Ali Ahmad Malik and Arne Bilberg
Over the past years, collaborative robots have been introduced as a new generation of industrial robotics working alongside humans to share the workload. These robots have the…
Abstract
Purpose
Over the past years, collaborative robots have been introduced as a new generation of industrial robotics working alongside humans to share the workload. These robots have the potential to enable human–robot collaboration (HRC) for flexible automation. However, the deployment of these robots in industrial environments, particularly in assembly, still comprises several challenges, of which one is skills-based tasks distribution between humans and robots. With ever-decreasing product life cycles and high-mix low volume production, the skills-based task distribution is to become a frequent activity. This paper aims to present a methodology for tasks distribution between human and robot in assembly work by complexity-based tasks classification.
Design/methodology/approach
The assessment method of assembly tasks is based on the physical features of the components and associated task description. The attributes that can influence assembly complexity for automation are presented. Physical experimentation with a collaborative robot and work with several industrial cases helped to formulate the presented method.
Findings
The method will differentiate the tasks with higher complexity of handling, mounting, human safety and part feeding from low-complexity tasks, thereby simplifying collaborative automation in HRC scenario. Such structured method for tasks distribution in HRC can significantly reduce deployment and changeover times.
Originality/value
Assembly attributes affecting HRC automation are identified. The methodology is presented for evaluating tasks for assigning to the robot and creating a work–load balance forming a human–robot work team. Finally, an assessment tool for simplified industrial deployment.
Details
Keywords
Kristian R. Petersen, Erik Skov Madsen and Arne Bilberg
This paper aims to explore how maintenance tasks can be planned and executed in a smarter way and, consequently, how the operations and maintenance of offshore wind power…
Abstract
Purpose
This paper aims to explore how maintenance tasks can be planned and executed in a smarter way and, consequently, how the operations and maintenance of offshore wind power installations can be improved through modularisation.
Design/methodology/approach
This is a case study of one of Europe’s leading offshore wind power operators with more than 1,000 wind turbine generators in operation. By focusing on this company, in-depth insights into its operations and maintenance processes are investigated.
Findings
Lean is identified to constitute an important first step before the modularisation of maintenance tasks. The modularisation of the maintenance of offshore wind farms is identified to reduce preventive maintenance times.
Practical implications
The paper develops a process to identify the resources needed for maintenance before the modularisation of maintenance tasks and resources can take place. The authors also establish a foundation for the development of a software tool to support the development of the modularisation of maintenance tasks.
Originality/value
The present study contributes to the rather immature field of research on the operations and maintenance of offshore wind power. Furthermore, it adds to the emerging research area of service modularity.