493.
[8] Klahn, C.; Leutenecker, B.; Meboldt, M.: Design for Additive Manufacturing – Supporting the substitution of components in series products. In: Procedia CIRP 21 (2014), S. 138-143.
[9] Kumke, M.; Watschke, H.; Vietor, T.: A new methodological framework for design for additive manufacturing. In: Virtual and physical prototyping 11 (2016) 1, S. 3-19.
[10] Gläßner, C.; Yi, L.; Aurich, J. C.: Concept for development of additive process chains in manufacturing companies. In: Schmitt, R.; Schuh, G. (Hrsg): Advances in Production Re- search – Proceedings of the 8th Congress of the German Academic Association for Production Technology (WGP). Cham, Switzerland 2018.
[11] Yi, L.; Gläßner, C.; Aurich, J. C.: How to integrate additive manufacturing technologies into manufacturing systems successfully: A perspective from the commercial vehicle industry. In: Journal of Manufacturing Systems 53 (2019), S. 195-211.
[12] Thompson, M. K.; Stolfi, A.; Mischkot, M.: Process chain modelling and selection in an additive manufacturing context. In: CIRP Journal of Manufacturing Science and Technology 12 (2016), S. 25- 34.
[13] Spalt, P.; Bauernhansl, T.: A framework for integration of additive manufacturing technologies in production networks. In: Procedia CIRP 57 (2016), S. 716-721.
[14] Zanardini, M.; Bacchetti, A.; Adrodegari, F.: Evaluation of technical and economic feasibility of additive manufacturing technology: evidences from a case study. In: Industrial Systems Engineering (2016), S. 6-11.
[15] Thomas, D. S.; Gillbert, S. W.: Costs and Cost Effectiveness of Additive Manufacturing – A Literature Review and Discussion. In: NIST Special Publication 1176. 2014.

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