Feature based building orientation optimization for additive manufacturing

Purpose The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning. Design/methodology/approach To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations. Findings The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers. Research limitations/implications The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations. Originality/value AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

[1]  David W. Rosen,et al.  A process planning method for improving build performance in stereolithography , 2001, Comput. Aided Des..

[2]  Kalyanmoy Deb,et al.  Multi‐objective optimisation and multi‐criteria decision making in SLS using evolutionary approaches , 2011 .

[3]  Alessandro Franco,et al.  Experimental Analysis of Selective Laser Sintering of Polyamide Powders: an Energy Perspective , 2010 .

[4]  Alain Bernard,et al.  Fundamental concepts of product/technology/process informational integration for process modelling and process planning , 2003, Int. J. Comput. Integr. Manuf..

[5]  Pio G. Iovenitti,et al.  Part Build Orientations Based on Volumetric Error in Fused Deposition Modelling , 2000 .

[6]  Han Tong Loh,et al.  Benchmarking for comparative evaluation of RP systems and processes , 2004 .

[7]  Kunwoo Lee,et al.  The development of a CAD environment to determine the preferred build-up direction for layered manufacturing , 1998 .

[8]  Alain Bernard,et al.  A new decision support method for the selection of RP process: knowledge value measuring , 2014, Int. J. Comput. Integr. Manuf..

[9]  B. Gurumoorthy,et al.  A Feature-Based Framework for Semantic Interoperability of Product Models , 2008 .

[10]  S. S. Pande,et al.  Automatic recognition of machining features using artificial neural networks , 2009 .

[11]  David W. Rosen,et al.  Computer‐aided build style decision support for stereolithography , 1998 .

[12]  David W. Rosen,et al.  Usage of accuracy models in stereolithography process planning , 2000 .

[13]  Jami J. Shah.  Assessment of features technology , 1991, Comput. Aided Des..

[14]  P. M. Pandey,et al.  Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm , 2004 .

[15]  Alain Bernard,et al.  An integrated decision-making model for multi-attributes decision-making (MADM) problems in additive manufacturing process planning , 2014 .

[16]  Franca Giannini,et al.  Automatic recognition and representation of shape-based features in a geometric modeling system , 1989, Comput. Vis. Graph. Image Process..

[17]  Andrew Y. C. Nee,et al.  Multi‐objective optimization of part‐ building orientation in stereolithography , 1995 .

[18]  Han Tong Loh,et al.  Optimal orientation with variable slicing in stereolithography , 1997 .

[19]  Ian A. Ashcroft,et al.  ENERGY INPUTS TO ADDITIVE MANUFACTURING: DOES CAPACITY UTILIZATION MATTER? , 2011 .

[20]  Ho-Chan Kim,et al.  Reduction of post-processing for stereolithography systems by fabrication-direction optimization , 2005, Comput. Aided Des..

[21]  Rajit Gadh,et al.  Shape feature determination usiang the curvature region representation , 1997, SMA '97.

[22]  Antonio Armillotta,et al.  A tool for computer-aided orientation selection in additive manufacturing processes , 2013 .

[23]  Georges Fadel,et al.  Expert system-based selection of the preferred direction of build for rapid prototyping processes , 1995, J. Intell. Manuf..

[24]  Kwan H. Lee,et al.  Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making , 2006 .

[25]  Seok-Hee Lee,et al.  Determination of fabricating orientation and packing in SLS process , 2001 .

[26]  Han Tong Loh,et al.  Considerations and selection of optimal orientation for different rapid prototyping systems , 1999 .

[27]  S. H. Choi,et al.  Modelling and optimisation of Rapid Prototyping , 2002, Comput. Ind..

[28]  Richard J.M. Hague,et al.  Cost estimation for rapid manufacturing - laser sintering production for low to medium volumes , 2006 .

[29]  H. S. Byun,et al.  A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method , 2005 .

[30]  Richard H. Crawford,et al.  Form feature recognition using base volume decomposition , 1994 .

[31]  Alain Bernard,et al.  An Overview on Knowledge Management , 2008 .

[32]  Fons Wijnhoven,et al.  Manufacturing Knowledge Work: The European Perspective , 2008 .

[33]  Seth Allen,et al.  Part orientation and build cost determination in layered manufacturing , 1998, Comput. Aided Des..

[34]  Duc Truong Pham,et al.  Part Orientation in Stereolithography , 1999 .

[35]  Paul A. Sullivan,et al.  Predicting total build‐time and the resultant cure depth of the 3D stereolithography process , 1996 .

[36]  David W. Rosen,et al.  Computer-Aided Design for Additive Manufacturing of Cellular Structures , 2007 .

[37]  B. Gurumoorthy,et al.  Unified Taxonomy for Reference Ontology of Shape Features in Product Model , 2013, PLM.

[38]  N. Venkata Reddy,et al.  Part deposition orientation studies in layered manufacturing , 2007 .

[39]  Alain Bernard,et al.  Using AM feature and multi-attribute decision making to orientate part in Additive Manufacturing , 2013 .

[40]  Shuo-Yan Chou,et al.  Determining fabrication orientations for rapid prototyping with Stereolithography apparatus , 1997, Comput. Aided Des..

[41]  Stephan Grimm,et al.  Feature ontologies for the explicit representation of shape semantics , 2005, Int. J. Comput. Appl. Technol..

[42]  Syed H. Masood,et al.  A generic algorithm for a best part orientation system for complex parts in rapid prototyping , 2003 .

[43]  Alain Bernard,et al.  Deployment of an Innovative Resource Choice Method for Process Planning , 2014, ArXiv.

[44]  Yunfeng Zhang,et al.  A volume decomposition approach to machining feature extraction of casting and forging components , 2001, Comput. Aided Des..

[45]  Alain Bernard.  The feature approach for the integrated design and machining of forming dies , 1993 .

[46]  Debasish Dutta,et al.  A review of process planning techniques in layered manufacturing , 2000 .