Skip to main content
SpringerLink
Log in

Cloud Enabled CPS and Big Data in Manufacturing

  • Conference paper
  • First Online:
Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing (AMP 2018)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

This paper presents a cloud enhanced cyber-physical system (cloud CPS) in manufacturing by combining CPS and cloud technologies. The cloud CPS is enhanced by using the combined strength of holons, agents and function blocks (FBs). Here, a holarchy of multiple holons is a sub-CPS within cloud CPS, and a logical part and a physical part are involved in each holon, and they mimic the cyber and physical entities of the CPS. They are able to be realised by agents and FBs for the manufacturing processes. In addition, to address the weakness in operation level, big data analytics (BDA) is applied to optimise machining jobs and to predict faults in scheduling. Within the processes, machining relevant factors, including workpiece, machining requirement, machine tools, cutting tool, cutting conditions, machining process and machining results, are represented by data, which is able to solve the many operational issues in cloud CPS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, S., Wan, J., Zhang, D., et al.: Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 101, 158–168 (2016). https://doi.org/10.1016/j.comnet.2015.12.017

    Article  Google Scholar 

  2. Wang, L.: Cyber manufacturing: research and applications. In: TMCE, pp. 39–49 (2014)

    Google Scholar 

  3. Shi, J., Wan, J., Yan, H., Suo, H.: A survey of cyber-physical systems. In: 2011 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6 (2011)

    Google Scholar 

  4. Derler, P., Lee, E.A., Vincentelli, A.S.: Modeling cyber-physical systems. Proc. IEEE 100, 13–28 (2012)

    Article  Google Scholar 

  5. Lee, J., Bagheri, B., Kao, H.-A.: A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015). https://doi.org/10.1016/j.mfglet.2014.12.001

    Article  Google Scholar 

  6. Wang, L., Gao, R., Ragai, I.: An Integrated Cyber-Physical System for Cloud Manufacturing. V001T04A029 (2014)

    Google Scholar 

  7. Chang, Q., Gao, R., Lei, Y., et al.: Cyber-physical systems in manufacturing and service systems. Math. Probl. Eng. 2015, 2–4 (2015). https://doi.org/10.1155/2015/704213

    Article  Google Scholar 

  8. Lee, E.A.: Cyber-Physical Systems - Are Computing Foundations Adequate? vol. 1, pp. 1–9, October 2006

    Google Scholar 

  9. Lee, E.A.: Cyber physical systems: design challenges. In: 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), pp 363–369 (2008)

    Google Scholar 

  10. Wang, L., Törngren, M., Onori, M.: Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 37, 517–527 (2015). https://doi.org/10.1016/j.jmsy.2015.04.008

    Article  Google Scholar 

  11. Wan, J., Zhang, D., Zhao, S., et al.: Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions. IEEE Commun. Mag. 52, 106–113 (2014). https://doi.org/10.1109/MCOM.2014.6871677

    Article  Google Scholar 

  12. Wan, J., Yan, H., Suo, H., Li, F.: Advances in cyber-physical systems research. KSII Trans. Internet Inf. Syst. 5, 1891–1908 (2011). https://doi.org/10.3837/tiis.2011.11.001

    Article  Google Scholar 

  13. Wang, L., Törngren, M., Onori, M.: Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 37(Part 2), 517–527 (2015). https://doi.org/10.1016/j.jmsy.2015.04.008

    Article  Google Scholar 

  14. Chua, M.S., Loh, H.T., Wong, Y.S.: Optimization of cutting conditions for multi-pass turning operations using sequential quadratic programming. J. Mater. Process. Technol. 28, 253–262 (1991). https://doi.org/10.1016/0924-0136(91)90224-3

    Article  Google Scholar 

  15. He, J.R.: Tolerancing for manufacturing via cost minimization. Int. J. Mach. Tools Manuf. 31, 455–470 (1991). https://doi.org/10.1016/0890-6955(91)90029-3

    Article  Google Scholar 

  16. Yang, Y.K., Chuang, M.T., Lin, S.S.: Optimization of dry machining parameters for high-purity graphite in end milling process via design of experiments methods. J. Mater. Process. Technol. 209, 4395–4400 (2009). https://doi.org/10.1016/j.jmatprotec.2008.11.021

    Article  Google Scholar 

  17. Chen, C.-C., Chiang, K.-T., Chou, C.-C., Liao, Y.-C.: The use of D-optimal design for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool. Int. J. Adv. Manuf. Technol. 54, 465–478 (2011). https://doi.org/10.1007/s00170-010-2964-0

    Article  Google Scholar 

  18. Li, W., Ong, S.K., Nee, A.Y.C.: Optimization of process plans using a constraint-based tabu search approach. Int. J. Prod. Res. 42, 1955–1985 (2004). https://doi.org/10.1080/00207540310001652897

    Article  MATH  Google Scholar 

  19. Wei, S.Y., Lo, C.C., Chang, C.A.: Using throughput profit for selecting manufacturing process plan. Comput. Ind. Eng. 32, 939–948 (1997). https://doi.org/10.1016/S0360-8352(97)00022-3

    Article  Google Scholar 

  20. Brown, K.N., Cagan, J.: Optimized process planning by generative simulated annealing. AI Edam-Artif. Intell. Eng. Des Anal. Manuf. 11, 219–235 (1997)

    Article  Google Scholar 

  21. Ma, G.H., Zhang, Y.F., Nee, A.Y.C.: A simulated annealing-based optimization algorithm for process planning. Int. J. Prod. Res. 38, 2671–2687 (2000). https://doi.org/10.1080/002075400411420

    Article  Google Scholar 

  22. Lee, D.H., Kiritsis, D., Xirouchakis, P.: Search heuristics for operation sequencing in process planning. Int. J. Prod. Res. 39, 3771–3788 (2001). https://doi.org/10.1080/00207540110061922

    Article  MATH  Google Scholar 

  23. Lee, D.H., Kiritsis, D., Xirouchakis, P.: Branch and fathoming algorithms for operation sequencing in process planning. Int. J. Prod. Res. 39, 1649–1669 (2001). https://doi.org/10.1080/00207540010028100

    Article  MATH  Google Scholar 

  24. Lee, D.H., Kiritsis, D., Xirouchakis, P.: Iterative approach to operation selection and sequencing in process planning. Int. J. Prod. Res. 42, 4745–4766 (2004). https://doi.org/10.1080/00207540410001720412

    Article  MATH  Google Scholar 

  25. Wang, Y.C., Chen, T.: Modelling and optimization of machining conditions for the multi-pass dry turning process. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 222, 1387–1394 (2008). https://doi.org/10.1243/09544054JEM1162

    Article  Google Scholar 

  26. Abhishek, K., Datta, S., Mahapatra, S.S.: Multi-objective optimization in drilling of CFRP (polyester) composites: application of a fuzzy embedded harmony search (HS) algorithm. Measurement 77, 222–239 (2016). https://doi.org/10.1016/j.measurement.2015.09.015

    Article  Google Scholar 

  27. Chen, M.C., Tseng, H.Y.: Machining parameters selection for stock removal turning in process plans using a float encoding genetic algorithm. J. Chin. Inst. Eng. 21, 493–506 (1998). https://doi.org/10.1080/02533839.1998.9670412

    Article  Google Scholar 

  28. Reddy, S.V.B., Shunmugam, M.S., Narendran, T.T.: Optimal sub-division of the depth of cut to achieve minimum production cost in multi-pass turning using a genetic algorithm. J. Mater. Process. Technol. 79, 101–108 (1998)

    Article  Google Scholar 

  29. Guo, Y.W., Mileham, A.R., Owen, G.W., et al.: Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 223, 485–497 (2009). https://doi.org/10.1243/09544054JEM1224

    Article  Google Scholar 

  30. Kafashi, S., Shakeri, M., Abedini, V.: Automated setup planning in CAPP: a modified particle swarm optimisation-based approach. Int. J. Prod. Res. 50, 4127–4140 (2012). https://doi.org/10.1080/00207543.2011.592157

    Article  Google Scholar 

  31. Abbas, A.T., Aly, M.F., Hamza, K.: Optimum drilling path planning for a rectangular matrix of holes using ant colony optimisation. Int. J. Prod. Res. 49, 5877–5891 (2011). https://doi.org/10.1080/00207543.2010.507608

    Article  Google Scholar 

  32. Wen, X.Y., Li, X.Y., Gao, L., Sang, H.Y.: Honey bees mating optimization algorithm for process planning problem. J. Intell. Manuf. 25, 459–472 (2014). https://doi.org/10.1007/s10845-012-0696-8

    Article  Google Scholar 

  33. Li, W.D., Ong, S.K., Nee, A.Y.C.: Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int. J. Prod. Res. 40, 1899–1922 (2002). https://doi.org/10.1080/00207540110119991

    Article  MATH  Google Scholar 

  34. Venkatesan, D., Kannan, K., Saravanan, R.: A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput. Appl. 18, 135–140 (2009). https://doi.org/10.1007/s00521-007-0166-y

    Article  Google Scholar 

  35. Wong, T.N., Chan, L.C.F., Lau, H.C.W.: Machining process sequencing with fuzzy expert system and genetic algorithms. Eng. Comput. 19, 191–202 (2003). https://doi.org/10.1007/s00366-003-0260-4

    Article  Google Scholar 

  36. Jayabal, S., Natarajan, U.: Optimization of thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites using Nelder-Mead and genetic algorithm methods. Int. J. Adv. Manuf. Technol. 51, 371–381 (2010). https://doi.org/10.1007/s00170-010-2605-7

    Article  Google Scholar 

  37. Salehi, M., Bahreininejad, A.: Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. J. Intell. Manuf. 22, 643–652 (2011). https://doi.org/10.1007/s10845-010-0382-7

    Article  Google Scholar 

  38. Tiwari, M.K., Dashora, Y., Kumar, S., Shankar, R.: Ant colony optimization to select the best process plan in an automated manufacturing environment. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 220, 1457–1472 (2006). https://doi.org/10.1243/09544054JEM449

    Article  Google Scholar 

  39. Petrovic, M., Mitic, M., Vukovic, N., Miljkovic, Z.: Chaotic particle swarm optimization algorithm for flexible process planning. Int. J. Adv. Manuf. Technol. 85, 2535–2555 (2016). https://doi.org/10.1007/s00170-015-7991-4

    Article  Google Scholar 

  40. Sardinas, R.Q., Santana, M.R., Brindis, E.A.: Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Eng. Appl. Artif. Intell. 19, 127–133 (2006). https://doi.org/10.1016/j.engappai.2005.06.007

    Article  Google Scholar 

  41. Solimanpur, M., Ranjdoostfard, F.: Optimisation of cutting parameters using a multi-objective genetic algorithm. Int. J. Prod. Res. 47, 6019–6036 (2009). https://doi.org/10.1080/00207540802178109

    Article  Google Scholar 

  42. Shin, K.S., Park, J.O., Kim, Y.K.: Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm. Comput. Oper. Res. 38, 702–712 (2011). https://doi.org/10.1016/j.cor.2010.08.007

    Article  MathSciNet  MATH  Google Scholar 

  43. El-Sawy, A.A., Abdalla, H.S.: A hybrid approach for machining process optimization using multiple experts data. Comput. Ind. Eng. 37, 445–448 (1999). https://doi.org/10.1016/S0360-8352(99)00114-X

    Article  Google Scholar 

  44. Ribeiro, M.V., Coppini, N.L.: An applied database system for the optimization of cutting conditions and tool selection. J. Mater. Process. Technol. 93, 371–374 (1999)

    Article  Google Scholar 

  45. Arezoo, B., Ridgway, K., Al-Ahmari, A.M.A.: Selection of cutting tools and conditions of machining operations using an expect system. Comput. Ind. 42, 43–58 (2000). https://doi.org/10.1016/S0166-3615(99)00051-2

    Article  Google Scholar 

  46. Gupta, D.P., Gopalakrishnan, B., Chaudhari, S.A., Jalali, S.: Development of an integrated model for process planning and parameter selection for machining processes. Int. J. Prod. Res. 49, 6301–6319 (2011). https://doi.org/10.1080/00207543.2010.523722

    Article  Google Scholar 

  47. Sluga, A., Jermol, M., Zupanic, D., Mladenic, D.: Machine learning approach to machinability analysis. Comput. Ind. 37, 185–196 (1998). https://doi.org/10.1016/S0166-3615(98)00098-0

    Article  Google Scholar 

  48. Thimm, G., Britton, G.A., Whybrew, K., Fok, S.C.: Optimal process plans for manufacturing and tolerance charting. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 215, 1099–1105 (2001). https://doi.org/10.1243/0954405011519024

    Article  Google Scholar 

  49. Wang, J.: Multiple-objective optimisation of machining operations based on neural networks. Int. J. Adv. Manuf. Technol. 8, 235–243 (1993). https://doi.org/10.1007/BF01748633

    Article  Google Scholar 

  50. Monostori, L., Viharos, Z.J., Markos, S.: Satisfying various requirements in different levels and stages of machining using one general ANN-based process model. J. Mater. Process. Technol. 107, 228–235 (2000). https://doi.org/10.1016/S0924-0136(00)00698-1

    Article  Google Scholar 

  51. Manne, A.S.: On the job-shop scheduling problem. Oper. Res. 8, 219–223 (1960). https://doi.org/10.1287/opre.8.2.219

    Article  MathSciNet  Google Scholar 

  52. Laporte, G., Salazar-GonzÁLez, J.J., Semet, F.: Exact algorithms for the job sequencing and tool switching problem. IIE Trans. 36, 37–45 (2004). https://doi.org/10.1080/07408170490257871

    Article  Google Scholar 

  53. Khoshnevis, B., Chen, Q.M.: Integration of process planning and scheduling functions. J. Intell. Manuf. 2, 165–175 (1991). https://doi.org/10.1007/bf01471363

    Article  Google Scholar 

  54. Chen, Q., Khoshnevis, B.: Scheduling with flexible process plans. Prod. Plan. Control 4, 333–343 (1993). https://doi.org/10.1080/09537289308919455

    Article  Google Scholar 

  55. Tan, W., Khoshnevis, B.: Integration of process planning and scheduling—a review. J. Intell. Manuf. 11, 51–63 (2000). https://doi.org/10.1023/a:1008952024606

    Article  Google Scholar 

  56. Mohapatra, P., Nayak, A., Kumar, S.K., Tiwari, M.K.: Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm. Int. J. Prod. Res. 53, 1712–1735 (2014). https://doi.org/10.1080/00207543.2014.957872

    Article  Google Scholar 

  57. Freitag, M., Hildebrandt, T.: Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization. CIRP Ann. - Manuf. Technol. 65, 433–436 (2016). https://doi.org/10.1016/j.cirp.2016.04.066

    Article  Google Scholar 

  58. Gahm, C., Denz, F., Dirr, M., Tuma, A.: Energy-efficient scheduling in manufacturing companies: a review and research framework. Eur. J. Oper. Res. 248, 744–757 (2016). https://doi.org/10.1016/j.ejor.2015.07.017

    Article  MathSciNet  MATH  Google Scholar 

  59. Khodke, P.M., Bhongade, A.S.: Real-time scheduling in manufacturing system with machining and assembly operations: a state of art. Int. J. Prod. Res. 51, 4966–4978 (2013). https://doi.org/10.1080/00207543.2013.784414

    Article  Google Scholar 

  60. Vieira, G.E., Herrmann, J.W., Lin, E.: Rescheduling manufacturing systems: a framework of strategies, policies, and methods. J. Sched. 6, 39–62 (2003). https://doi.org/10.1023/a:1022235519958

    Article  MathSciNet  MATH  Google Scholar 

  61. Ham, M., Lee, Y.H., Kim, S.H.: Real-time scheduling of multi-stage flexible job shop floor. Int. J. Prod. Res. 49, 3715–3730 (2011). https://doi.org/10.1080/00207543.2010.492797

    Article  Google Scholar 

  62. Iwamura, K., Okubo, N., Tanimizu, Y., Sugimura, N.: Real-time scheduling for holonic manufacturing systems based on estimation of future status. Int. J. Prod. Res. 44, 3657–3675 (2006). https://doi.org/10.1080/00207540600847129

    Article  MATH  Google Scholar 

  63. Wang, C., Ghenniwa, H., Shen, W.: Real time distributed shop floor scheduling using an agent-based service-oriented architecture. Int. J. Prod. Res. 46, 2433–2452 (2008). https://doi.org/10.1080/00207540701738052

    Article  MATH  Google Scholar 

  64. Yih, Y., Thesen, A.: Semi-Markov decision models for real-time scheduling. Int. J. Prod. Res. 29, 2331–2346 (1991). https://doi.org/10.1080/00207549108948086

    Article  MATH  Google Scholar 

  65. Zhou, R., Lee, H.P., Nee, A.Y.C.: Applying Ant Colony Optimisation (ACO) algorithm to dynamic job shop scheduling problems. Int. J. Manuf. Res. 3, 301–320 (2008). https://doi.org/10.1504/ijmr.2008.019212

    Article  Google Scholar 

  66. Metan, G., Sabuncuoglu, I., Pierreval, H.: Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining. Int. J. Prod. Res. 48, 6909–6938 (2010). https://doi.org/10.1080/00207540903307581

    Article  Google Scholar 

  67. Jain, V.K., Premalatha, S., Baskar, N.: Implementation of supervised statistical data mining algorithm for single machine scheduling. J. Adv. Manag. Res. 9, 170–177 (2012). https://doi.org/10.1108/09727981211271913

    Article  Google Scholar 

  68. Kohn, W., Brayman, V., Littleton, J.: Repair-control of enterprise systems using RFID sensory data. IIE Trans. 37, 281–290 (2005). https://doi.org/10.1080/07408170590516953

    Article  Google Scholar 

  69. Wang, C., Jiang, P.: Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops. J. Intell. Manuf. (2016). https://doi.org/10.1007/s10845-016-1194-1

  70. Zhang, Y., Jiang, P., Huang, G.: RFID-based smart Kanbans for Just-In-Time manufacturing. Int. J Mater. Prod. Technol. 33, 170–184 (2008). https://doi.org/10.1504/ijmpt.2008.01978

    Article  Google Scholar 

  71. Qu, T., Yang, H.D., Huang, G.Q., et al.: A case of implementing RFID-based real-time shop-floor material management for household electrical appliance manufacturers. J. Intell. Manuf. 23, 2343–2356 (2012). https://doi.org/10.1007/s10845-010-0476-2

    Article  Google Scholar 

  72. Wang, L., Mohammed, A., Onori, M.: Remote robotic assembly guided by 3D models linking to a real robot. CIRP Ann. Manuf. Technol. 63, 1–4 (2014). https://doi.org/10.1016/j.cirp.2014.03.013

    Article  Google Scholar 

  73. Wang, L., Wang, X.V., Gao, L., Váncza, J.: A cloud-based approach for WEEE remanufacturing. CIRP Ann. 63, 409–412 (2014). https://doi.org/10.1016/j.cirp.2014.03.114

    Article  Google Scholar 

  74. Xu, Y., Song, R., Korba, L., et al.: Distributed device networks with security constraints. IEEE Trans. Ind. Inform. 1, 217–225 (2005). https://doi.org/10.1109/TII.2005.843826

    Article  Google Scholar 

  75. Wang, L., Wong, B., Shen, W., Lang, S.: A Java 3D-enabled cyber workspace. Commun. ACM 45, 45–49 (2002). https://doi.org/10.1145/581571.581592

    Article  Google Scholar 

  76. Monostori, L.: Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17, 9–13 (2014). https://doi.org/10.1016/j.procir.2014.03.115

    Article  Google Scholar 

  77. Wang, L., Haghighi, A.: Combined strength of holons, agents and function blocks in cyber-physical systems. J. Manuf. Syst. 40, 25–34 (2016). https://doi.org/10.1016/j.jmsy.2016.05.002

    Article  Google Scholar 

  78. Ji, W., Shi, J., Liu, X., et al.: A novel approach of tool wear evaluation. J. Manuf. Sci. Eng. 139, 1–8 (2017). https://doi.org/10.1115/1.4037231

    Article  Google Scholar 

  79. Ji, W., Wang, L.: Big data analytics based optimisation for enriched process planning: a methodology. Procedia CIRP 63, 161–166 (2017). https://doi.org/10.1016/j.procir.2017.03.090

    Article  Google Scholar 

  80. Wang, L., Shen, W.: DPP: An agent-based approch for distributed process planing. J. Intell. Manuf. 14, 429–439 (2003)

    Article  Google Scholar 

  81. Wang, L., Feng, H.-Y., Cai, N.: Architecture design for distributed process planning. J. Manuf. Syst. 22, 99–115 (2003)

    Article  Google Scholar 

  82. Ji, W., Wang, L.: Big data analytics based fault prediction for shop floor scheduling. J. Manuf. Syst. 43, 187–194 (2017). https://doi.org/10.1016/j.jmsy.2017.03.008

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Production Engineering, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden

    Lihui Wang & Wei Ji

Authors
  1. Lihui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lihui Wang .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA

    Jun Ni

  2. University of Belgrade, Faculty of Mechanical Engineering, Laboratory for Production Metrology and TQM, Belgrade, Serbia

    Vidosav D. Majstorovic

  3. Department of Mechanical Engineering, University of Texas, Austin, Texas, USA

    Dragan Djurdjanovic

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, L., Ji, W. (2018). Cloud Enabled CPS and Big Data in Manufacturing. In: Ni, J., Majstorovic, V., Djurdjanovic, D. (eds) Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing. AMP 2018. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-89563-5_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-89563-5_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89562-8

  • Online ISBN: 978-3-319-89563-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation