Available online at www.sciencedirect.com
ScienceDirect Procedia CIRP 69 (2018) 656 – 661
25th CIRP Life Cycle Engineering (LCE) Conference, 30 April – 2 May 2018, Copenhagen, Denmark
An IoT-enabled approach for energy monitoring and analysis of die casting machines Weipeng Liua, Renzhong Tanga*, Tao Penga a
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
* Corresponding author. Tel.: +86-0571-8795-2048; E-mail address:
[email protected]
Abstract Die casting machines, widely used in manufacturing industry, consume a significant amount of energy. To reduce energy consumption, the primary task is to accurately characterize and evaluate the current performance. The ability to access energy-related data and, more importantly, effectively analyze these data to obtain key indicators is critical. In this paper, an Internet of Things (IoT) enabled method is proposed to stream online energy data for energy analysis of a die casting machine. The energy data captured by digital power meters and PLCs was transferred to a central server using real-time Ethernet. A set of indicators, including energy per part and energy per action, were developed to interpret the data and to evaluate the performance of a die casting machine. The feasibility of the developed energy monitoring and analysis approach was examined in a case study. Based on the results, several potential ways of energy savings were suggested. © Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ©201 2017The The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 25th CIRP Life Cycle Engineering (LCE) Conference. Peer-review under responsibility of the scientific committee of the 25th CIRP Life Cycle Engineering (LCE) Conference
Keywords: Die casting machines; Energy efficiency; Internet of Things
1. Introduction As an important stage of a life cycle, manufacturing accounts for nearly one third of energy consumption and approximately 25% of energy-related carbon dioxide (CO2) emission globally [1,2]. Sustainable manufacturing has become an area of increasing interests, which aims for more environmental friendliness [3]. The first step towards this is to effectively evaluate the sustainability of manufacturing processes. Die casting is one of the oldest and most energy-intensive manufacturing processes [4]. 90% of manufactured products in the United States contain die-casted components [5]. Approximately 25% of the cost of die casting is attributed to energy consumption [6,7]. Existing research suggested that increasing resource utilization could save energy consumption of a die casting process by 20 to 30 percent [8]. More importantly, improving its energy efficiency (EE) significantly contributes to both sustainability improvement and manufacturing cost savings.
In a die casting process, around 50% of the energy is consumed in melting and holding as a furnace usually runs 24 hours 7 days. The second largest energy is consumed in casting process, which accounts for 30%, and a die casting machine is the key energy consumer in this process. The rest is consumed in fetching, trimming, and so on [9]. There are many studies on energy consumption and EE improvement of the melting and holding, such as Constrained Rapid Induction Melting Single Shot Up-Casting [10,11]. However, few have been done to evaluate these of die casting machines. Thus, this work aims to bridge the gap by monitoring and evaluating the energy consumption of die casting machines. One of the biggest barriers for evaluating the energy consumption of die casting machines is the difficulty in collecting energy data [12,13]. The ability to overcome such a difficulty is crucial for successful energy management. Internet-of-Things (IoT) helps to connect physical devices embedded with sensors to the internet, enabling ubiquitous data collection. Such a technology has been used in many applications. For energy monitoring and EE analysis, Juergen
2212-8271 © 201 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 25th CIRP Life Cycle Engineering (LCE) Conference doi:10.1016/j.procir.2017.11.109
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et al. [14] employed an online method to evaluate the EE of machine tool operations, which revealed the energy consumption and EE of each component. Eberhard et al. [15] presented a standardized approach for energy data collection of machines and their components on the PLCs, and developed an approach for empowering PLCs with energy-optimal control functions. Yee et al. [16] introduced an IoT-enabled software application for real-time monitoring of EE at shop floor. Yen et al. [17] presented an IoT-enabled approach for real-time waste monitoring and analysis, and discovered waste reduction potentials at shop floor. Enabled by IoT, huge amounts of real-time energy and operation data of die casting machines can be acquired. Subsequently, energy analysis for die casting machines can be performed in a central server, and the results can be visualized by a worker or manager using a webpage in a timely fashion. Energy optimization can then be applied. The remainder of this paper is organized as follows. In Section 2, the technological processes and actions of die casting machines are described. Indicators are proposed to evaluate the energy performance as well. The approach to capturing and analyzing the energy data is introduced in Section 3. A case study is conducted to demonstrate the feasibility of the approach, and the preliminary results of the implementation is presented in Section 4. Brief discussions, conclusions, and future works are given in Section 5. 2. Die casting machine process There are two types of die casting machines: hot chamber and cold chamber. Hot chamber machines are used to cast parts made of low melting temperature metals alloys, such as zinc and tin. Cold chamber machines are used to cast parts made of metal alloys with high melting temperatures, such as aluminum and brass [3]. Among the metals that are casted, aluminum is a very important one. Specifically, aluminum casting has experienced continuous growth and dominates the nonferrous sector in general, comprising 78% of total nonferrous shipments [6]. Aluminum production consumes 3.5% of global electricity and causes about 1% of global CO2 emission [18]. Therefore, the research object of this paper is the cold chamber machines. 2.1. Technical description of die casting machine process Die casting is a cyclical process. Each cycle follows the same sequence that is depicted in Fig.1. The three bold rectangular boxes indicating feeding, fetching, and spraying respectively, represent the processes supplied by other auxiliary equipment. The four dashed rectangular boxes represent that those processes are not indispensable.
Fig. 1.The technical process of cold-chamber die casting machines.
As soon as the door is closed, the machine starts to lock the die by four phases: slow moving, quick moving, low pressure clamping, and high pressure clamping. Afterwards, the machine starts the first energy accumulation and waits for the filling of liquid metal into shot chamber by feeding machine. When the above processes finished, a plunger will squeeze the molten metal into the cavity, followed by several optional phases including slow speed injection, first speed injection, second speed injection, and pressurization injection. The molten metal remains in the die cavity until the metal solidifies. Once the metal solidifies, the die will be opened by the clamping unit by two phases: high pressure opening and quick moving. There are two ways by which a cast could get out of a die. If the plunger trace is chosen, the plunger would move forward a little to push the cast out of the static die simultaneously with high pressure opening the die. Then the plunger would move back when the cast is ejected. If the plunger trace is not chosen, the plunger would move back at the same time with opening the die. Through two actions, ejector out and ejector back, an ejection system will force the cast out of the die. Once the part is ejected, a fetching machine would pick it up. Finally, lubricant is sprayed on the surface of the die, the machine starts the second energy accumulation, and the plunger would be lubricated. Then the next cycle will start.
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2.2. Energy consumption indicators of die casting machines For efficiently understanding and characterizing the energy consumption of a die casting machine, some indicators are proposed to reveal the performance. The amount of energy required for processing a part (energy per part) is a crucial indicator to both the green property of die casting machine and the cost. Besides, energy per volume (m3), the amount of energy required for processing one cubic meter material of a part, and energy per weight (kg), the amount of energy required for processing one kilogram material of a part, are introduced to interpret the performance of the die casting machine. These indicators can not only clearly reveal the EE performance of a machine but provide necessary data for the carbon footprint assessment of a part. For further understanding and assessing the energy consumption of a die casting machine, the total energy consumption is divided by actions. Energy per action is defined as the amount of energy consumed by one of actions for processing a part. Energy per action can show the energy consumption of a component of a die casting machine and reveal the potential for energy reduction. The reduction can be achieved by substituting a more efficiency component for the old one. Besides, actions strongly depend on the parameter setting of a die casting machine, so the energy per action may be reduced by optimizing the parameters of a die casting machine. The technical processes of die casting machines are described and analyzed incisively in the previous section. It is necessary to divide them into many basic actions, taking the characteristic and energy consumption of per process into consideration. In this paper, the processes are divided into seven categories consisting of standby, locking die, opening die, injection, ejection, loose core, and plunger actions. The descriptions and analyses of those base actions are the following. x Standby The standby state, is also called basic action, is a state that some basic devices such as PLC keep running and stay ready to work. x Locking die The locking die action which consists of four phases: slow speed, quick speed, low pressure, and high pressure, is to drive the movable mold moving until locked with the static mold. . x Opening die The opening die action which consists of two phases: high pressure and quick speed, is to drive the movable mold moving back. x Injection The injection generally including four phases: slow injection, first speed injection, second speed injection, and pressurization injection is to squeeze the melting metal into the cavity. x Loose core The loose core action includes loose core insertion and extraction. The loose core insertion is to insert loose core into mold. The loose core extraction is to pull out loose core from mold. x Ejection
The ejection action includes two phases: ejector out and ejector back. The ejector out is to push out the cast from the movable die and ejector back is to pull back the ejector. x Plunger actions The plunger actions include plunger tracing which move up to push out the product at almost the same time with high pressure opening die, plunger lubrication, and plunger return. 3. Energy data acquisition and analysis For evaluating the energy consumption of those actions which are described in the previous section, it is necessary to capture and analyze the power and operation data of die casting machines. The approach of data capturing and analysis is introduced in the next paragraphs. 3.1. Energy data acquisition The real-time power data can be accessed by a Power Meter. The real-time operation data including current action, current pressure, current flow, die position, and plunger position is accessed through a PLC. The data capturing architecture of die casting machines is depicted in Fig.2. For accessing and storing the real-time power data, a power meter should contain a communication module and most of the power meters support the Modbus protocol. Connected with each other by serial line, the power meter and an industrial personal computer can exchange data according to communication protocol. Though the communication protocol varies with different PLC suppliers, the majority of PLCs supports the Ethernet way to exchange data with host computer. So the real-time operation data can be accessed through the Ethernet. The industrial personal computer, which exchanges data with the power meter and PLC, sends the data to the central server. The central server processes the data in real time and display to the user by web service.
Fig. 2. The data capturing architecture for die casting machines.
3.2. Energy data analysis The acquisition energy data including power and operation data consists of value and timestamp. We can describe those data in the time domain.
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The notations of the variables in the Equations are listed below: x t1 (s)denotes the start time of a cycle x t2 (s)denotes the end time of a cycle x P(t) (w)tę[t1, t2] denotes the power at time t x A(t) tę[t1, t2] denotes the action at time t x ݊ denotes the action of die casting machines x tns (s)denotes the start time of n-th action x tne (s)denotes the end time of n-th action x ܧpart (J)denotes the energy per part x ܧpart_v (J/m3)denotes the energy per volume x ܧpart_w (J/kg)denotes the energy per weight x ܧn_action (J)denotes the energy per action x m(kg) denotes the weight of per part x ȡ NJP3) denotes the material density of part As stated in Equation 1, the energy consumption of per part is calculated. The ܧpart_w and ܧpart_v can be calculated by Equation 2 and Equation 3 respectively.
Fig. 3. Five-star feet.
The power function P(t) of producing a Five-Star feet, is depicted in Fig.4, and the real-time operation data including action, pressure, and flow are depicted in Fig.5, Fig.6, and Fig.7 respectively.
௧మ
ܧ௧ = න Pሺtሻ ሺͳሻ ௧భ
ܧ௧ ሺʹሻ ܧ௧̴௪ = ݉ ܧ௧̴௪ ܧ௧̴௩ = ሺ͵ሻ ɏ The energy consumption of action n can be calculated in Equation 4. The tns and tne can be calculated in Equation 5 and the solution t is in a feasible solution region [t_ns, t_ne]. ௧
ܧ̴௧ = න
Pሺtሻ ሺͶሻ
௧ೞ
ሺݐሻ ൌ ݊ሺͷሻ 4. Case study An IMPRESS-III Series DCC800 cold-chamber die casting machine by L.K. GROUP was chosen. It is a best seller with locking force up to 8000KN, high injection speed greater than 8m/s, low injection speed from 0.1 to 0.7m/s, injection force up to 247kN, injection stroke up to 11.2kg, and Servo motor power up to 75kW. Its PLC is CJ1W Series by OMRON. A power meter which supports the Modbus protocol was used for the power data extraction. A CJ1W-EIP21 unit by OMRON was added to support EtherNet/IP communication of PLC. An industrial personal computer was chosen to send and receive command with the power meter and PLC. The real-time data is transferred from the industrial personal computers to a central server and stored in database. The data is analysed in the central server and the result is showed to the web and mobile web. Five-Star feet is chosen. It is a component of office chair and can be produced by the DCC800 cold-chamber die casting machine. Fig. 3 is pictures of it. The material of the component is Aluminium alloy, the weight is 1.7kg and the density is 2721kg/m3. The cycle time of producing the component is 61s.
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Fig. 4. The power curve of producing a Five-Star feet.
Fig. 5. The machine actions of producing a Five-Star feet.
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The energy consumption of each action is calculated using Equation 4 and 5. Most of the actions' starting and ending time can be acquired from the machine actions data depicted in Fig.5. A few of actions such as accumulation need the pressure data depicted in Fig.6 and flow data depicted in Fig.7 to acquire the starting and ending time. Table 1 shows the starting and ending time, energy consumption and average power of actions. Table 1. Energy consumption of actions. Action
Fig. 6. The pressure of producing a Five-Star feet.
Locking die Injection High pressure Opening die Plunger trace Fast opening die Ejector out Ejector back Plunger return Accumulation Standby
Starting time(s) 0.73 14.95
Ending time(s) 6.54 21.08
energy consumption(kJ) 82.92 49.27
average power(kW) 14.26 8.00
32.80
34.20
27.68
19.87
34.20
35.85
18.62
11.29
35.85
37.63
5.23
2.94
41.09 49.09 53.94 56.91 0
42.25 50.22 56.34 60.70 60.70
9.78 2.91 117.44 92.47 43.50
8.36 2.58 49.08 24.35 0.72
Fig. 7. The flow of producing a Five-Star feet.
The total electric energy consumption excluding the aluminium melting and heat preservation of producing a FiveStar feet is 755.88kJ (about 0.21kWh). As depicted in Fig.9, the total electric energy consumption is as follows. The die casting machine consumes 455.78kJ electric energy and accounts for 60.30%. The auxiliary equipment 1, consisting of cooling fan, conveyer belt and trimming machine, consumes 267.11kJ electric energy and accounts for 35.34%. The auxiliary equipment 2 consisting of feeding machine, fetching machine, and spraying machine, consumes 32.99kJ electric energy and accounts for 4.36%. The energy per part is calculated using Equation 1 and the value is 455.78kJ. The energy per weight and volume are calculated respectively using Equation 2 and 3 and the values are 268.11kJ/kg and 98.53J/m3 respectively.
Fig. 9. The energy consumption distribution of die casting machine for producing a Five-Star feet.
The total energy consumption of die casting machine can be divided into seven class actions which are described in section 2. As depicted in Fig.10, injection consumes 141.74kJ electric energy and account for 31.10%. Plunger actions consumes 136.06kJ electric energy and account for 29.85%. Locking die consumes 82.92kJ electric energy and accounts for 18.19%. Opening die consumes 32.90kJ electric energy and accounts for 7.22%. Ejection consumes 12.68kJ electric energy and account for 2.78%. Standby consumes 43.50kJ electric energy and account for 9.54%. Others consumes 5.98kJ electric energy and account for 1.31%. 5. Discussions and conclusions
Fig. 8. The energy consumption distribution of producing a Five-Star feet.
In this paper, an IoT-enabled method is presented. Power data were captured by power meters, and operation data by PLC to evaluate the energy consumption of die casting machines. Machine actions were used to segment a die casting process. Some indicators were proposed to reveal the performance of a die casting machine. Then, energy data acquisition and analysis was conducted to support the calculation of these indicators. Through a case study, the feasibility of the method was demonstrated.
Weipeng Liu et al. / Procedia CIRP 69 (2018) 656 – 661
Based on the analysis of acquired data, three findings are given as follows. x The poor coordination between the die casting machine and other auxiliary equipment leads to a long waiting time in feeding, cooling, fetching, and spraying. It results in low energy efficiency. The coordination enabled by IoT online data will improve it. x It is surprising that the plunger actions consumed nearly 30% energy, for these actions are not the critical and have no significant impact on product quality. In particular, the plunger return action consumed 117.44 kJ energy in 2.4 s and the average power is as high as 49.08 kW. Optimizing the plunger return speed is an option. x The power, flow, and pressure changed sharply. It could damage equipment and waste energy. The power peak is 72 kW and the power of most actions ranges from 15 kW to 35 kW. The running time of all actions is only a few seconds. High power and short running time implies energy waste. This also deserves research attention. In future work, more detailed information about energy consumption, such as energy consumption of per action, will be investigated, and algorithms to optimize energy consumption and EE based on real-time data will be developed. This will efficiently help energy saving in die casting machines. Acknowledgements This research and development project is funded by the National Natural Science Foundation of China (Grant No. U1501248), Fundamental Research Funds for the Central Universities (No.2016QNA4002), and Nantaihu Innovation Program of Huzhou Zhejiang China. References [1] International Energy Agency (IEA).International energy outlook 2016. Retrieved from, http://www.eia.gov/pressroom/presentations/sieminski_0 5112016.pdf. [2] U.S. Environmental Protection Agency (EPA). Inventory of U.S. Gree nhouse Gas Emissions and Sinks:1990-2014. 2016. Retrieved from,
661
https://www.epa.gov/sites/production/files/2016-04/documents/us-ghginventory-2016-main-text.pdf. [3] Watkins MF, et al. Sustainability Characterization for Die Casting Process. 2013. p.V02AT02A006. [4] Salonitis K, et al. The Challenges for Energy Efficient Casting Processes. Procedia CIRP; 2016.40(Supplement C):p.24-29. [5] Brevick JR, et al. Modeling alloy and energy utilization in high volume die casting. Clean Technologies and Environmental Policy; 2014.16(1):p.201-209. [6] Heinemann T. Energy and Resource Efficiency in Aluminium Die Casting. Springer International Publishing; 2016. [7] Brevick J, et al. Energy Consumption of Die Casting Operations. Office of Scientific & Technical Information Technical Reports; 2004. [8] Shao Y. Analysis of energy savings potential of China's nonferrous metals industry. Resources, Conservation and Recycling; 2017.117(Part A):p.25-33. [9] FRECH. eFACTS-Energy Efficiency: A Critical Success Factor. 2015. Retrieved from, http://www.frech.com/downloads.html?file=files/frech/Documents/Dow nloads/eFACTS_en.pdf. [10] Salonitis K, et al. Improvements in energy consumption and environmen tal impact by novel single shot melting process for casting. Journal of Cleaner Production; 2016.137(Supplement C):p.1532-1542. [11] Salonitis KM, Jolly and B Zeng. Simulation Based Energy and Resource Efficient Casting Process Chain Selection: A Case Study. Procedia Manufacturing; 2017.8(Supplement C):p.67-74. [12] Trianni A, et al. Barriers to industrial energy efficiency in foundries: a European comparison. Journal of Cleaner Production; 2013.40(Supplement C):p.161-176. [13] Thollander P, et al. Beyond barriers – A case study on driving forces for improved energy efficiency in the foundry industries in Finland, France, Germany, Italy, Poland, Spain, and Sweden. Applied Energy; 2013.111(Supplement C):p.636-643. [14] Lenz J, et al. Energy Efficiency in Machine Tool Operation by Online Energy Monitoring Capturing and Analysis. Procedia CIRP; 2017.61(Supplement C):p.365-369. [15] Abele E, et al. Data Collection for Energy Monitoring Purposes and Energy Control of Production Machines. Procedia CIRP; 2015.29(Supplement C):p.299-304. [16] Tan YS, et al. Internet-of-Things Enabled Real-time Monitoring of Energy Efficiency on Manufacturing Shop Floors. Procedia CIRP; 2017.61(Supplement C):p.376-381. [17] Yen Ting NG, et al. Internet of Things for Real-time Waste Monitoring and Benchmarking: Waste Reduction in Manufacturing Shop Floor. Procedia CIRP; 2017.61(Supplement C):p.382-386. [18] Cullen JM, JM Allwood. Mapping the Global Flow of Aluminum: From Liquid Aluminum to End-Use Goods. Environmental Science & Technology; 2013:p.130311125652007
