Volume 7, No. 1, January 2018

General Information

  • ISSN: 2278-0149 (Online)
  • Abbreviated Title:  Int. J. Mech. Eng. Robot. Res.  
  • Editor-in-Chief: ​Prof Richard (Chunhui) Yang, Western Sydney University, Australia
  • Associate Editor: Prof. B.V. Appa Rao, Andhra University; Prof. Ian R. McAndrew, Capitol Technology University, USA
  • Managing Editor: Murali Krishna. B
  • DOI: 10.18178/ijmerr
  • Abstracting/Indexing: Scopus (since 2016), CNKI, Google Scholar, Crossref, etc.
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International Journal of Mechanical Engineering and Robotics Research
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An Approach to Model Additive Manufacturing Process Rules

Christelle Grandvallet 1, Frederic Vignat 2, Franck Pourroy 2, Guy Prudhomme 2, and Nicolas Béraud 3
1. Univ.Grenoble Alpes, CNRS, G-SCOP, 38000 Grenoble, France
2. Univ.Grenoble Alpes, CNRS, G-SCOP, 38000 Grenoble, France
3. DP Research Institute, 34000 Montpellier, France

Abstract—The building of small and complex metallic products with Additive Manufacturing (AM) has modified the practices of experts. While this technology opens up new horizons in terms of shape and geometry it requires various knowledge and strategies to design and manufacture parts properly. In this context for instance trials and errors or process simulation are used by experts to gain in knowledge maturity. This paper presents how simulation, experimentation, as well as knowledge construction can complement each other and add value for solving AM engineering problem and specifically manufacturing defects. A first section introduces the context and the challenges of simulating a powder bed multilayer process as well as characterizing AM knowledge. A second section presents the State of the Art related to simulation process and AM knowledge management. The proposed approach next highlights the need for integrating knowledge elements as simulation and experimentation are undertaken. It is then applied to a case study about the manufacturing of lattice structures with EBM technology. Results point out two benefits of this co-construction: a step-by-step method for research with simulation; a knowledge model made of progressive stages and leading to AM process rules. As a conclusion, the study confirms that the proposed approach helps not only to map the AM activity but also to formalize its associated knowledge. AM knowledge process acts thus as a support for AM research process but is also nourished by simulation and experience results in a continuous improvement cycle. Lastly this work brings perspectives for the development of AM knowledge modelling for CAM systems. 
Index Terms—computer aided manufacturing, additive manufacturing, knowledge modeling

Cite: Christelle Grandvallet, Frederic Vignat, Franck Pourroy, Guy Prudhomme, and Nicolas Béraud, "An Approach to Model Additive Manufacturing Process Rules," International Journal of Mechanical Engineering and Robotics Research, Vol. 7, No. 1, pp. 9-15, January 2018. DOI: 10.18178/ijmerr.7.1.9-15