Energy-efficient concurrent assessment of industrial robot operation based on association rules in manufacturing

Wang, Shengnan; Zhang, Xiaomei; Xu, Wenjun; Liu, Aiming; Zhou, Zude; Pham, Duc Truong

doi:10.1109/icnsc.2018.8361334

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Park et al (2020) applied a neural network based on among others sensor data from IIoT to optimize the energy consumption of a dyeing process. Wang et al (2018) developed an assessment method with association rules to increase the energy efficiency of industrial robots.…”

Section: Resultsmentioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

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Advanced analytics applications in smart manufacturing provide beneficiary effects such as predictive maintenance, energy optimized factories, and automized quality management of products. While the benefits and thus the sustainability effects are well investigated, the research field is characterized by many single applications with little conceptual synthesis. Therefore, this research aims to systematize the field and to identify specific sustainability themes in context of advance analytics applications. This chapter provides an overview of existing advanced analytics applications in smart manufacturing and clusters the addressed sustainability themes. Applying a systematic literature review, following the PRISMA statement, 65 different applications within four research perspective could be identified. Furthermore, ten different sustainability-related effects of the applications in smart manufacturing unfold. The content analysis resulted in 27 sustainability themes, which holistically cover all spheres of sustainability, with a focus on the social and economic spheres.

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Section: Resultsmentioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

View full text Add to dashboard Cite

show abstract

“…[34] combined FAHP, VIKOR, and TOPSIS methods to comprehensively evaluate the processing risk of a certain part in the automobile manufacturing industry using FAHP-fuzzy VIKOR method. From the perspective of whether it can reflect the dynamic changes of the object to be evaluated over time, the evaluation methods can be divided into two categories: static evaluation [35] and dynamic evaluation [36]. In the manufacturing process with uncertainty and dynamics, static assessment methods are not applicable for ICRs manufacturing capability which is dynamically changed in the manufacturing process.…”

Section: Manufacturing Capability Correlation and Evaluation Methodsmentioning

confidence: 99%

Dynamic assessment of sustainable manufacturing capability based on correlation relationship for industrial cloud robotics

Tian

Xie

et al. 2021

Int J Adv Manuf Technol

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The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment is the key link in determining the optimal service composition to realize the value-added of ICRs resources. However, the traditional evaluation method ignores the positive and negative cooperative effects of the manufacturing capability correlation among the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and serious environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship to solve above problems. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which the evaluation indicators raw data is processed based on the service correlation model and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in assessment of sustainable manufacturing capability for ICRs.

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Modelling and Vibration Signal Analysis for Condition Monitoring of Industrial Robots

Han

Shi

et al. 2022

Proceedings of IncoME-VI and TEPEN 2021

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Energy-efficient concurrent assessment of industrial robot operation based on association rules in manufacturing

Cited by 5 publications

References 21 publications

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Dynamic assessment of sustainable manufacturing capability based on correlation relationship for industrial cloud robotics

Modelling and Vibration Signal Analysis for Condition Monitoring of Industrial Robots

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