Wikipedia

Design for additive manufacturing

Design for additive manufacturing (DfAM or DFAM) is design for manufacturability as applied to additive manufacturing (AM). It is a general type of design methods or tools whereby functional performance and/or other key product life-cycle considerations such as manufacturability, reliability, and cost can be optimized subjected to the capabilities of additive manufacturing technologies.[1]

This concept emerges due to the enormous design freedom provided by AM technologies. To take full advantages of unique capabilities from AM processes, DfAM methods or tools are needed. Typical DfAM methods or tools includes topology optimization, design for multiscale structures (lattice or cellular structures), multi-material design, mass customization, part consolidation, and other design methods which can make use of AM-enabled features.

DfAM is not always separate from broader DFM, as the making of many objects can involve both additive and subtractive steps. Nonetheless, the name "DfAM" has value because it focuses attention on the way that commercializing AM in production roles is not just a matter of figuring out how to switch existing parts from subtractive to additive. Rather, it is about redesigning entire objects (assemblies, subsystems) in view of the newfound availability of advanced AM. That is, it involves redesigning them because their entire earlier design—including even how, why, and at which places they were originally divided into discrete parts—was conceived within the constraints of a world where advanced AM did not yet exist. Thus instead of just modifying an existing part design to allow it to be made additively, full-fledged DfAM involves things like reimagining the overall object such that it has fewer parts or a new set of parts with substantially different boundaries and connections. The object thus may no longer be an assembly at all, or it may be an assembly with many fewer parts. Many examples of such deep-rooted practical impact of DfAM have been emerging in the 2010s, as AM greatly broadens its commercialization. For example, in 2017, GE Aviation revealed that it had used DfAM to create a helicopter engine with 16 parts instead of 900, with great potential impact on reducing the complexity of supply chains.[2] It is this radical rethinking aspect that has led to themes such as that "DfAM requires 'enterprise-level disruption'."[3] In other words, the disruptive innovation that AM can allow can logically extend throughout the enterprise and its supply chain, not just change the layout on a machine shop floor.

DfAM involves both broad themes (which apply to many AM processes) and optimizations specific to a particular AM process. For example, DFM analysis for stereolithography maximizes DfAM for that modality.

Background

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Additive manufacturing is defined as a material joining process, whereby a product can be directly fabricated from its 3D model, usually layer upon layer.[4] Comparing to traditional manufacturing technologies such as CNC machining or casting, AM processes have several unique capabilities. It enables the fabrication of parts with a complex shape as well as complex material distribution.[5] These unique capabilities significantly enlarge the design freedom for designers. However, they also bring a big challenge. Traditional Design for manufacturing (DFM) rules or guidelines deeply rooted in designers’ mind and severely restrict designers to further improve product functional performance by taking advantages of these unique capabilities brought by AM processes. Moreover, traditional feature-based CAD tools are also difficult to deal with irregular geometry for the improvement of functional performance. To solve these issues, design methods or tools are needed to help designers to take full advantages of design freedom provide by AM processes. These design methods or tools can be categorized as Design for Additive Manufacturing.

Methods

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Topology optimization

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Topology optimization is a type of structural optimization technique which can optimize material layout within a given design space. Compared to other typical structural optimization techniques, such as size optimization or shape optimization, topology optimization can update both shape and topology of a part. However, the complex optimized shapes obtained from topology optimization are always difficult to handle for traditional manufacturing processes such as CNC machining. To solve this issue, additive manufacturing processes can be applied to fabricate topology optimization result.[6] However, it should be noticed, some manufacturing constraints such as minimal feature size also need to be considered during the topology optimization process.[7] Since the topology optimization can help designers to get an optimal complex geometry for additive manufacturing, this technique can be considered one of DfAM methods.

Multiscale structure design

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Due to the unique capabilities of AM processes, parts with multiscale complexities can be realized. This provides a great design freedom for designers to use cellular structures or lattice structures on micro or meso-scales for the preferred properties. For example, in the aerospace field, lattice structures fabricated by AM process can be used for weight reduction.[8] In the bio-medical field, bio-implant made of lattice or cellular structures can enhance osseointegration.[9]

Multi-material design

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Parts with multi-material or complex material distribution can be achieved by additive manufacturing processes. To help designers take advantage of this capability, several design and simulation methods [10][11][12] have been proposed to support the design of a part with multiple materials or Functionally Graded Materials . These design methods also bring a challenge to traditional CAD system. Most of them can only deal with homogeneous materials now.

Design for mass customization

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Since additive manufacturing can directly fabricate parts from products’ digital model, it significantly reduces the cost and leading time of producing customized products. Thus, how to rapidly generate customized parts becomes a central issue for mass customization. Several design methods [13] have been proposed to help designers or users to obtain the customized product in an easy way. These methods or tools can also be considered as the DfAM methods.

Parts consolidation

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Due to the constraints of traditional manufacturing methods, some complex components are usually separated into several parts for the ease of manufacturing as well as assembly. This situation has been changed by the using of additive manufacturing technologies. Some case studies have been done to shows some parts in the original design can be consolidated into one complex part and fabricated by additive manufacturing processes. This redesigning process can be called as parts consolidation. The research shows parts consolidation will not only reduce part count, it can also improve the product functional performance.[14] The design methods which can guide designers to do part consolidation can also be regarded as a type of DfAM methods.

Lattice structures

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Lattice structures is a type of cellular structures (i.e. open). These structures were previously difficult to manufacture, hence was not widely used. Thanks to the free-form manufacturing capability of additive manufacturing technology, it is now possible to design and manufacture complex forms. Lattice structures have high strength and low mass mechanical properties and multifunctionality.[15] These structures can be found in parts in the aerospace and biomedical industries.[16][17] It has been observed that these lattice structures mimic atomic crystal lattice, where the nodes and struts represent atoms and atomic bonds, respectively, and termed as meta-crystals. They obey the metallurgical hardening principles (grain boundary strengthening, precipitate hardening etc.) when undergoing deformation.[18] It has been further reported that the yield strength and ductility of the struts (meta-atomic bonds) can be increased drastically by taking advantage of the non-equilibrium solidification phenomenon in Additive Manufacturing, thus increasing the performance of the bulk structures.[19]

Thermal issues in design

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For AM processes that use heat to fuse powder or feedstock, process consistency and part quality are strongly influenced by the temperature history inside the part during manufacture, especially for metal AM.[20][21] Thermal modelling can be used to inform part design and the choice of process parameters for manufacture, in place of expensive empirical testing.[22][23][24]

Optimal design for additive manufacturing

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Additively manufactured metallic structures with the same (macroscopic) shape and size but fabricated by different process parameters have strikingly different microstructures and hence mechanical properties.[25] The abundant and highly flexible AM process parameters substantially influence the AM microstructures.[25] Therefore, in principle, one could simultaneously 3D-print the (macro-)structure as well as the desirable microstructure depending on the expected performance of the specialized AM component under the known service load. In this context, multi-scale and multi-physics integrated computational materials engineering (ICME) for computational linkage of process-(micro)structure-properties-performance (PSPP) chain can be used to efficiently search an AM design subspace for the optimum point with respect to the performance of the AM structure under the known service load.[26] The comprehensive design space of metal AM is boundless and high dimensional, which includes all the possible combinations of alloy compositions, process parameters and structural geometries. However, always a constrained subset of the design space (design subspace) is under consideration. The performance, as the design objective, depending on the thermo-chemo-mechanical service load, may include multiple functional aspects, such as specific energy absorption capacity, fatigue life/strength, high temperature strength, creep resistance, erosion/wear resistance and/or corrosion resistance. It is hypothesized that the optimal design approach is essential for unraveling the full potential of metal AM technologies and thus their widespread adoption for production of structurally critical load-bearing components.[26]

References

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  1. ^ Tang, Yunlong (2016). "A survey of the design methods for additive manufacturing to improve functional performance". Rapid Prototyping Journal. 22 (3): 569–590. doi:10.1108/RPJ-01-2015-0011.
  2. ^ Zelinski, Peter (2017-03-31), "GE team secretly printed a helicopter engine, replacing 900 parts with 16", Modern Machine Shop, retrieved 2017-04-09.
  3. ^ Hendrixson, Stephanie (2017-04-24), "How to think about design for additive manufacturing", Modern Machine Shop, retrieved 2017-05-05.
  4. ^ "ASTM F2792 - 12a Standard Terminology for Additive Manufacturing Technologies, (Withdrawn 2015)". www.astm.org. Retrieved 2016-09-03.
  5. ^ Gibson, Dr Ian; Rosen, Dr David W.; Stucker, Dr Brent (2010-01-01). Additive Manufacturing Technologies. Springer US. pp. 299–332. doi:10.1007/978-1-4419-1120-9_11. ISBN 9781441911193. S2CID 106433250.
  6. ^ Barbieri, Loris; Muzzupappa, Maurizio (2022). "Performance-Driven Engineering Design Approaches Based on Generative Design and Topology Optimization Tools: A Comparative Study". Applied Sciences. 12 (4): 2106. doi:10.3390/app12042106.
  7. ^ Leary, Martin; Merli, Luigi; Torti, Federico; Mazur, Maciej; Brandt, Milan (2014-11-01). "Optimal topology for additive manufacture: A method for enabling additive manufacture of support-free optimal structures". Materials & Design. 63: 678–690. doi:10.1016/j.matdes.2014.06.015.
  8. ^ Tang, Yunlong; Kurtz, Aidan; Zhao, Yaoyao Fiona (2015-12-01). "Bidirectional Evolutionary Structural Optimization (BESO) based design method for lattice structure to be fabricated by additive manufacturing". Computer-Aided Design. 69: 91–101. doi:10.1016/j.cad.2015.06.001.
  9. ^ Schmidt, M.; Zaeh, M.; Graf, T.; Ostendorf, A.; Emmelmann, C.; Scheinemann, P.; Munsch, M.; Seyda, V. (2011-01-01). "Lasers in Manufacturing 2011 - Proceedings of the Sixth International WLT Conference on Lasers in ManufacturingLaser Additive Manufacturing of Modified Implant Surfaces with Osseointegrative Characteristics". Physics Procedia. 12: 375–384. doi:10.1016/j.phpro.2011.03.048. hdl:11420/2013.
  10. ^ Zhang, Feng; Zhou, Chi; Das, Sonjoy (2015-08-02). Volume 1A: 35th Computers and Information in Engineering Conference. pp. V01AT02A031. doi:10.1115/DETC2015-47772. ISBN 978-0-7918-5704-5.
  11. ^ Zhou, Shiwei; Wang, Michael Yu (2006-07-18). "Multimaterial structural topology optimization with a generalized Cahn–Hilliard model of multiphase transition". Structural and Multidisciplinary Optimization. 33 (2): 89. doi:10.1007/s00158-006-0035-9. ISSN 1615-147X. S2CID 120392674.
  12. ^ Stanković, Tino; Mueller, Jochen; Egan, Paul; Shea, Kristina (2015-08-02). "Optimization of Additively Manufactured Multi-Material Lattice Structures Using Generalized Optimality Criteria". {{cite journal}}: Cite journal requires |journal= (help)
  13. ^ Reeves, Phil; Tuck, Chris; Hague, Richard (2011-01-01). Fogliatto, Flavio S.; Silveira, Giovani J. C. da (eds.). Mass Customization. Springer Series in Advanced Manufacturing. Springer London. pp. 275–289. doi:10.1007/978-1-84996-489-0_13. ISBN 9781849964883.
  14. ^ Yang, Sheng; Tang, Yunlong; Zhao, Yaoyao Fiona (2015-10-01). "A new part consolidation method to embrace the design freedom of additive manufacturing". Journal of Manufacturing Processes. 20, Part 3: 444–449. doi:10.1016/j.jmapro.2015.06.024.
  15. ^ Azman, Abdul Hadi; Vignat, Frédéric; Villeneuve, François (2018-04-29). "Cad Tools and File Format Performance Evaluation in Designing Lattice Structures for Additive Manufacturing". Jurnal Teknologi. 80 (4). doi:10.11113/jt.v80.12058. ISSN 2180-3722.
  16. ^ Gao, Wei; Zhang, Yunbo; Ramanujan, Devarajan; Ramani, Karthik; Chen, Yong; Williams, Christopher B.; Wang, Charlie C.L.; Shin, Yung C.; Zhang, Song (December 2015). "The status, challenges, and future of additive manufacturing in engineering". Computer-Aided Design. 69: 65–89. doi:10.1016/j.cad.2015.04.001. ISSN 0010-4485.
  17. ^ Rashed, M. G.; Ashraf, Mahmud; Mines, R. A. W.; Hazell, Paul J. (2016-04-05). "Metallic microlattice materials: A current state of the art on manufacturing, mechanical properties and applications". Materials & Design. 95: 518–533. doi:10.1016/j.matdes.2016.01.146. ISSN 0264-1275.
  18. ^ Pham, Minh-Son; Liu, Chen; Todd, Iain; Lertthanasarn, Jedsada (2019). "Damage-tolerant architected materials inspired by crystal microstructure" (PDF). Nature. 565 (7739): 305–311. Bibcode:2019Natur.565..305P. doi:10.1038/s41586-018-0850-3. ISSN 1476-4687. PMID 30651615. S2CID 58014425.
  19. ^ Rashed, M. G.; Bhattacharyya, Dhriti; Mines, R. A. W.; Saadatfar, M.; Xu, Alan; Ashraf, Mahmud; Smith, M.; Hazell, Paul J. (2019-10-23). "Enhancing the bond strength in meta-crystal lattice of architected materials". arXiv:1910.10658 [cond-mat.mtrl-sci].
  20. ^ Dowling, L.; Kennedy, J.; O'Shaughnessy, S.; Trimble, D. (2020). "A review of critical repeatability and reproducibility issues in powder bed fusion". Materials and Design. 186: 108346. doi:10.1016/j.matdes.2019.108346.
  21. ^ Diegel, O.; Nordin, A.; Motte, D. (2019). A Practical Guide to Design for Additive Manufacturing. Springer Series in Advanced Manufacturing. Singapore: Springer. doi:10.1007/978-981-13-8281-9. ISBN 978-981-13-8280-2. S2CID 181963805.
  22. ^ Yavari, R.; Cole, K. D.; Rao, P. K. (2019). "Design Rules for Additive Manufacturing – Understanding the Fundamental Thermal Phenomena to Reduce Scrap". Procedia Manufacturing. 33: 375–382. doi:10.1016/j.promfg.2019.04.046.
  23. ^ Yavari, R.; Cole, K. D.; Rao, P. K. (2019). "Thermal Modeling in Metal Additive Manufacturing using Graph Theory". Journal of Manufacturing Science and Engineering. 141 (7): 071007. doi:10.1115/1.4043648. S2CID 155207189.
  24. ^ Denlinger, E. R.; Irwin, J.; Michaleris, P. (2014). "Thermomechanical modeling of additive manufacturing large parts". Journal of Manufacturing Science and Engineering. 136 (6): 061007. doi:10.1115/1.4028669.
  25. ^ a b Motaman, S. Amir H.; Haase, Christian (2021-05-01). "The microstructural effects on the mechanical response of polycrystals: A comparative experimental-numerical study on conventionally and additively manufactured metallic materials". International Journal of Plasticity. 140: 102941. doi:10.1016/j.ijplas.2021.102941. ISSN 0749-6419. S2CID 234056213.
  26. ^ a b Motaman, S. Amir H.; Kies, Fabian; Köhnen, Patrick; Létang, Maike; Lin, Mingxuan; Molotnikov, Andrey; Haase, Christian (2020-03-01). "Optimal Design for Metal Additive Manufacturing: An Integrated Computational Materials Engineering (ICME) Approach". JOM. 72 (3): 1092–1104. Bibcode:2020JOM....72.1092M. doi:10.1007/s11837-020-04028-4. ISSN 1543-1851.
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