Please wait a minute...

Frontiers of Computer Science

Front. Comput. Sci.    2017, Vol. 11 Issue (2) : 347-357     DOI: 10.1007/s11704-016-6154-6
Optimizing product manufacturability in 3D printing
Yu HAN,Guozhu JIA()
School of Economics and Management, Beihang University, Beijing 100191, China
Download: PDF(352 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

3D printing has become a promising technique for industry production. This paper presents a research on the manufacturability optimization of discrete products under the influence of 3D printing technology. For this, we first model the problem using a tree structure, and then formulate it as a linear integer programming, where the total production time is to be minimized with the production cost constraint. To solve the problem, a differential evolution (DE) algorithm is developed, which automatically determines whether traditional manufacturing methods or 3D printing technology should be used for each part of the production. The algorithm is further quantitatively evaluated on a synthetic dataset, compared with the exhaustive search and alternating optimization solutions. Simulation results show that the proposed algorithm can well combine the traditional manufacturing methods and 3D printing technology in production, which is helpful to attain optimized product design and process planning concerning manufacture time. Therefore, it is beneficial to provide reference of the widely application and further industrialization of the 3D printing technology.

Keywords 3D printing      manufacturability      optimization      discrete products      differential evolution algorithm     
Corresponding Authors: Guozhu JIA   
Just Accepted Date: 19 July 2016   Online First Date: 17 October 2016    Issue Date: 06 April 2017
 Cite this article:   
Yu HAN,Guozhu JIA. Optimizing product manufacturability in 3D printing[J]. Front. Comput. Sci., 2017, 11(2): 347-357.
E-mail this article
E-mail Alert
Articles by authors
Guozhu JIA
1 Oropallo W, Piegl L A. Ten challenges in 3D printing. Engineering with Computers, 2016, 32(1): 135–148
doi: 10.1007/s00366-015-0407-0
2 Barnatt C. 3D Printing: The Next Industrial Revolution. Charleston: Create Space Independent Publishing Platform, 2013
3 Atzeni E, Salmi A. Economics of additive manufacturing for endusable metal parts. International Journal of Advanced Manufacturing Technology, 2012, 62(9–12): 1147–1156
doi: 10.1007/s00170-011-3878-1
4 Dolphin J. 3D printing: piracy or opportunity? Keeping Good Companies, 2012, 64(5): 300–303
5 Cesaretti G, Dini E, De Kestelier X, Colla V, Pambaguian L. Building components for an outpost on the lunar soil by means of a novel 3D printing technology. Acta Astronautica, 2014, 93(1): 430–450
doi: 10.1016/j.actaastro.2013.07.034
6 Yan Y, Qi H. The connotation and application of rapid manufacturing. Aviation Manufacturing Technology, 2004: 26–29
7 Lu B H, Li D C. Development of additive manufacturing (3D printing) technology. Machine Building & Automation, 2013, 42: 1–4
8 Wang H M. Materials fundamental issues of laser additive manufacturing for high-performance large metallic components. Acta Aeronautica Et Astronautica Sinica, 2014, 35: 2690–2698
9 Tuck C, Hague R, Burns N. Rapid manufacturing: impact on supply chain methodologies and practice. International Journal of Services & Operations Management, 2006, 3(1): 1–22
doi: 10.1504/IJSOM.2007.011459
10 Holmström J, Partanen J, Tuomi J, Walter M. Rapid manufacturing in the spare parts supply chain alternative approaches to capacity deployment. Journal of Manufacturing Technology Management, 2010, 21(6): 687–697
doi: 10.1108/17410381011063996
11 Nyman H J, Sarlin P. From bits to atoms: 3D printing in the context of supply chain strategies. In: Proceedings of the 47th IEEE Hawaii International Conference on System Sciences. 2014, 4190–4199
doi: 10.1109/hicss.2014.518
12 Rayna T, Striukova L. Adaptivity and rapid prototyping: how 3D printing is changing business model innovation. In: van den Berg B, van der Hof S, Kosta E, eds. 3D Printing, Vol 26. Hague: T.M.C. Asser Press, 2015, 167–182
13 Liu X L, Deng C, Lang B, Tao D C, Li X L. Query-adaptive reciprocal hash tables for nearest neighbor search. IEEE Transactions on Image Processing, 2015, 25(2): 907–919
doi: 10.1109/TIP.2015.2505180
14 Liu X L, Mu Y D, Zhang D C, Lang B, Li X L. Large-zcale unsupervised hashing with shared structure learning. IEEE Transactions on Cybernetics, 2015, 45(9): 1811–1822
doi: 10.1109/TCYB.2014.2360856
15 Nachtigall K, Voget S. A genetic algorithm approach to periodic railway synchronization. Computers & Operations Research, 1996, 23(5): 453–463
doi: 10.1016/0305-0548(95)00032-1
16 Canellidis V, Giannatsis J, Dedoussis V. Evolutionary computing and genetic algorithms: paradigm applications in 3D printing process optimization. In: Tsihrintzis G A, Virvou M, Jain L C, eds. Intelligent Computing Systems, Vol 627. Berlin: Springer-Verlag, 2016, 271–298
doi: 10.1007/978-3-662-49179-9_13
17 Das S, Suganthan P N. Differential evolution: a survey of the state-ofthe- art. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 4–31
doi: 10.1109/TEVC.2010.2059031
18 Vasile M, Minisci E, Locatelli M. An inflationary differential evolution algorithm for space trajectory optimization. IEEE Transactions on Evolutionary Computation, 2011, 15(2): 267–281
doi: 10.1109/TEVC.2010.2087026
19 Storn R, Price K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341–359
doi: 10.1023/A:1008202821328
20 Xu H, Li D. Review and outlook process planning research. Manufacturing Automation, 2008, 30: 1–7
21 Ba L, Li Y, Yang M, Liu Y. Integrated process planning and scheduling problem with consideration of assemble and transportation. Computer Integrated Manufacturing Systems, 2015, 9: 2332–2342
22 Pan X. Principle and application of concurrent engineering. Beijing: Tsinghua University Press, 1998
23 Liao W, Guo Y, Cheng X. BOM modeling based on multi-color graph. Journal of Shandong University (Engineering Science), 2008: 70–75
24 Chao Y, Yang J, Wu Z. Automatic positioning design based on graph theory. Journal of Zhejiang University (Engineering Science), 2005, 39(12): 1925–1929
25 Liu X L, He J F, Lang B, Chang S F. Hash bit selection: a unified solution for selection problems in hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2013, 1570–1577
doi: 10.1109/cvpr.2013.206
26 Shen F, Shen C, Shi Q, Hengel A, Tang Z, Shen H T. Hashing on nonlinear manifolds. IEEE Transactions on Image Processing, 2015, 24(6): 1839–1851
doi: 10.1109/TIP.2015.2405340
Related articles from Frontiers Journals
[1] Yue XIE,Ye YUAN,Xiang CHEN,Changxi ZHENG,Kun ZHOU. Continuous optimization of interior carving in 3D fabrication[J]. Front. Comput. Sci., 2017, 11(2): 332-346.
[2] Sarab ALMUHAIDEB,Mohamed El Bachir MENAI. Impact of preprocessing on medical data classification[J]. Front. Comput. Sci., 2016, 10(6): 1082-1102.
[3] Lamia SADEG-BELKACEM,Zineb HABBAS,Wassila AGGOUNE-MTALAA. Adaptive genetic algorithms guided by decomposition for PCSPs: application to frequency assignment problems[J]. Front. Comput. Sci., 2016, 10(6): 1012-1025.
[4] Chuang LIN,Min YAO,Yin LI. Joint study on VMs deployment, assignment and migration in geographically distributed data centers[J]. Front. Comput. Sci., 2016, 10(3): 559-573.
[5] Yiqiao CAI,Yonghong CHEN,Tian WANG,Hui TIAN. Improving differential evolution with a new selection method of parents for mutation[J]. Front. Comput. Sci., 2016, 10(2): 246-269.
[6] Sio Kei IM,Mohammad Mahdi GHANDI. Improved rate-distortion optimized video coding using non-integer bit estimation and multiple Lambda search[J]. Front. Comput. Sci., 2016, 10(1): 157-166.
[7] Genggeng LIU,Wenzhong GUO,Rongrong LI,Yuzhen NIU,Guolong CHEN. XGRouter: high-quality global router in X-architecture with particle swarm optimization[J]. Front. Comput. Sci., 2015, 9(4): 576-594.
[8] Jie XIN,Zhiming CUI,Pengpeng ZHAO,Tianxu HE. Active transfer learning of matching query results across multiple sources[J]. Front. Comput. Sci., 2015, 9(4): 595-607.
[9] Lijin WANG,Yilong YIN,Yiwen ZHONG. Cuckoo search with varied scaling factor[J]. Front. Comput. Sci., 2015, 9(4): 623-635.
[10] Priyanka CHAWLA,Inderveer CHANA,Ajay RANA. A novel strategy for automatic test data generation using soft computing technique[J]. Front. Comput. Sci., 2015, 9(3): 346-363.
[11] Xu YU,Jing YANG,Zhiqiang XIE. Training SVMs on a bound vectors set based on Fisher projection[J]. Front. Comput. Sci., 2014, 8(5): 793-806.
[12] Yan ZHANG,Dunwei GONG. Generating test data for both paths coverage and faults detection using genetic algorithms: multi-path case[J]. Front. Comput. Sci., 2014, 8(5): 726-740.
[13] Ruochen LIU,Chenlin MA,Fei HE,Wenping MA,Licheng JIAO. Reference direction based immune clone algorithm for many-objective optimization[J]. Front. Comput. Sci., 2014, 8(4): 642-655.
[14] Yingsheng JI,Yingzhuo ZHANG,Guangwen YANG. Interpolation oriented parallel communication to optimize coupling in earth system modeling[J]. Front. Comput. Sci., 2014, 8(4): 693-708.
[15] Peng ZHANG. Unbalanced graph cuts with minimum capacity[J]. Front. Comput. Sci., 2014, 8(4): 676-683.
Full text