Abstract

The use of Human–Robot Collaboration (HRC) in assembly tasks has gained increasing attention in recent years as it allows for the combination of the flexibility and dexterity of human operators with the repeatability of robots, thus meeting the demands of the current market. However, the performance of these collaborative systems is known to be influenced by various factors, including the complexity perceived by operators. This study aimed to investigate the effects of perceived complexity on the performance measures of HRC assembly. An experimental campaign was conducted in which a sample of skilled operators was instructed to perform six different variants of electronic boards and express a complexity assessment based on a set of assembly complexity criteria. Performance measures such as assembly time, in-process defects, quality control times, offline defects, total defects, and human stress response were monitored. The results of the study showed that the perceived complexity had a significant effect on assembly time, in-process and total defects, and human stress response, while no significant effect was found for offline defects and quality control times. Specifically, product variants perceived as more complex resulted in lower performance measures compared to products perceived as less complex. These findings hold important implications for the design and implementation of HRC assembly systems and suggest that perceived complexity should be taken into consideration to increase HRC performance.

References

1.
Falck
,
A.-C.
,
Örtengren
,
R.
,
Rosenqvist
,
M.
, and
Söderberg
,
R.
,
2017
, “
Basic Complexity Criteria and Their Impact on Manual Assembly Quality in Actual Production
,”
Int. J. Ind. Ergon.
,
58
(Part C), pp.
117
128
.
2.
Verna
,
E.
,
Genta
,
G.
,
Galetto
,
M.
, and
Franceschini
,
F.
,
2022
, “
Zero Defect Manufacturing: A Self-Adaptive Defect Prediction Model Based on Assembly Complexity
,”
Int. J. Comput. Integr. Manuf.
, pp.
1
14
.
3.
Orfi
,
N.
,
Terpenny
,
J.
, and
Sahin-Sariisik
,
A.
,
2011
, “
Harnessing Product Complexity: Step 1—Establishing Product Complexity Dimensions and Indicators
,”
Eng. Econ.
,
56
(
1
), pp.
59
79
.
4.
Faccio
,
M.
,
Minto
,
R.
,
Rosati
,
G.
, and
Bottin
,
M.
,
2020
, “
The Influence of the Product Characteristics on Human-Robot Collaboration: A Model for the Performance of Collaborative Robotic Assembly
,”
Int. J. Adv. Manuf. Technol.
,
106
(
5–6
), pp.
2317
2331
.
5.
Rekiek
,
B.
,
De Lit
,
P.
, and
Delchambre
,
A.
,
2000
, “
Designing Mixed-Product Assembly Lines
,”
IEEE Trans. Robot. Autom.
,
16
(
3
), pp.
268
280
.
6.
Zhu
,
X.
,
Hu
,
S. J.
,
Koren
,
Y.
, and
Marin
,
S. P.
,
2008
, “
Modeling of Manufacturing Complexity in Mixed-Model Assembly Lines
,”
ASME. J. Manuf. Sci. Eng.
,
130
(
5
), p.
051013
.
7.
Di Pasquale
,
V.
,
Miranda
,
S.
,
Neumann
,
W. P.
, and
Setayesh
,
A.
,
2018
, “
Human Reliability in Manual Assembly Systems: A Systematic Literature Review
,”
Ifac-Papersonline
,
51
(
11
), pp.
675
680
.
8.
Hu
,
S. J.
,
Ko
,
J.
,
Weyand
,
L.
,
ElMaraghy
,
H. A.
,
Lien
,
T. K.
,
Koren
,
Y.
,
Bley
,
H.
,
Chryssolouris
,
G.
,
Nasr
,
N.
, and
Shpitalni
,
M.
,
2011
, “
Assembly System Design and Operations for Product Variety
,”
CIRP Ann.
,
60
(
2
), pp.
715
733
.
9.
Faccio
,
M.
,
Bottin
,
M.
, and
Rosati
,
G.
,
2019
, “
Collaborative and Traditional Robotic Assembly: A Comparison Model
,”
Int. J. Adv. Manuf. Technol.
,
102
(
5–8
), pp.
1355
1372
.
10.
Verna
,
E.
,
Puttero
,
S.
,
Genta
,
G.
, and
Galetto
,
M.
,
2022
, “
Challenges and Opportunities of Collaborative Robots for Quality Control in Manufacturing: Evidences From Research and Industry
,”
Proceedings Book of the 5th International Conference on Quality Engineering and Management
,
Braga, Portugal
,
July 14–15
, pp.
235
262
.
11.
Verna
,
E.
,
Genta
,
G.
,
Galetto
,
M.
, and
Franceschini
,
F.
,
2022
, “
Defects-Per-Unit Control Chart for Assembled Products Based on Defect Prediction Models
,”
Int. J. Adv. Manuf. Technol.
,
119
(
5–6
), pp.
2835
2846
.
12.
Galetto
,
M.
,
Verna
,
E.
,
Genta
,
G.
, and
Franceschini
,
F.
,
2020
, “
Uncertainty Evaluation in the Prediction of Defects and Costs for Quality Inspection Planning in Low-Volume Productions
,”
Int. J. Adv. Manuf. Technol.
,
108
(
11
), pp.
3793
3805
.
13.
Peshkin
,
M.
, and
Colgate
,
J. E.
,
1999
, “
Cobots
,”
Ind. Rob.
,
26
(
5
), pp.
335
341
.
14.
Bauer
,
A.
,
Wollherr
,
D.
, and
Buss
,
M.
,
2007
, “
Huma-Robot Collaboration: A Survey
,”
Int. J. Humanoid Rob.
,
5
(
1
), pp.
47
66
.
15.
Galin
,
R.
, and
Mamchenko
,
M.
,
2021
, “
Human-Robot Collaboration in the Society of the Future: A Survey on the Challenges and the Barriers
,”
Futuristic Trends in Network and Communication Technologies: Third International Conference, FTNCT 2020
,
Oct. 14–16
,
Taganrog, Russia
, Revised Selected Papers, Part I 3, Springer, Singapore, pp.
111
122
.
16.
Wang
,
L.
,
Gao
,
R.
,
Váncza
,
J.
,
Krüger
,
J.
,
Wang
,
X. V.
,
Makris
,
S.
, and
Chryssolouris
,
G.
,
2019
, “
Symbiotic Human-Robot Collaborative Assembly
,”
CIRP Ann.
,
68
(
2
), pp.
701
726
.
17.
Inkulu
,
A. K.
,
Bahubalendruni
,
M. V. A. R.
, and
Dara
,
A.
,
2022
, “
Challenges and Opportunities in Human Robot Collaboration Context of Industry 4.0—A State of the Art Review
,”
Ind. Rob.
,
49
(
2
), pp.
226
239
.
18.
ISO 10218-1:2011
,
2011
,
Robots and Robotic Devices. Safety Requirements for Industrial Robots. Robots
,
International Organization for Standardization
,
Genève
.
19.
ISO 10218-2:2011
,
2011
,
Robots and Robotic Devices. Safety Requirements for Industrial Robots. Robot Systems and Integration
,
International Organization for Standardization
,
Genève
.
20.
ISO/TS 15066:2016
,
2016
,
Robots and Robotic Devices. Collaborative Robots
,
International Organization for Standardization
,
Genève
.
21.
Zanchettin
,
A. M.
,
Ceriani
,
N. M.
,
Rocco
,
P.
,
Ding
,
H.
, and
Matthias
,
B.
,
2015
, “
Safety in Human-Robot Collaborative Manufacturing Environments: Metrics and Control
,”
IEEE Trans. Autom. Sci. Eng.
,
13
(
2
), pp.
882
893
.
22.
Gervasi
,
R.
,
Mastrogiacomo
,
L.
, and
Franceschini
,
F.
,
2020
, “
A Conceptual Framework to Evaluate Human-Robot Collaboration
,”
Int. J. Adv. Manuf. Technol.
,
108
(
3
), pp.
841
865
.
23.
Gawron
,
V. J.
,
2008
,
Human Performance, Workload, and Situational Awareness Measures Handbook
,
CRC Press
,
Boca Raton, FL
.
24.
Young
,
M. S.
,
Brookhuis
,
K. A.
,
Wickens
,
C. D.
, and
Hancock
,
P. A.
,
2015
, “
State of Science: Mental Workload in Ergonomics
,”
Ergonomics
,
58
(
1
), pp.
1
17
.
25.
Wickens
,
C. D.
,
2008
, “
Multiple Resources and Mental Workload
,”
Hum. Factors
,
50
(
3
), pp.
449
455
.
26.
Hart
,
S. G.
, and
Staveland
,
L. E.
,
1988
, “
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
,”
Human Mental Workload
,
P.
Hancock
and
N.
Meshkati
, eds.,
North Holland, Amsterdam
, pp.
139
183
.
27.
Reid
,
G. B.
, and
Nygren
,
T. E.
,
1988
, “
The Subjective Workload Assessment Technique: A Scaling Procedure for Measuring Mental Workload
,”
Adv. Psychol.
,
52
(
C
), pp.
185
218
.
28.
Marinescu
,
A. C.
,
Sharples
,
S.
,
Ritchie
,
A. C.
,
Sánchez López
,
T.
,
McDowell
,
M.
, and
Morvan
,
H. P.
,
2018
, “
Physiological Parameter Response to Variation of Mental Workload
,”
Hum. Factors
,
60
(
1
), pp.
31
56
.
29.
Argyle
,
E. M.
,
Marinescu
,
A.
,
Wilson
,
M. L.
,
Lawson
,
G.
, and
Sharples
,
S.
,
2021
, “
Physiological Indicators of Task Demand, Fatigue, and Cognition in Future Digital Manufacturing Environments
,”
Int. J. Hum. Comput. Stud.
,
145
, p.
102522
.
30.
Bradley
,
M. M.
, and
Lang
,
P. J.
,
1994
, “
Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential
,”
J. Behav. Ther. Exp. Psychiatry
,
25
(
1
), pp.
49
59
.
31.
Gervasi
,
R.
,
Barravecchia
,
F.
,
Mastrogiacomo
,
L.
, and
Franceschini
,
F.
,
2023
, “
Applications of Affective Computing in Human–Robot Interaction: State-of-Art and Challenges for Manufacturing
,”
Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.
,
237
(
6–7
), pp.
815
832
.
32.
Gervasi
,
R.
,
Aliev
,
K.
,
Mastrogiacomo
,
L.
, and
Franceschini
,
F.
,
2022
, “
User Experience and Physiological Response in Human-Robot Collaboration: A Preliminary Investigation
,”
J. Intell. Rob. Syst.
,
106
(
2
), p.
36
.
33.
Kulic
,
D.
, and
Croft
,
E. A.
,
2007
, “
Affective State Estimation for Human–Robot Interaction
,”
IEEE Trans. Rob.
,
23
(
5
), pp.
991
1000
.
34.
Arai
,
T.
,
Kato
,
R.
, and
Fujita
,
M.
,
2010
, “
Assessment of Operator Stress Induced by Robot Collaboration in Assembly
,”
CIRP Ann.
,
59
(
1
), pp.
5
8
.
35.
Kühnlenz
,
B.
,
Erhart
,
M.
,
Kainert
,
M.
,
Wang
,
Z.-Q.
,
Wilm
,
J.
, and
Kühnlenz
,
K.
,
2018
, “
Impact of Trajectory Profiles on User Stress in Close Human-Robot Interaction
,”
at-Automatisierungstechnik
,
66
(
6
), pp.
483
491
.
36.
Coronado
,
E.
,
Kiyokawa
,
T.
,
Ricardez
,
G. A. G.
,
Ramirez-Alpizar
,
I. G.
,
Venture
,
G.
, and
Yamanobe
,
N.
,
2022
, “
Evaluating Quality in Human-Robot Interaction: A Systematic Search and Classification of Performance and Human-Centered Factors, Measures and Metrics Towards an Industry 5.0
,”
J. Manuf. Syst.
,
63
, pp.
392
410
.
37.
Colim
,
A.
,
Faria
,
C.
,
Cunha
,
J.
,
Oliveira
,
J.
,
Sousa
,
N.
, and
Rocha
,
L. A.
,
2021
, “
Physical Ergonomic Improvement and Safe Design of an Assembly Workstation Through Collaborative Robotics
,”
Safety
,
7
(
1
), p.
14
.
38.
Khalid
,
A.
,
Kirisci
,
P.
,
Ghrairi
,
Z.
,
Thoben
,
K. D.
, and
Pannek
,
J.
,
2017
, “
Towards Implementing Safety and Security Concepts for Human-Robot Collaboration in the Context of Industry 4.0
,”
Proceedings of the 39th International MATADOR Conference on Advanced Manufacturing
,
Manchester, UK
,
July 5–7
, pp.
55
63
.
39.
Galin
,
R. R.
, and
Meshcheryakov
,
R. V.
,
2020
, “Human-Robot Interaction Efficiency and Human-Robot Collaboration,”
Robotics: Industry 4.0 Issues & New Intelligent Control Paradigms
,
A. G.
Kravets
, ed.,
Springer International Publishing
,
Cham
, pp.
55
63
.
40.
Zadeh
,
L.
,
1962
, “
From Circuit Theory to System Theory
,”
Proc. IRE
,
50
(
5
), pp.
856
865
.
41.
Verna
,
E.
,
Genta
,
G.
,
Galetto
,
M.
, and
Franceschini
,
F.
,
2022
, “
Defect Prediction for Assembled Products: A Novel Model Based on the Structural Complexity Paradigm
,”
Int. J. Adv. Manuf. Technol.
,
120
(
5–6
), pp.
3405
3426
.
42.
Sinha
,
K.
,
2014
, “
Structural Complexity and Its Implications for Design of Cyber-Physical Systems
,”
Ph.D. dissertation
,
Engineering Systems Division, Massachusetts Institute of Technology
,
Cambridge, MA
.
43.
Alkan
,
B.
, and
Harrison
,
R.
,
2019
, “
A Virtual Engineering Based Approach to Verify Structural Complexity of Component-Based Automation Systems in Early Design Phase
,”
J. Manuf. Syst.
,
53
, pp.
18
31
.
44.
Lucas
,
S. M.
, and
Reynolds
,
T. J.
,
2003
, “
Learning DFA: Evolution Versus Evidence Driven State Merging
,”
The 2003 Congress on Evolutionary Computation, 2003. CEC ‘03
,
IEEE
, pp.
351
358
.
45.
Taylor
,
S.
,
Jaques
,
N.
,
Chen
,
W.
,
Fedor
,
S.
,
Sano
,
A.
, and
Picard
,
R.
,
2015
, “
Automatic Identification of Artifacts in Electrodermal Activity Data
,”
Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
,
Milan, Italy, Aug. 25–29
, pp.
1934
1937
.
46.
Liu
,
P.
, and
Li
,
Z.
,
2012
, “
Task Complexity: A Review and Conceptualization Framework
,”
Int. J. Ind. Ergon.
,
42
(
6
), pp.
553
568
.
47.
Falck
,
A. C.
, and
Rosenqvist
,
M.
,
2012
, “
What Are the Obstacles and Needs of Proactive Ergonomics Measures at Early Product Development Stages?—An Interview Study in Five Swedish Companies
,”
Int. J. Ind. Ergon.
,
42
(
5
), pp.
406
415
.
48.
Falck
,
A.-C.
,
Örtengren
,
R.
, and
Rosenqvist
,
M.
,
2014
, “
Assembly Failures and Action Cost in Relation to Complexity Level and Assembly Ergonomics in Manual Assembly (Part 2)
,”
Int. J. Ind. Ergon.
,
44
(
3
), pp.
455
459
.
49.
Alkan
,
B.
,
2019
, “
An Experimental Investigation on the Relationship Between Perceived Assembly Complexity and Product Design Complexity
,”
Int. J. Interact. Des. Manuf.
,
13
(
3
), pp.
1145
1157
.
50.
Falck
,
A.-C.
,
Örtengren
,
R.
,
Rosenqvist
,
M.
, and
Söderberg
,
R.
,
2017
, “
Proactive Assessment of Basic Complexity in Manual Assembly: Development of a Tool to Predict and Control Operator-Induced Quality Errors
,”
Int. J. Prod. Res.
,
55
(
15
), pp.
4248
4260
.
51.
Falck
,
A.-C.
,
Örtengren
,
R.
,
Rosenqvist
,
M.
, and
Söderberg
,
R.
,
2016
, “
Criteria for Assessment of Basic Manual Assembly Complexity
,”
Procedia CIRP
,
44
, pp.
424
428
.
52.
Yager
,
R. R.
,
1993
, “
Non-Numeric Multi-Criteria Multi-Person Decision Making
,”
Group Decis. Negot.
,
2
(
1
), pp.
81
93
.
53.
Verna
,
E.
,
Genta
,
G.
, and
Galetto
,
M.
,
2023
, “
A New Approach for Evaluating Experienced Assembly Complexity Based on Multi Expert-Multi Criteria Decision Making Method
,”
Res. Eng. Des.
,
34
(
3
), pp.
301
325
.
54.
Agresti
,
A.
,
2003
,
Categorical Data Analysis
,
John Wiley & Sons
,
Hoboken, NJ
.
55.
McCullagh
,
P.
,
1980
, “
Regression Models for Ordinal Data
,”
J. R. Stat. Soc. Ser. B
,
42
(
2
), pp.
109
127
.
56.
Powers
,
D.
, and
Xie
,
Y.
,
2008
,
Statistical Methods for Categorical Data Analysis
,
Emerald Group Publishing
,
Bingley, UK.
57.
ElMaraghy
,
W.
,
ElMaraghy
,
H.
,
Tomiyama
,
T.
, and
Monostori
,
L.
,
2012
, “
Complexity in Engineering Design and Manufacturing
,”
CIRP Ann.
,
61
(
2
), pp.
793
814
.
You do not currently have access to this content.