Energy Efficient Usage of Industrial Robots for Machining Processes

Uhlmann, Eckart; Reinkober, Sascha; Hollerbach, Tobias

doi:10.1016/j.procir.2016.03.241

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…Acemoglu et al, 2016;Ertel, 2019). In particular, Uhlmann et al (2016) and Dusik et al (2018) claimed that the large-scale applications of industrial robots are more likely to give rise to more energy consumption. However, they did not provide solid statistical and econometric analysis.…”

Section: The Impact Of Robots On Air Environment Mediated By Energy C...mentioning

confidence: 99%

Industrial robots and air environment: A moderated mediation model of population density and energy consumption

Luan

Yang

Chen

et al. 2022

Sustainable Production and Consumption

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Section: The Impact Of Robots On Air Environment Mediated By Energy C...mentioning

confidence: 99%

Industrial robots and air environment: A moderated mediation model of population density and energy consumption

Luan

Yang

Chen

et al. 2022

Sustainable Production and Consumption

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“…Afterwards, they updated a set of modal analysis, in which both identified natural frequencies and damping ratios were used to update an elastodynamic model of the robot through a minimisation procedure [144]. Energy efficiency was given an attention by Uhlmann et al [145], and they identified the energy status which provides energy-optimised path planning in CAM system of robot machining. In addition, Denkena et al [146] discussed the needed changes of the conventional process planning chain to adapt robotic machining.…”

Section: Other Aspectsmentioning

confidence: 99%

Industrial robotic machining: a review

Wang

2019

Int J Adv Manuf Technol

271

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For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

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“…More recently they proposed a new algorithm designed to minimize the EC by changing the robot speed, taking into consideration the robot positions as well as the power-saving modes (Bukata et al, 2017 ), (Bukata et al, 2019 ). Uhlmann et al conducted a study on EC of industrial robots for machining processes and their experimental tests also showed that the robot movement speed has a major impact on the EC along with the cooling systems (Uhlmann et al, 2016 ).…”

Section: R Elated Workmentioning

confidence: 99%

Digital Twin and web services for robotic deburring in intelligent manufacturing

et al. 2022

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The development of modern manufacturing requires key solutions to enhance the intelligence of manufacturing such as digitalization, real-time monitoring, or simulation techniques. For smart robotic manufacturing, the modern approach regarding robot programming and process planning aims for both high efficiency and energy-awareness. During the design and manufacturing stages, optimization becomes crucial and can be fulfilled by means of appropriate digital manufacturing tools. This paper presents the development of a Digital Twin for a robotic deburring workcell along with the process planning and robot programming. Considering a large size workpiece, a new robot programming solution was implemented, based on image processing to safely re-machine only areas where burrs could not be completely removed in the main deburring routine. The work also covers the development of a new web platform to remotely monitor the robotic workcell, to trigger alerts for unexpected events and to allow the control to authorized personnel enabled by the employment of robot web services following an architectural RESTful style which establishes a communication link to the robot virtual controller. The aim of this research is to integrate the Digital Twin with the innovative proposals of Industry 4.0, offering a project-based model of smart robotic manufacturing and experience concepts such as Cyber-Physical System, digitalization, data acquisition, continuous monitoring, and intelligent solutions in a novel approach. Furthermore, the work covers energy consumption strategies for energy-aware robotic manufacturing. Finally, the results of an energy-efficient motion planning along with signal-based scheduling optimization of the robotic deburring cell are discussed.

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Energy Efficient Usage of Industrial Robots for Machining Processes

Cited by 16 publications

References 9 publications

Industrial robots and air environment: A moderated mediation model of population density and energy consumption

Industrial robots and air environment: A moderated mediation model of population density and energy consumption

Industrial robotic machining: a review

Digital Twin and web services for robotic deburring in intelligent manufacturing

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