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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

Postal Subscription Code 80-975

2018 Impact Factor: 0.989

Front. Mech. Eng.    2021, Vol. 16 Issue (4) : 868-886    https://doi.org/10.1007/s11465-021-0656-0
RESEARCH ARTICLE
An energy consumption prediction approach of die casting machines driven by product parameters
Erheng CHEN1, Hongcheng LI2, Huajun CAO1(), Xuanhao WEN1
1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
2. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunication, Chongqing 400065, China
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Abstract

Die casting machines, which are the core equipment of the machinery manufacturing industry, consume great amounts of energy. The energy consumption prediction of die casting machines can support energy consumption quota, process parameter energy-saving optimization, energy-saving design, and energy efficiency evaluation; thus, it is of great significance for Industry 4.0 and green manufacturing. Nevertheless, due to the uncertainty and complexity of the energy consumption in die casting machines, there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration. To fill this gap, this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters. Firstly, the system boundary of energy consumption prediction is defined, and subsequently, based on the energy consumption characteristics analysis, a theoretical energy consumption model is established. Consequently, a systematic energy consumption prediction approach for die casting machines, involving product, die, equipment, and process parameters, is proposed. Finally, the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products. The results show that the prediction accuracy of production time and energy consumption reached 91.64% and 85.55%, respectively. Overall, the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.

Keywords die casting machine      energy consumption prediction      product parameters     
Corresponding Author(s): Huajun CAO   
Just Accepted Date: 09 November 2021   Online First Date: 30 November 2021    Issue Date: 28 January 2022
 Cite this article:   
Erheng CHEN,Hongcheng LI,Huajun CAO, et al. An energy consumption prediction approach of die casting machines driven by product parameters[J]. Front. Mech. Eng., 2021, 16(4): 868-886.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-021-0656-0
https://academic.hep.com.cn/fme/EN/Y2021/V16/I4/868
Fig.1  Overview of the die casting process.
Energy consumption characteristic Description Characteristic diagram
Consistency The energy consumption curve of each production cycle includes the die closing (DC), dosing (DO), plunger forward (PF), solidification (SO), die opening (DP), ejector out (EO), extracting (EX), plunger backward (PB), and spraying (SP) in order.
Uncertainty and complexity The energy consumption curve of each type of sub-processes is uncertain and complex in the different production cycles.
Tab.1  Energy consumption characteristics of die casting machines
Fig.2  Schematic of the plunger forward and solidification sub-processes.
Fig.3  Energy consumption prediction approach of die casting machines.
Item Energy-related parameters Group 1 Group 2 Group 3 Group 4 Group 5 Group 6
Product Average wall thickness/mm 5.0 4.0 9.6 7.0 9.0 6.0
Maximum wall thickness/mm 13.00 10.00 14.86 14.86 21.00 12.00
Volume/cm3 180 283 833 443 1074 2329
Length/mm 325 240 280 346.8 430 391.6
Width/mm 140.0 180.0 200.0 283.9 330.0 344.7
Surface area/m2 0.21 0.23 0.40 0.32 0.47 1.20
Die Number of cavities 1 1 1 1 1 1
Machine Plunger tip area/mm2 3846 3846 6358 6358 11304 11305
Recommended melt injection temperature/°C 640 640 660 660 660 660
Process parameters DC pressure/kN 109 96 98 107 195 209
DC displacement/mm 600 600 800 800 1100 1200
Die clamping pressure/kN 5250 5250 8400 8400 16000 16000
Die clamping displacement /mm 5 5 5 5 5 5
PF pressure/bar 160 170 160 140 180 160
PF displacement /mm 120 120 110 120 150 150
SO pressure/bar 570 550 570 560 660 570
SO displacement /mm 100 100 120 100 150 150
Ejection pressure/kN 100 120 180 180 240 360
Ejection displacement/mm 50 55 87 92 105 120
Tab.2  Key information on the energy consumption prediction of die casting machines
Fig.4  Selected die casting machines and products.
Fig.5  Time, energy consumption, pressure, and displacement data obtainment of the die casting machine.
Fig.6  Predicted time, actual time, and time prediction accuracy of each sub-process: (a) Group 1, (b) Group 2, (c) Group 3, (d) Group 4, (e) Group 5, and (f) Group 6.
Fig.7  Ideal, theoretical, predicted, and actual energy consumption curves for each sub-process: (a) Group 1, (b) Group 2, (c) Group 3, (d) Group 4, (e) Group 5, and (f) Group 6.
Fig.8  Energy consumption proportion of each sub-process in die casting machines: (a) ideal energy consumption, (b) theoretical energy consumption, (c) predicted energy consumption, and (d) actual energy consumption.
Group DCO/% DO/% PF/% SO/% EO/% EX/% SP/% PB/%
1 89.68 88.01 79.71 84.79 90.82 89.14 87.43 83.9
2 88.93 90.16 80.59 85.36 89.19 88.91 88.5 83.21
3 87.10 91.90 82.81 84.80 87.05 86.50 86.91 84.69
4 85.86 88.07 84.67 84.27 85.09 89.64 88.32 83.39
5 82.84 86.31 80.78 83.76 84.39 87.26 86.59 80.16
6 80.97 83.86 79.66 82.23 84.17 88.90 86.24 78.72
Tab.3  Energy consumption prediction accuracy of each sub-process in different groups
Fig.9  Energy consumption prediction accuracy of each sub-process in the production cycles I and II: (a) Group 1, (b) Group 2, (c) Group 3, (d) Group 4, (e) Group 5, and (f) Group 6.
Abbreviations
CNC Computer numerical control
DC Die closing
DCO Die closing and opening
DO Dosing
DP Die opening
ECPA Energy consumption prediction accuracy
EO Ejector out
EX Extracting
IoT Internet of Things
PB Plunger backward
PF Plunger forward
PFB Plunger forward and backward
SO Solidification
SP Spraying
Variables
Aap Shot piston area
Aar Shot piston area minus the rod area
ADC Cross-sectional area of the hydraulic cylinder
AEO Cross-sectional area of the piston
Apa Cavity projected area
APB Area of plunger tip
APF Area of the plunger tip
Apt Area of the plunger tip
Asa Cavity surface area
ASO Area of the gate
dDC Diameter of the hydraulic cylinder
deo Displacement of EO
dEO Diameter of the piston
dPB Diameter of plunger tip
dPF Diameter of the plunger tip
dPFB Displacement of PFB
dSP Diameter of hydraulic cylinder for a certain sub-process
Ebasic Basic energy consumption
EDC Energy consumption of DC
EDO Energy consumption of DO
  
EDP Energy consumption of DP
EEO Energy consumption of EO
EEX Energy consumption of EX
Eideal Sum of the ideal energy consumption
Eideali Ideal energy consumption of the ith sub-process
EPB Energy consumption of PB
EPF Energy consumption of PF
Epredict Predicted energy consumption per part
ESO Energy consumption of SO
ESP Energy consumption of SP
Etheoretical Theoretical energy consumption
FPBA Average force of PB
h Average wall thickness of product
hmax Maximum wall thickness
K Magnification times of toggle clamps
L Product length
LDC Displacement of DC
LEO Displacement of EO
LPB Displacement of PB
LPF Displacement of PF
LSO Theoretical length of the metal liquid
LSP Displacement during a certain sub-process
nc Number of die cavities
nl Number of machine cycles per lubrication
ns Total number of side-pulls
P Injection pressure of PF or SO
Pap Accumulator pressure
Pbasic Basic power of die casting machine
PDC Instantaneous hydraulic pressure of DC
PDCA Average hydraulic pressure during DC
PEO Instantaneous hydraulic pressure of EO
PEOA Average hydraulic pressure of EO
Pep Exhaust pressure
PPB Instantaneous hydraulic pressure of PB
PPBA Average hydraulic pressure of PB
PPDCA Pre-set hydraulic pressures of DC
PPDP Pre-set hydraulic pressures of DP
PPEO Pre-set hydraulic pressures of EO
PPF Instantaneous hydraulic pressure of PF
PPFA Average hydraulic pressure of PF
PPPBA Pre-set hydraulic pressures of PB
PPPFA Pre-set hydraulic pressures of PF
PPSOA Pre-set hydraulic pressures of SO
PSO Instantaneous hydraulic pressure of SO
PSOA Average hydraulic pressure of SO
PSP Instantaneous hydraulic pressure
tcycle Production cycle
tDC Duration of DC
tDCO Duration of DCO
tDCT Machine dry cycle time
tDO Duration of DO
tDP Duration of DP
tEO Duration of EO
tEX Duration of EX
tPB Duration PB
tPF Duration of PF
tSO Duration of SO
tSP Duration for a certain sub-process
tSPR Duration of SP
Ti Recommended melt injection temperature
Tl Die casting alloy liquid temperature
Tm Die temperature before the shot
veo Average velocity of EO
vPFB Average velocity of PFB
Vcavities Volume of cavities
Vfeed Volume of feed system
Vinject Shot volume
Voverflow Volume of overflow wells
VSO Volume of the metal liquid
W Product width
WSP Power value of a certain sub-process
yacti Actual time or energy consumption
yprei Predicted time or energy consumption
β Cooling factor
ηDCpump Pump efficiency of DC
ηDCservo Servo efficiency of DC
ηDCA Hydraulic system average efficiency of DC
ηDPpump Pump efficiency of DP
ηDPservo Servo efficiency of DP
ηDPA Hydraulic system average efficiency of DP
ηEOpump Pump efficiency of EO
ηEOservo Servo efficiency of EO
ηEOA Hydraulic system average efficiency of EO
ηmachine Efficiency of die casting machines
ηPBpump Pump efficiency of PB
ηPBservo Servo efficiency of PB
ηPBA Hydraulic system average efficiency of PB
ηPFpump Pump efficiency of PF
ηPFservo Servo efficiency of PF
ηPFA Hydraulic system average efficiency of PF
ηSOpump Pump efficiency of SO
ηSOservo Servo efficiency of SO
ηSOA Hydraulic system average efficiency of SO
ηSP Average efficiency of hydraulic system
  
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