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Optimizing energy savings of the injection molding process by using a cloud energy management system

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Abstract

The injection molding (IM) process is a widely used manufacturing process for injecting material into a mold for producing a diverse array of parts. It includes several energy-consuming procedures, such as heating plastic pellets, forcing melted polymer into a mold cavity, and cooling down the molded products. In this study, developmental factors of IM machines and processes along with energy savings progress are reviewed. In addition to several machining factors and process parameter optimizations, applying an energy management system (EMS), as well as new tools to reduce energy consumption in the IM process, has the potential for great improvements in the long term. A cloud energy management system (CEMS), called the intelligent Energy Management Network (iEN), which was launched by Chunghwa Telecom, was installed on two IM machines to illustrate the optimization of energy savings by a variable-frequency drive (VFD) and process parameter optimization. Through the recorded process dynamics, the energy usage, and product quality of the IM process using the iEN, the energy savings could be analyzed by the expert, measurement and verification (M&V) systems on the software as a service (SaaS) platform. The electricity savings on the IM machine after installing a VFD were 41.3%. Further optimization by using the one-factor-at-a-time (OFAT) approach to measure the process parameters, such as melting temperature (310.0~350.0 °C), mold temperature (110.0~130.0 °C), and clamping force (120.0~160.0 T), was carried out. The experimental and analyzed results indicated that the optimal operating conditions were at a melting temperature of 330.0 °C, a mold temperature of 120.0 °C, and a clamping force of 140.0 T. Through the optimization procedure of the process parameters carried out by the iEN, further electricity savings of 12.2% were added. Therefore, the saved electricity cost and payback period of installing the VFD and the iEN were NT$ 26,363/month and within 4 months, respectively. The saved electricity and reduced carbon dioxide (CO2) amounts were 107,200.5 kWh/year and 55,851.5 kg/year, respectively. Continuous analysis of the optimization process, energy savings, resource conservation, and waste reduction of the IM process using the iEN has shown overall benefits to the IM process, the machines, and the future decisions and designs regarding new products.

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Abbreviations

ANN:

artificial neural network

CEMS:

cloud energy management system

EMS:

energy management system

GA:

genetic algorithm

IaaS:

infrastructure as a service

iEN:

intelligent energy management network

IM process:

injection molding process

M&V systems:

measurement and verification systems

OFAT:

one factor at a time

PaaS:

platform as a service

SaaS:

software as a service

VLANs:

virtual local area networks

VDP:

variable-displacement pump

VFD:

variable-frequency driver

VSD:

variable-speed drive

References

  • Altan, M. (2010). Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Materials & Design, 31(1), 599–604.

    Article  Google Scholar 

  • ARBURG (2016). ARBURG—an energy-efficient company. http://www.cfic.com.tw/pages/downloads/news/ arburg_an_energy_efficient_company.pdf. Accessed 10 Jun 2016.

  • Bayer Material Science LLC (2007). Makrolon® 2405, 2407 and 2456. http://www.ledil.com/sites/default/files/Documents/Technical/Material/PC%20Makrolon%202405_2407_2456-Datasheet.pdf. Accessed 10 Mar 2017.

  • Bharti, P. K., Khan, M. I., & Singh, H. (2010). Recent methods for optimization of plastic injection molding process—a retrospective and literature review. International Journal of Engineering Science and Technology 2010, 2(9), 4540–4554.

    Google Scholar 

  • Black, F., & Scholes, M. (1973). The pricing of option and corporate liabilities. Journal of Political Economy, 81, 637–659.

    Article  MathSciNet  MATH  Google Scholar 

  • Bolur, P. C. (2000). A guide to injection moulding of plastics. New Delhi: Allied Publishers Limited.

    Google Scholar 

  • Capehart, B. L., Muth, E. J., & Storin, M. O. (1982). Minimizing residential electrical energy costs using microcomputer energy management systems. Computers and Industrial Engineering, 6, 261–269.

    Article  Google Scholar 

  • Chen, Z. B., & Turng, L. S. (2005). A review of current developments in process and quality control for injection molding. Advances in Polymer Technology, 24(3), 165–182.

    Article  Google Scholar 

  • Chen, W. C., Nguyen, M. H., Chiu, W. H., Chen, T. N., & Tai, P. H. (2016). Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO. International Journal of Advanced Manufacturing Technology, 83, 1873–1886.

    Article  Google Scholar 

  • Cheng, C. C., Lee, D. S., Wang, C. H., Lin, S. F., Chang, H. P., & Fang, S. T. (2015). The development of cloud energy management. Energies, 8, 4357–4377.

    Article  Google Scholar 

  • Department of Energy, U.S. (2007). International performance measurement and verification protocol: concepts and options for determining energy and water savings (Vol. 1, pp. 16–19). Oak Ridge: U.S. Department of Energy.

    Google Scholar 

  • Dow Chemical Company (2011). Dow launches ELITE™ advanced technology polyethylene resins. http://www.dow.com/polyethylene/global_news/2011/20110404a.htm. Accessed 10 Mar 2017.

  • Dublin (2015). Global and China injection molding machine industry report 2015. Research and Markets. http://www.prnewswire.com/news-releases/global-and-china-injection-molding-machine-industry-report-2015-300055001.html. Accessed 10 Jun 2016.

  • Energy Technology Support Unit and British Plastics Federation (ETSU&BPF) (1999). Good practice guide 292: energy in plastics processing—a practical guide. http://www.tangram.co.uk/TI-Energy_in_Plastics_Processing_(GPG292).pdf. Accessed 10 Mar 2017.

  • Fiedler, T., & Mircea, P. M. (2012). Energy management systems according to the ISO 50001 standard—challenges and benefits. 2012 International Conference on Applied and Theoretical Electricity (ICATE), Craiova, Romania, pp. 1–4.

  • Godec, D., Rujnic-Sokele, M., & Šercer, M. (2012). Processing parameters influencing energy efficient injection moulding of plastics and rubbers. Polimeri., 33, 112–117.

    Google Scholar 

  • Goodship, V. (2004). Arburg practical guide to injection moulding. Shrewsbury: Rapra Technology Limited.

    Google Scholar 

  • Gutowski, T., Dahmus, J., & Thiriez, A. (2006). Electrical energy requirements for manufacturing processes. Proceedings of the 13th CIRP International Conference on Life Cycle Engineering.

  • Hordeski, M. F. (2004). Dictionary of energy efficiency technologies. Lilburn: Fairmont Press, Inc..

    Google Scholar 

  • Intelligent Energy-Europe (2006). Reduced energy consumption in plastics engineering, low energy plastics processing: European best practice guide. http://www.tangram.co.uk/TI-Energy-Low%20Energy%20Plastics%20Processing%20(e).pdf. Accessed 10 Mar 2017.

  • Kazmer, D. O. (2011). Design of plastic parts. In Applied plastics engineering handbook: processing and materials. Waltham: William Andrew Publishing.

    Google Scholar 

  • Kent, R. (2008a). Energy management in plastics processing: strategies, targets, techniques and tools. Bristol: Plastics Information Direct.

    Google Scholar 

  • Kent, R. (2008b). Energy management in plastics processing—framework for measurement, assessment and prediction. Plastics, Rubber and Composites, 37(2–4), 96–104.

    Article  Google Scholar 

  • Kurtaran, H., & Erzurumlu, T. (2006). Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. The International Journal of Advanced Manufacturing Technology, 27(5), 468–472.

    Article  Google Scholar 

  • Lee, D. S. (2008). Energy harvesting chip and the chip based power supply development for a wireless sensor network. Sensors, 8, 7690–7714.

    Article  Google Scholar 

  • Lee, Y., Wong, C. T., Po, G., & Chen, F. F. (2008). CN100415488 C.

  • Ma, S. (2012). A review on cloud computing development. Journal of Networks, 7, 305–309.

    Google Scholar 

  • Madan, J., Mani, M., & Lyons, K. W. (2013). Characterizing energy consumption of the injection molding process. Proceedings of the ASME 2013 Manufacturing Science and Engineering Conference, pp. V002T04A015.

  • Myers, J. A., Ruberg, M., Waterfield, R., Elsass, M., & Kelsay, S. (2008). Experimental study on the energy efficiency of different screw designs for injection moulding. Proceedings of the Society of Plastics Engineers Annual Technical (ANTEC) Conference.

  • Pillai, M. K. G., Ramakrishna, V., & Agrawai, V. K. (1995). Real time data acquisition and energy management system using distributed computer architecture. IEEE/IAS International Conference on Industrial Automation and Control, pp. 427–434.

  • Qureshi, F., Li, W., Kara, S., & Herrmann, C. (2012). Unit process energy consumption models for material addition processes: a case of the injection molding process. In D. A. Dornfeld & B. S. Linke (Eds.), Leveraging technology for a sustainable world (pp. 269–274). Berlin: Springer.

    Chapter  Google Scholar 

  • Rahman, S., & Bhatnagar, R. (1986). Computerized energy management systems—why and how. Journal of Microcomputer Applications, 9, 261–270.

    Article  Google Scholar 

  • Rosato, D. V., Rosato, D. V., & Rosato, M. G. (2000). Injection molding handbook. Norwell: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Rosato, D. V., Rosato, D. V., Rosato, M. G., & Schott, N. R. (2001). Plastics engineering manufacturing and data handbook/Plastics Institute of America. Norwell: Kluwer Academic Publishers.

    Book  MATH  Google Scholar 

  • Rusdi, M. S., Abdullah, M. Z., Mahmud, A. S., Khor, C. Y., Abdul Aziz, M. S., Ariff, Z. M., & Abdullah, M. K. (2016). Numerical investigation on the effect of pressure and temperature on the melt filling during injection molding process. Arabian Journal for Science and Engineering, 41, 1907–1919.

    Article  Google Scholar 

  • Shen, Y. K., Chien, H. W., & Lin, Y. (2004). Optimization of the micro-injection molding process using Grey relational analysis and moldflow analysis. Journal of Reinforced Plastics and Composites, 23(17), 1799–1814.

    Article  Google Scholar 

  • Sjoberg, S., Hedberg, T., Selberg, L., & Wikstrom, R. (2000). Implementing a nationwide energy management system. 2000 International Telecommunications Energy Conference, pp. 163–166.

  • Taylor, B. F., Womer, T. W., & Kadykowski, R. (2007). Efficiency gains and control improvements using different barrel heating technologies for the injection molding process. Proceedings of the 2007 Society of Plastics Engineers Annual Technical (ANTEC) Conference, p. 2429.

  • Velazquex, D., Gonzalez-Falcon, R., Perez-Lombard, L., Gallego, L. M., Monedero, I., & Biscarri, F. (2013). Development of an energy management system for a naphtha reforming plant: a data mining approach. Energy Conversion and Management, 67, 217–225.

    Article  Google Scholar 

  • Wang, F.Y., & Chen, A. (2016). Energy management handbook. Business for social responsibility. Available online: http://www.bsr.org/reports/bsr-energy-management-handbook.pdf. Accessed 10 Jun 2016.

  • Weissman, A., Ananthanarayanan, A., Gupta, S. K., & Sriram, R. D. (2010). A systematic methodology for accurate design-stage estimation of energy consumption for injection molded parts. Proceedings of the ASME 2010 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference 2010, pp. DETC2010–28889.

  • Xu, Y., Zhang, Q. W., Zhang, W. H., & Zhang, P. (2015). Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact. International Journal of Advanced Manufacturing Technology, 76, 2199–2208.

    Article  Google Scholar 

  • Yin, K. H. (2015). Dynamic optimisation for energy efficiency of injection moulding process. PhD thesis, University of Nottingham, pp. 31–67.

  • Zhang, Y. G. (2008). Analysis of energy-saving technique for injection molding machine—part two (in Chinese). China Rubber/ Plastics Technology and Equipment, 34(4), 57–65.

    Google Scholar 

Download references

Acknowledgments

Special thanks to Prof. Dasheng Lee for the useful discussions. The authors would like to acknowledge the cooperation project of “iEN, the cloud energy management service” provided by ChungHwa Telecom, Taiwan.

Funding information

The authors would like to acknowledge the financial support from the Ministry of Science and Technology (project number: NSC 103-2622-E-027-001), the Foundation of Taiwan Industry Service, and the Institute for Information Industry (Ministry of Economy Affairs, Republic of China).

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Authors and Affiliations

  1. Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd, Taipei, 10608, Taiwan

    Chin-Chi Cheng & Kang-Wei Liu

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  1. Chin-Chi Cheng
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Correspondence to Chin-Chi Cheng.

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Cheng, CC., Liu, KW. Optimizing energy savings of the injection molding process by using a cloud energy management system. Energy Efficiency 11, 415–426 (2018). https://doi.org/10.1007/s12053-017-9574-8

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