Assessing perceived assembly complexity in human-robot collaboration processes: a proposal based on Thurstone’s law of comparative judgement

Abstract

Due to the growing demand for customised products, companies have faced increasing product and process complexity levels. To address this issue, manufacturing processes should become more flexible. One of the most promising technologies to achieve this goal is collaborative robotics (or ‘cobots’). In collaborative assembly processes, human and robot combine their skills. However, the co-existence of humans and cobots in the same workspace may influence the operators’ perception of assembly complexity. The analysis and control of assembly complexity are crucial to achieving better performances in terms of process quality and operators’ well-being. Many qualitative methods have been proposed in the literature to provide a holistic assessment of assembly complexity. This paper proposes a novel method to define a quantitative scale of perceived assembly complexity, based on Thurstone Law of Comparative Judgements. This method was applied to an experimental case-study concerning the assembly of three different products in two modalities (i.e. manual and collaborative). Regression analysis showed that the perceived complexity may be related to the occurrence of process failures and to the perceived workload. The method also proved capable of identifying assembly processes where cobot assistance was helpful, providing process designers with a supporting tool to minimise perceived complexity.

KEYWORDS:

• Perceived assembly complexity
• product complexity
• Thurstone law of comparative judgement
• human-robot collaboration
• process failures
• perceived workload

Acknowledgements

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M. Capponi and R. Gervasi. The first draft of the manuscript was written by M. Capponi and R. Gervasi under the supervision of L. Mastrogiacomo and F. Franceschini. All authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Compliance with ethical standards

The authors respect the Ethical Guidelines of the Journal. Informed consent was obtained from all individual participants included in the study.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Notes on contributors

Matteo Capponi

Matteo Capponi is a third year Ph.D. student in Management, Production and Design at Politecnico di Torino (Italy). He holds a bachelor’s degree in mechanical engineering and a master’s degree in management and production engineering. His research focuses on human-robot collaboration in manufacturing, quality engineering and production systems.

Riccardo Gervasi

Riccardo Gervasi is an Assistant Professor at Politecnico di Torino (Italy) – Department of Management and Production Engineering, where he also earned his doctorate, specialising in management, production, and design. He is the author and co-author of papers published in scientific journals and international conference proceedings on topics concerning human-robot collaboration and quality engineering.

Luca Mastrogiacomo

Luca Mastrogiacomo is a Full Professor of Quality Engineering and Manufacturing Systems at Politecnico di Torino (Italy). He received a doctorate in management, production and design from Politecnico di Torino. He is the author or co-author of many published papers in scientific journals and international conference proceedings regarding quality engineering and manufacturing systems.

Fiorenzo Franceschini

Fiorenzo Franceschini is a Full Professor of Quality Engineering at Politecnico di Torino (Italy) – Department of Management and Production Engineering. He is the author or co-author of 9 books and more than 300 published papers in prestigious scientific journals, and international conference proceedings. His current research interests are in the areas of quality engineering, industrial engineering and performance measurement systems.