Recent changes in legislation along with environmental initiatives to drive sustainability and reduce carbon emissions have sprouted the development of energy models to characterize manufacturing processes. In the case of injection molding, much work has been performed in coupling sensors with control statistical systems to promptly identify process' instabilities, such as pressure drops or fluctuations in the filling point. Latest energy models for injection molding make use of injection pressure and temperature parameters that are a function of the machine, mold geometry, and process characteristics. The latest state-of-the-art way to measure energy consumption is through the use of energy loggers, which provide power data at the end of the production cycles. Although seemingly correlated, little has been published on the extrapolation of cavity signals for their use in energy calculations. In this study, the advantages and disadvantages of using cavity sensors in injection molding are explored; a novel approach to the use of cavity sensors' pressure and temperature data is proposed by exploring their input in an energy model for the estimation of specific energy consumption (SEC). The model was validated against power data obtained via an energy logger; the averaged energy reported by the model indicated a range of 60–67% accuracy.

References

References
1.
Rubin
,
I. I.
,
1972
,
Injection Molding Theory and Practice
,
Wiley
,
New York
.
2.
Grand View Research,
2015
, “
Injection Molded Plastic Market Report
,” Grand View Research, Inc., San Francisco, CA, accessed Feb. 27, 2017, http://www.grandviewresearch.com/industry-analysis/injection-molded-plastics-market
3.
Thiriez
,
A.
,
2004
, “
An Environmental Analysis of Injection Moulding
,”
Master's thesis
, Massachussetts Institute of Technology, Cambridge, MA.http://www.mtmgroup.it/wp-content/uploads/2014/11/Enviromental-Analysis-of-Injection-Moldin_MIT_2006.pdf
4.
Kordonowy
,
D. N.
,
2002
, “
A Power Assessment of Machining Tools
,”
Thesis
, Massachusetts Institute of Technology, Cambridge, MA.https://dspace.mit.edu/handle/1721.1/31108
5.
Gutowski
,
T.
,
Dahmus
,
J.
, and
Thiriez
,
A.
,
2006
, “
Electrical Energy Requirements for Manufacturing Processes
,”
13th CIRP International Conference of Life Cycle Engineering
, Lueven, Belgium, May 31–June 2.http://web.mit.edu/2.813/www/readings/Gutowski-CIRP.pdf
6.
Madan
,
J.
,
Mani
,
M.
, and
Lyons
,
K. W.
,
2013
, “
Characterizing Energy Consumption of the Injection Molding Process
,”
ASME
Paper No. MSEC2013-1222.
7.
Gallo
,
V.
, and Montgomery, S.,
2012
, “
Achieve Process Transparency With In-Mold Cavity Sensors
,” Gardner Business Media, Inc., Cincinnati, OH, accessed Nov. 6, 2017, https://www.ptonline.com/articles/achieve-process-transparency-with-in-mold-cavity-sensors
8.
Collins
,
C.
,
1999
, “
Monitoring Cavity Pressure Perfects Injection Molding
,”
Assem. Autom.
,
19
(
3
), pp.
197
202
.
9.
Nam
,
J. S.
,
Baek
,
D. S.
,
Jo
,
H. H.
,
Song
,
J. Y.
,
Ha
,
T. H.
, and
Lee
,
S. W.
,
2015
, “
Lens Injection Moulding Condition Diagnosis and Form Error Analysis Using Cavity Pressure Signals Based on Response Surface Methodology
,”
Proc. Inst. Mech. Eng., Part B
,
230
(
7
), pp.
1343
1350
.
10.
Estrada
,
P.
,
Siller
,
H. R.
,
Vázquez
,
E.
,
Rodríguez
,
C. A.
,
Martínez-Romero
,
O.
, and
Corona
,
R.
,
2016
, “
Micro-Injection Moulding of Polymer Locking Ligation Systems
,”
Procedia CIRP
,
49
, pp.
1
7
.
11.
Wagemakers
,
T.
,
2017
, “
Sensorising Injection Moulds
,” Interviewee, Mar. 13.
12.
INEOS
,
2016
, “
Polypropylene Homopolymer
,” INEOS Olefins & Polymers Europe, London.
13.
Osswald
,
T.
, and
Hernandez-Ortiz
,
J. P.
, 2006, “
Processing Properties
,”
Polymer Processing, Modeling and Simulation
,
Hanser Publishers
, Munich, Germany, pp.
37
47
.
14.
Mettler Toledo
, 2017,
Thermal Analysis: Polymer DSC
, Mettler Toledo, Columbus, OH.
15.
Plastik City
, 2017, “
Plastic Material Shrinkage Rates
,” Plastik City, Coventry, UK, accessed Feb. 27, 2017, https://www.plastikcity.co.uk/useful-stuff/material-shrinkage-rates
16.
Tsai
,
K.-M.
, and
Lan
,
J.-K.
,
2015
, “
Correlation Between Runner Pressure and Cavity Pressure Within Injection Mold
,”
Int. J. Adv. Manuf. Technol.
,
79
(
1–4
), pp.
273
284
.
17.
PREVIEW
,
2015
, “
Predictive System to Recommend Injection Mould Setup With Process Optimisation in Wireless Sensor Networks
,” PREVIEW, Barcelona, Spain, accessed Mar. 16, 2017, http://www.preview-project.eu/
18.
Naumann
,
R.
,
Dietzel
,
S.
,
Wartschinski
,
L.
,
Schumacher
,
B.
, and
Scheuermann
,
B.
,
2017
, “
TANDEM: Prioritizing Wireless Communication for Robust Industrial Process Control
,”
26th International Conference on Computer Communications and Networks
(
ICCCN
), Vancouver, BC, Canada, July 31–Aug. 3, pp. 1–9.
19.
Vogelesang
,
H.
,
2008
, “
An Introduction to Energy Consumption in Pumps
,”
World Pumps
,
2008
(
496
), pp.
28
31
.
20.
Nguyen
,
D. T.
,
2004
, “
Injection Molding Scrap Reduction: A Study in the Relationships of Plastics Processing Methods
,”
Master's thesis
, University of Wisconsin, Madison, WI.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.390.9740&rep=rep1&type=pdf
21.
Thiriez
,
A.
, and
Gutowski
,
T.
,
2006
, “
An Environmental Analysis of Injection Molding
,” IEEE International Symposium on Electronics and the Environment (
ISEE
), Scottsdale, AZ, May 8–11, pp. 195–200.
22.
Nannapaneni
,
S.
, and
Mahadevan
,
S.
,
2014
, “
Uncertainty Quantification in Performance Evaluation of Manufacturing Processes
,”
IEEE International Conference on Big Data: Data Analytics for Smart Manufacturing Systems
(
BigData
), Washington, DC, Oct. 27–30, pp. 996–1005.
23.
University of Iowa, 2017, “
Percent Error Formula
,” University of Iowa, Iowa City, IA, accessed Mar. 27, 2017, http://astro.physics.uiowa.edu/ITU/glossary/percent-error-formula/
24.
Bendada
,
A.
,
Derdouri
,
A.
,
Simard
,
Y.
, and
Lamontagne
,
M.
,
2007
, “
Advances in Infrared Technology for the Online Monitoring of Injection Moulding: Application to the Understanding of the Nature of Contact at the Polymer-Mould Interface
,”
Trans. Inst. Meas. Control
,
29
(
5
), pp.
431
451
.
25.
Yokoi
,
H.
,
Murata
,
Y.
, and
Tsukakoshi
,
H.
,
1992
, “
Measurement of Melt Temperature Profiles During Filling and Packing Processes Using a New Integrated Thermocouple Sensor
,”
ANTEC 92-Shaping the Future
, Detroit, MI, CRC Press, Boca Raton, FL.
26.
Migler
,
K.
, and
Bur
,
A. J.
,
1998
, “
Fluorescence Based Measurement of Temperature Profiles During Polymer Processing
,”
Polym. Eng. Sci.
,
38
(
1
), pp.
213
221
.
27.
Kusic
,
D.
,
Kek
,
T.
,
Slabe
,
J. M.
,
Svecko
,
R.
, and
Grum
,
J.
,
2013
, “
The Impact of Process Parameters on Test Specimen Deviations and Their Correlation With AE Signals Captured During the Injection Moulding Cycle
,”
Polym. Test.
,
32
(3), pp.
583
593
.
28.
Pacher
,
G.
,
Berger
,
G.
,
Friesenbichler
,
W.
,
Gruber
,
D.
, and
Macher
,
J.
,
2014
, “
In-Mold Sensor Concept to Calculate Process-Specific Rheological Properties
,”
AIP Conf. Proc.
,
1593
(1), pp. 179–182. https://doi.org/10.1063/1.4873759
29.
Gordon
,
G.
,
Kazmer
,
D. O.
,
Tang
,
X.
,
Fan
,
Z.
, and
Gao
,
R. X.
,
2015
, “
Quality Control Using a Multivariate Injection Molding Sensor
,”
Int. J. Adv. Manuf. Technol.
,
78
(
9–12
), pp.
1381
1391
.
30.
Müller
,
F.
,
Kukla
,
C.
,
Lucyshyn
,
T.
,
Harker
,
M.
,
Rath
,
G.
, and
Holzer
,
C.
,
2014
, “
Wireless In-Mold Melt Front Detection for Injection Molding: A Long-Term Evaluation
,”
J. Appl. Polym. Sci.
,
131
(
11
), pp.
1
10
.
31.
Gao
,
R. X.
,
Tang
,
X. T.
,
Gordon
,
G.
, and
Kazmer
,
D. O.
,
2014
, “
Online Product Quality Monitoring Through In-Process Measurement
,”
CIRP Ann.-Manuf. Technol.
,
63
(1), pp.
493
496
.
You do not currently have access to this content.