Abstract

The growing flexibility of modern production systems complicates the quality assurance and process safety of mechanical processing. As an import component of milling machines, the workpiece clamping systems plays a quality-determining role within every milling process. Thus, a sensory workpiece clamping system that utilizes sensory swing clamps was developed in former research work in order to provide monitoring capabilities. This contribution deals with the experimental analysis of the multiple integrated sensors of the sensory swing clamp and the characterization of their measuring capability toward different measurands. By means of the stepwise linear regression method, different models were developed that enable the determination of the clamping force, the hydraulic pressure, and the piston position. The results verify that the multi-sensor evaluation significantly increases the measuring accuracy of a sensory swing clamp. Thus, the measuring accuracy is measurable with a standard deviation of 0.05 MPa for the hydraulic pressure, 101 N for the clamping force, and 0.62 mm for the piston position. Furthermore, the practicability and flexible use at varying boundary conditions is proved.

References

References
1.
Enke
,
J.
,
Glass
,
R.
,
Kreß
,
A.
,
Hambach
,
J.
,
Tisch
,
M.
, and
Metternich
,
J.
,
2018
, “
Industrie 4.0—Competencies for a Modern Production System
,”
Procedia Manuf.
,
23
(
1
), pp.
267
272
. 10.1016/j.promfg.2018.04.028
2.
Fleischer
,
J.
,
Denkena
,
B.
,
Winfough
,
B.
, and
Mori
,
M.
,
2006
, “
Workpiece and Tool Handling in Metal Cutting Machines
,”
CIRP Ann. Manuf. Technol.
,
55
(
2
), pp.
817
839
. 10.1016/j.cirp.2006.10.009
3.
Teti
,
R.
,
Jemielniak
,
K.
,
O’Donnell
,
G.
, and
Dornfeld
,
D.
,
2010
, “
Advanced Monitoring of Machining Operations
,”
CIRP Ann. Manuf. Technol.
,
59
(
2
), pp.
717
739
. 10.1016/j.cirp.2010.05.010
4.
Karpuschewski
,
B.
,
Kundrák
,
J.
,
Varga
,
G.
,
Deszpoth
,
I.
, and
Borysenko
,
D.
,
2018
, “
Determination of Specific Cutting Force Components and Exponents When Applying High Feed Rates
,”
Procedia CIRP
,
77
(
1
), pp.
30
33
. 10.1016/j.procir.2018.08.199
5.
Gouarir
,
A.
,
Martínez-Arellano
,
G.
,
Terrazas
,
G.
,
Benardos
,
P.
, and
Ratchev
,
S.
,
2018
, “
In-Process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis
,”
Procedia CIRP
,
77
(
1
), pp.
501
504
. 10.1016/j.procir.2018.08.253
6.
Praetzas
,
C.
,
Teppernegg
,
T.
,
Mayr
,
J.
,
Czettl
,
C.
,
Schäfer
,
J.
, and
Abele
,
E.
,
2018
, “
Investigation of Tool Core Temperature and Mechanical Tool Load in Milling of Ti6Al4 V
,”
Procedia CIRP
,
77
(
1
), pp.
118
121
. 10.1016/j.procir.2018.08.240
7.
Brecher
,
C.
,
Klatte
,
M.
,
Lee
,
T. H.
, and
Tzanetos
,
F.
,
2018
, “
Metrological Analysis of a Mechatronic System Based on Novel Deformation Sensors for Thermal Issues in Machine Tools
,”
Procedia CIRP
,
77
(
1
), pp.
517
520
. 10.1016/j.procir.2018.08.245
8.
Östling
,
D.
,
Jensen
,
T.
,
Tjomsland
,
M.
,
Standal
,
O.
, and
Mugaas
,
T.
,
2018
, “
Cutting Process Monitoring With an Instrumented Boring Bar Measuring Cutting Force and Vibration
,”
Procedia CIRP
,
77
(
1
), pp.
235
238
. 10.1016/j.procir.2018.09.004
9.
Prasad
,
B. S.
, and
Babu
,
M. P.
,
2017
, “
Correlation Between Vibration Amplitude and Tool Wear in Turning: Numerical and Experimental Analysis
,”
Eng. Sci. Technol. Int. J.
,
20
(
1
), pp.
197
211
. 10.1016/j.jestch.2016.06.011
10.
Denkena
,
B.
,
Dahlmann
,
D.
, and
Neff
,
T.
,
2016
, “
Autonomous Modular Process Monitoring
,”
Procedia Technol.
,
26
(
1
), pp.
302
308
. 10.1016/j.protcy.2016.08.039
11.
Fujishima
,
M.
,
Ohno
,
K.
,
Nishikawa
,
S.
,
Nishimura
,
K.
,
Sakamoto
,
M.
, and
Kawai
,
K.
,
2016
, “
Study of Sensing Technologies for Machine Tools
,”
CIRP J. Manuf. Sci. Technol.
,
14
(
1
), pp.
71
75
. 10.1016/j.cirpj.2016.05.005
12.
Maier
,
W.
,
Möhring
,
H.-C.
, and
Werkle
,
K.
,
2018
, “
Tools 4.0—Intelligence Starts on the Cutting Edge
,”
Procedia Manuf.
,
24
(
1
), pp.
299
304
. 10.1016/j.promfg.2018.06.024
13.
Shaffer
,
D.
,
Lorson
,
P.
,
Plunkett
,
Z.
,
Ragai
,
I.
,
Danesh-Yazdi
,
A.
, and
Ashour
,
O.
,
2018
, “
Development of Experiment-Based Mathematical Models of Acoustic Signals for Machine Condition Monitoring
,”
Procedia CIRP
,
72
(
1
), pp.
1316
1320
. 10.1016/j.procir.2018.03.269
14.
Lauro
,
C. H.
,
Brandão
,
L. C.
,
Baldo
,
D.
,
Reis
,
R. A.
, and
Davim
,
J. P.
,
2014
, “
Monitoring and Processing Signal Applied in Machining Processes—A Review
,”
Measurement
,
58
(
1
), pp.
73
86
. 10.1016/j.measurement.2014.08.035
15.
Stavropoulos
,
P.
,
Chantzis
,
D.
,
Doukas
,
C.
,
Papacharalampopoulos
,
A.
, and
Chryssolouris
,
G.
,
2013
, “
Monitoring and Control of Manufacturing Processes: A Review
,”
Procedia CIRP
,
8
(
1
), pp.
421
425
. 10.1016/j.procir.2013.06.127
16.
Bauerdick
,
C. J. H.
,
Helfert
,
M.
,
Petruschke
,
L.
,
Sossenheimer
,
J.
, and
Abele
,
E.
,
2018
, “
An Automated Procedure for Workpiece Quality Monitoring Based on Machine Drive-Based Signals in Machine Tools
,”
Procedia CIRP
,
72
(
1
), pp.
357
362
. 10.1016/j.procir.2018.03.245
17.
Gontarz
,
A. M.
,
Hampl
,
D.
,
Weiss
,
L.
, and
Wegener
,
K.
,
2015
, “
Resource Consumption Monitoring in Manufacturing Environments
,”
Procedia CIRP
,
26
(
1
), pp.
264
269
. 10.1016/j.procir.2014.07.098
18.
Möhring
,
H.-C.
,
Litwinski
,
K. M.
, and
Gümmer
,
O.
,
2010
, “
Process Monitoring With Sensory Machine Tool Components
,”
CIRP Ann. Manuf. Technol.
,
59
(
1
), pp.
383
386
. 10.1016/j.cirp.2010.03.087
19.
Abellan-Nebot
,
J. V.
,
Liu
,
J.
, and
Romero Subirón
,
F.
,
2012
, “
Quality Prediction and Compensation in Multi-Station Machining Processes Using Sensor-Based Fixtures
,”
Rob. Comput. Integr. Manuf.
,
28
(
2
), pp.
208
219
. 10.1016/j.rcim.2011.09.001
20.
Möhring
,
H.-C.
,
Wiederkehr
,
P.
,
Lerez
,
C.
,
Schmitz
,
H.
,
Goldau
,
H.
, and
Czichy
,
C.
,
2016
, “
Sensor Integrated CFRP Structures for Intelligent Fixtures
,”
Procedia Technol.
,
26
(
1
), pp.
120
128
. 10.1016/j.protcy.2016.08.017
21.
Möhring
,
H.-C.
, and
Wiederkehr
,
P.
,
2016
, “
Intelligent Fixtures for High Performance Machining
,”
Procedia CIRP
,
46
(
1
), pp.
383
390
. 10.1016/j.procir.2016.04.042
22.
Litwinski
,
K. M.
,
2011
, “
Sensorisches Spannsystem zur Überwachung von Zerspanprozessen in der Einzelteilfertigung
,” Dr.-Ing. dissertation,
IFW, Leibniz Universität Hannover
,
Garbsen
.
23.
Denkena
,
B.
, and
Kiesner
,
J.
,
2017
, “Feeling Clamping System,”
Cyber-Physical and Gentelligent Systems in Manufacturing and Life Cycle: Genetics and Intelligence – Keys to Industry 4.0
, Vol.
1
,
B.
Denkena
, and
T.
Moerke
, eds.,
Academic Press
,
London
, pp.
332
353
.
24.
Denkena
,
B.
,
Dahlmann
,
D.
, and
Kiesner
,
J.
,
2016
, “
Production Monitoring Based on Sensing Clamping Elements
,”
Procedia Technol.
,
26
(
1
), pp.
235
244
. 10.1016/j.protcy.2016.08.032
25.
Denkena
,
B.
, and
Kiesner
,
J.
,
2015
, “
Strain Gauge Based Sensing Hydraulic Fixtures
,”
Mechatronics
,
34
(
1
), pp.
111
118
. 10.1016/j.mechatronics.2015.05.008
26.
NN
,
2015
, “
Swing Clamps with Sturdy Swing Mechanism: Datasheet
,”(B 1.854 / 4-15 E),
Laubach, Germany, Roemheld GmbH
, https://www.roemheld-gruppe.de/?type=6668&path=fileadmin/user_upload/produkte/katalogblaetter/B1854/B1854_en_0415.pdf&h=73a2ad09182a4fc3c20c57a5b72746eb.
27.
Berger
,
P. D.
,
Maurer
,
R. E.
, and
Celli
,
G. B.
,
2018
,
Experimental Design
,
Springer International Publishing
,
Cham
.
28.
Harrell
,
F. E.
,
2015
,
Regression Modeling Strategies
,
Springer International Publishing
,
Cham
.
You do not currently have access to this content.