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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

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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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