ADDITIVE MANUFACTURING DESIGN FEATURE SELECTION FOR VARIABLE PRODUCT PLATFORMS

Additive manufacturing (AM) technologies enable new capabilities in producing innovative products with complex geometries, superior performance, and low material wastage. In this research, design for additive manufacturing (DFAM) freedoms and constraints are integrated with product platform design, aiming to help companies generate innovative platform-based product families by selecting appropriate AM design features to meet platform modules' design requirements in multiple market segments. In this paper, the concept a variable product platform is proposed to describe new characteristics of additive manufactured product platform modules. An object-oriented technique is used for representing design knowledge. A binary coding system is applied to code AM design features and platform variants' design requirements. Hierarchical agglomerative clustering is performed to create clusters that indicate appropriate AM design feature selection, and to group similar AM design features in terms of functionalities, materials, and key design parameters. The result provides a design proposal to explore AM-enabled design space at the conceptual design stage.

[1]  H. ElMaraghy,et al.  Product family formation for reconfigurable assembly systems , 2014 .

[2]  J. Kruth,et al.  Fine-structured aluminium products with controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder , 2013 .

[3]  Carolyn Conner Seepersad,et al.  Challenges and Opportunities in Design for Additive Manufacturing , 2014 .

[4]  Guido A.O. Adam,et al.  Direct Manufacturing Design Rules , 2011 .

[5]  Zuomin Dong,et al.  Coding and clustering of design and manufacturing features for concurrent design , 1997 .

[6]  Zahed Siddique,et al.  Platform-Based Product Family Development , 2006 .

[7]  Eujin Pei,et al.  Development of a design feature database to support design for additive manufacturing , 2012 .

[8]  Frédéric Vignat,et al.  Designing for Additive Manufacturing , 2012 .

[9]  David W. Rosen,et al.  Design for Additive Manufacturing , 2015, Additive Manufacturing Technologies.

[10]  Edward F. Crawley,et al.  Crafting Platform Strategy Based on Anticipated Benefits and Costs , 2014 .

[11]  R. Poprawe,et al.  Combined strengthening of multi-phase and graded interface in laser additive manufactured TiC/Inconel 718 composites , 2014 .

[12]  Steven B. Shooter.  Ice Scraper Product Family Development at Innovation Factory , 2006 .

[13]  David W. Rosen,et al.  Development of Additive Manufacturing Technology , 2010 .

[14]  Seung Ki Moon,et al.  Platform design variable identification for a product family using multi-objective particle swarm optimization , 2014 .

[15]  David W. Rosen,et al.  Research supporting principles for design for additive manufacturing , 2014 .

[16]  David W. Rosen,et al.  Ontology Based Knowledge Modeling and Reuse Approach of Supporting Process Planning in Layer-Based Additive Manufacturing , 2010, 2010 International Conference on Manufacturing Automation.

[17]  Osama Abu Abbas,et al.  Comparisons Between Data Clustering Algorithms , 2008, Int. Arab J. Inf. Technol..

[18]  Yuh-Min Chen,et al.  Computer-aided feature-based design for net shape manufacturing , 1997 .

[19]  Timothy W. Simpson,et al.  Product family design knowledge representation, aggregation, reuse, and analysis , 2007, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[20]  Eun Suk Suh,et al.  Flexible product platforms: framework and case study , 2007 .

[21]  Jacquelyn K. S. Nagel,et al.  Designing a Modular Rapid Manufacturing Process , 2010 .

[22]  Timothy W. Simpson,et al.  A Review of Recent Literature in Product Family Design and Platform-Based Product Development , 2014 .

[23]  W RosenDavid,et al.  The Roadmap for Additive Manufacturing and Its Impact , 2014 .

[24]  Chee Kai Chua,et al.  Interfacial characterization of SLM parts in multi-material processing: Metallurgical diffusion between 316L stainless steel and C18400 copper alloy , 2014 .