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3DGates: An Instruction-Level Energy Analysis and Optimization of 3D Printers

Published:04 April 2017Publication History
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Abstract

As the next-generation manufacturing driven force, 3D printing technology is having a transformative effect on various industrial domains and has been widely applied in a broad spectrum of applications. It also progresses towards other versatile fields with portable battery-powered 3D printers working on a limited energy budget. While reducing manufacturing energy is an essential challenge in industrial sustainability and national economics, this growing trend motivates us to explore the energy consumption of the 3D printer for the purpose of energy efficiency. To this end, we perform an in-depth analysis of energy consumption in commercial, off-the-shelf 3D printers from an instruction-level perspective. We build an instruction-level energy model and an energy profiler to analyze the energy cost during the fabrication process. From the insights obtained by the energy profiler, we propose and implement a cross-layer energy optimization solution, called 3DGates, which spans the instruction-set, the compiler and the firmware. We evaluate 3DGates over 338 benchmarks on a 3D printer and achieve an overall energy reduction of 25%.

References

  1. 3D Printing Stringing. https://www.matterhackers.com/articles/retraction-just-say-no-to-oozing. Accessed: 2016--11--8.Google ScholarGoogle Scholar
  2. Currently available Portable 3D Printers. https://3dprint.com/tag/portable-3d-printer/. Accessed: 2016--10--22.Google ScholarGoogle Scholar
  3. International Energy Outlook 2016. http://www.eia.gov/outlooks/ieo/. Accessed: 2016-05--11.Google ScholarGoogle Scholar
  4. ONO 3D Printers. http://www.ono3d.net/. Accessed: 2016--10--22.Google ScholarGoogle Scholar
  5. TekBot Portable 3D Printers. https://3dprint.com/107375/tek-bot-portable-printer/. Accessed: 2016--10--22.Google ScholarGoogle Scholar
  6. 3D Hubs. 3D Printig Industry Trends Q3--2016. https://www.3dhubs.com/trends. Accessed: 2016--11--8.Google ScholarGoogle Scholar
  7. Adrian Bowyer. G-code. http://reprap.org/wiki/G-code. Accessed: 2016-05--22.Google ScholarGoogle Scholar
  8. J. Ajay, A. S. Rathore, C. Song, C. Zhou, and W. Xu. Don't Forget Your Electricity Bills! An Empirical Study of Characterizing Energy Consumption of 3D Printers. In ACM SIGOPS Asia-Pacific Workshop on Systems (APSys), pages 1--8, Hong Kong, China, August 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M. Anand, E. B. Nightingale, and J. Flinn. Ghosts in the machine: Interfaces for better power management. In Proceedings of the 2nd international conference on Mobile systems, applications, and services, pages 23--35. ACM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. H. Aydin and Q. Yang. Energy-aware partitioning for multiprocessor real-time systems. In Parallel and Distributed Processing Symposium, 2003. Proceedings. International, pages 9--pp. IEEE, 2003. Google ScholarGoogle ScholarCross RefCross Ref
  11. E. Bini, G. Buttazzo, J. Eker, S. Schorr, R. Guerra, G. Fohler, K.-E. Årzén, V. Romero, and C. Scordino. Resource management on multicore systems: The actors approach. IEEE Micro, 31(3):72--81, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. Brajlih, B. Valentan, J. Balic, and I. Drstvensek. Speed and accuracy evaluation of additive manufacturing machines. Rapid prototyping journal, 17(1):64--75, 2011. Google ScholarGoogle ScholarCross RefCross Ref
  13. J. A. Butts and G. S. Sohi. A static power model for architects. In Proceedings of the 33rd annual ACM/IEEE international symposium on Microarchitecture, pages 191--201. ACM, 2000.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. T. Campbell, C. Williams, O. Ivanova, and B. Garrett. Could 3d printing change the world. Technologies, Potential, and Implications of Additive Manufacturing, Atlantic Council, Washington, DC, 2011.Google ScholarGoogle Scholar
  15. A. Carroll and G. Heiser. An analysis of power consumption in a smartphone. In USENIX annual technical conference, volume 14. Boston, MA, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. L. G. Cima and M. J. Cima. Preparation of medical devices by solid free-form fabrication methods. Robotics and Computer Integrated Manufacturing, 4(12):371, 1996.Google ScholarGoogle Scholar
  17. CNC Cookbook. CNC-code. http://www.cnccookbook.com/CCCNCGCodeRef.html. Accessed: 2016-05-22.Google ScholarGoogle Scholar
  18. G. Contreras and M. Martonosi. Power prediction for intel xscale® processors using performance monitoring unit events. In ISLPED'05. Proceedings of the 2005 International Symposium on Low Power Electronics and Design, 2005., pages 221--226. IEEE, 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Cura. 3D Printing Slicing Software. https://ultimaker.com/en/products/cura-software. Accessed: 2016-05--22.Google ScholarGoogle Scholar
  20. A. Emadi, K. Rajashekara, S. S. Williamson, and S. M. Lukic. Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations. IEEE Transactions on Vehicular Technology, 54(3):763--770, 2005. Google ScholarGoogle ScholarCross RefCross Ref
  21. D. Frear and P. Vianco. Intermetallic growth and mechanical behavior of low and high melting temperature solder alloys. Metallurgical and Materials Transactions A, 25(7):1509--1523, 1994. Google ScholarGoogle ScholarCross RefCross Ref
  22. J. F. Gieras. Permanent magnet motor technology: design and applications. CRC press, 2002.Google ScholarGoogle Scholar
  23. D. Godlinski and S. Morvan. Steel parts with tailored material gradients by 3d-printing using nano-particulate ink. In Materials Science Forum, volume 492, pages 679--684. Trans Tech Publ, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  24. M. Greulich, M. Greul, and T. Pintat. Fast, functional prototypes via multiphase jet solidification. Rapid Prototyping Journal, 1(1):20--25, 1995. Google ScholarGoogle ScholarCross RefCross Ref
  25. H. Hoffmann, S. Sidiroglou, M. Carbin, S. Misailovic, A. Agarwal, and M. Rinard. Dynamic knobs for responsive power-aware computing. In ACM SIGPLAN Notices, volume 46, pages 199--212. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. J. Howard, S. Dighe, Y. Hoskote, S. Vangal, D. Finan, G. Ruhl, D. Jenkins, H. Wilson, N. Borkar, G. Schrom, et al. A 48-core ia-32 message-passing processor with dvfs in 45nm cmos. In 2010 IEEE International Solid-State Circuits Conference-(ISSCC), pages 108--109. IEEE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  27. S. E. Hudson. Printing teddy bears: A technique for 3d printing of soft interactive objects. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '14, pages 459--468, New York, NY, USA, 2014. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. G. Jin, W. Li, C. Tsai, and L. Wang. Adaptive tool-path generation of rapid prototyping for complex product models. Journal of manufacturing systems, 30(3):154--164, 2011. Google ScholarGoogle ScholarCross RefCross Ref
  29. I. Jolliffe. Principal component analysis. Wiley Online Library, 2002.Google ScholarGoogle Scholar
  30. R. Joseph and M. Martonosi. Run-time power estimation in high performance microprocessors. In Proceedings of the 2001 international symposium on Low power electronics and design, pages 135--140. ACM, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. W. Kim, M. S. Gupta, G.-Y. Wei, and D. Brooks. System level analysis of fast, per-core dvfs using on-chip switching regulators. In 2008 IEEE 14th International Symposium on High Performance Computer Architecture, pages 123--134. IEEE, 2008.Google ScholarGoogle Scholar
  32. K. Klues, V. Handziski, C. Lu, A. Wolisz, D. Culler, D. Gay, and P. Levis. Integrating concurrency control and energy management in device drivers. In ACM SIGOPS Operating Systems Review, volume 41, pages 251--264. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. S. H. Ko, J. Chung, N. Hotz, K. H. Nam, and C. P. Grigoropoulos. Metal nanoparticle direct inkjet printing for low-temperature 3d micro metal structure fabrication. Journal of Micromechanics and Microengineering, 20(12):125010, 2010. Google ScholarGoogle ScholarCross RefCross Ref
  34. C. X. F. Lam, X. Mo, S.-H. Teoh, and D. Hutmacher. Scaffold development using 3d printing with a starch-based polymer. Materials Science and Engineering: C, 20(1):49--56, 2002. Google ScholarGoogle ScholarCross RefCross Ref
  35. H.-P. Le, J. Crossley, S. R. Sanders, and E. Alon. A sub-ns response fully integrated battery-connected switched-capacitor voltage regulator delivering 0.19 w/mm 2 at 73% efficiency. In 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers, pages 372--373. IEEE, 2013.Google ScholarGoogle Scholar
  36. W. Lei and Y. Hsu. Accuracy test of five-axis cnc machine tool with 3d probe--ball. part i: design and modeling. International Journal of Machine Tools and Manufacture, 42(10):1153--1162, 2002. Google ScholarGoogle ScholarCross RefCross Ref
  37. S. Lensgraf and R. R. Mettu. Beyond layers: A 3d-aware toolpath algorithm for fused filament fabrication. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 3625--3631, May 2016. Google ScholarGoogle ScholarCross RefCross Ref
  38. J. Liu and M. Rynerson. Method for article fabrication using carbohydrate binder, July 1 2003. US Patent 6,585,930.Google ScholarGoogle Scholar
  39. M. C. Melican, M. C. Zimmerman, M. S. Dhillon, A. R. Ponnambalam, A. Curodeau, and J. R. Parsons. Three-dimensional printing and porous metallic surfaces: A new orthopedic application. Journal of biomedical materials research, 55(2):194--202, 2001. Google ScholarGoogle ScholarCross RefCross Ref
  40. W. U. P. Meters. Watts up? .Net Power Meter Specifications. https://www.wattsupmeters.com/secure/products.php?pn=0&wai=0&spec=2. Accessed: 2016-05--22.Google ScholarGoogle Scholar
  41. J. Moon, A. C. Caballero, L. Hozer, Y.-M. Chiang, and M. J. Cima. Fabrication of functionally graded reaction infiltrated sic--si composite by three-dimensional printing (3dp) process. Materials Science and Engineering: A, 298(1):110--119, 2001. Google ScholarGoogle ScholarCross RefCross Ref
  42. N. Nethercote and J. Seward. Valgrind: a framework for heavyweight dynamic binary instrumentation. In ACM Sigplan notices, volume 42, pages 89--100. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. OhmEye. Print fans and hotend heaters. http://ohmeye.com/2015/print-fans-and-hotend-heaters/. Accessed: 2016--11--8.Google ScholarGoogle Scholar
  44. M. Paterson and V. Dančík. Longest common subsequences. In International Symposium on Mathematical Foundations of Computer Science, pages 127--142. Springer, 1994. Google ScholarGoogle ScholarCross RefCross Ref
  45. A. Pathak, Y. C. Hu, and M. Zhang. Where is the energy spent inside my app?: fine grained energy accounting on smartphones with eprof. In Proceedings of the 7th ACM european conference on Computer Systems, pages 29--42. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. A. Pathak, Y. C. Hu, M. Zhang, P. Bahl, and Y.-M. Wang. Fine-grained power modeling for smartphones using system call tracing. In Proceedings of the sixth conference on Computer systems, pages 153--168. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. T. Peng. Analysis of energy utilization in 3d printing processes. Procedia CIRP, 40:62--67, 2016. Google ScholarGoogle ScholarCross RefCross Ref
  48. Pronterface. Pronterface Website. http://www.pronterface.com/. Accessed: 2016--11--8.Google ScholarGoogle Scholar
  49. V. J. Reddi, A. Settle, D. A. Connors, and R. S. Cohn. Pin: a binary instrumentation tool for computer architecture research and education. In Proceedings of the 2004 workshop on Computer architecture education: held in conjunction with the 31st International Symposium on Computer Architecture, page 22. ACM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. G. Strano, L. Hao, R. Everson, and K. Evans. A new approach to the design and optimisation of support structures in additive manufacturing. The International Journal of Advanced Manufacturing Technology, 66(9--12):1247--1254, 2013.Google ScholarGoogle Scholar
  51. J. Suwanprateeb. Improvement in mechanical properties of three-dimensional printing parts made from natural polymers reinforced by acrylate resin for biomedical applications: a double infiltration approach. Polymer international, 55(1):57--62, 2006. Google ScholarGoogle ScholarCross RefCross Ref
  52. Q. Tang, S. K. S. Gupta, and G. Varsamopoulos. Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach. IEEE Transactions on Parallel and Distributed Systems, 19(11):1458--1472, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. F. A. Thabtah, P. Cowling, and Y. Peng. Mmac: A new multi-class, multi-label associative classification approach. In Data Mining, 2004. ICDM'04. Fourth IEEE International Conference on, pages 217--224. IEEE, 2004. Google ScholarGoogle ScholarCross RefCross Ref
  54. Thingverse. Digital Designs for Physical Objects. http://www.thingiverse.com/. Accessed: 2016-05--22.Google ScholarGoogle Scholar
  55. V. Tiwari, S. Malik, A. Wolfe, and M. T.-C. Lee. Instruction level power analysis and optimization of software. In Technologies for wireless computing, pages 139--154. Springer, 1996. Google ScholarGoogle ScholarCross RefCross Ref
  56. Ultimaker Inc. Ultimaker 2 Go. https://ultimaker.com/en/products/ultimaker-2-go. Accessed: 2016-05--22.Google ScholarGoogle Scholar
  57. Ultimaker Inc. Ultimaker 2 Go Components Specification. https://github.com/Ultimaker/Ultimaker2. Accessed: 2016-05--22.Google ScholarGoogle Scholar
  58. S. Vinodh, G. Sundararaj, S. Devadasan, D. Kuttalingam, and D. Rajanayagam. Agility through rapid prototyping technology in a manufacturing environment using a 3d printer. Journal of Manufacturing Technology Management, 20(7):1023--1041, 2009. Google ScholarGoogle ScholarCross RefCross Ref
  59. N. Volpato, R. Nakashima, L. Galv\ ao, A. Barboza, P. Benevides, and L. Nunes. Reducing repositioning distances in fused deposition-based processes using optimization algorithms. In High Value Manufacturing: Advanced Research in Virtual and Rapid Prototyping: Proceedings of the 6th International Conference on Advanced Research in Virtual and Rapid Prototyping, Leiria, Portugal, 1-5 October, 2013, page 417. CRC Press, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  60. G. Von Laszewski, L. Wang, A. J. Younge, and X. He. Power-aware scheduling of virtual machines in dvfs-enabled clusters. In 2009 IEEE International Conference on Cluster Computing and Workshops, pages 1--10. IEEE, 2009. Google ScholarGoogle ScholarCross RefCross Ref
  61. S. Walls, J. Corney, A. Vasantha, and G. Vijayumar. Relative energy consumption of low-cost 3d printers. In 12th International Conference on Manufacturing Research, 2014.Google ScholarGoogle Scholar
  62. L. Wang, G. Von Laszewski, J. Dayal, and F. Wang. Towards energy aware scheduling for precedence constrained parallel tasks in a cluster with dvfs. In Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on, pages 368--377. IEEE, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Wikipedia. G-code. https://en.wikipedia.org/wiki/G-code. Accessed: 2016-11-8.Google ScholarGoogle Scholar
  64. T. Wohlers. Wohlers report 2016. Wohlers Associates, Inc, 2016.Google ScholarGoogle Scholar
  65. C. Wright, A. Buchan, B. Brown, J. Geist, M. Schwerin, D. Rollinson, M. Tesch, and H. Choset. Design and architecture of the unified modular snake robot. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 4347--4354. IEEE, 2012. Google ScholarGoogle ScholarCross RefCross Ref
  66. C. Xu, F. X. Lin, Y. Wang, and L. Zhong. Automated os-level device runtime power management. ACM SIGARCH Computer Architecture News, 43(1):239--252, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis, pages 105--114. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

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