Robot-process precision modelling for the improvement of productivity in flexible manufacturing cells

Ferreras-Higuero, Eugenio; Leal-Muñoz, Erardo; Jalón, Javier García de; Chacón, Édgar; Vizán, Antonio

doi:10.1016/j.rcim.2020.101966

Cited by 23 publications

(15 citation statements)

References 29 publications

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“…In this research, prior findings have been cumulated (Li et al, 2015;Palombarini & Martínez, 2012;Siafara et al, 2018;Din et al, 2019) clarifying that artificial intelligence data-driven Internet of Things systems (Cavallo et al, 2021;Chung & Yoo, 2020;Li et al, 2021b;Qin & Lu, 2021), deep learning-assisted smart process planning (Elia & Margherita, 2021;Gain, 2021;Liu et al, 2022;Penumuru et al, 2020), and real-time sensor networks (Ferreras-Higuero et al, 2020;Ksentini et al, 2021;Hu et al, 2016;Ding et al, 2021) advance constantly optimized smart manufacturing systems. In this research, previous published findings have been cumulated clarifying that cognitive capabilities lead to increased flexibility and variability (Dumitrache et al, 2019;Emmer et al, 2018;Casadei et al, 2019;Hu et al, 2019) that enable streamlined production.…”

Section: Introductionmentioning

confidence: 86%

“…Cognitive manufacturing is pivotal in sustainable Industry 4.0 wireless networks (Chung et al, 2019;Din et al, 2019;Ferreras-Higuero et al, 2020;Hu et al, 2016;Ksentini et al, 2021) together with blockchain distributed ledger that ensures soundness, safety, and security through miningbased smart data technologies. By leveraging data mining techniques throughout cognitive manufacturing processes, information can be obtained and intrinsic rules are identified: the mining operation assists in the configuration of big data-driven decision-making processes.…”

Section: Cutting-edge Cognitive Computing Big Data Analytics Techniqu...mentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Lăzăroiu

Androniceanu

Grecu

et al. 2022

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Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including ?cognitive Industrial Internet of Things?, ?cognitive automation?, ?cognitive manufacturing systems?, ?cognitively-enhanced machine?, ?cognitive technology-driven automation?, ?cognitive computing technologies,? and ?cognitive technologies.? The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

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Section: Introductionmentioning

confidence: 86%

Section: Cutting-edge Cognitive Computing Big Data Analytics Techniqu...mentioning

confidence: 99%

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Lăzăroiu

Androniceanu

Grecu

et al. 2022

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“…Further manufacturing process-related tolerance models include fixturing and the consideration of manufacturing signatures [31,32]. In the field of robotic machining, a working precision analysis is presented in [33] using a robot-process model, to investigate the robot-and process-related influencing factors.…”

Section: Tolerance Modelingmentioning

confidence: 99%

Tolerance analysis for robotic pick-and-place operations

Tipary

Erdös

2021

Int J Adv Manuf Technol

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Robotic workcell design is a complex process, especially in case of flexible (e.g., bin-picking) workcells. The numerous requirements and the need for continuous system validation on multiple levels place a huge burden on the designers. There are a number of tools for analyzing the different aspects of robotic workcells, such as CAD software, system modelers, or grasp and path planners. However, the precision aspect of the robotic operation is often overlooked and tackled only as a matter of manipulator repeatability. This paper proposes a designer tool to assess the precision feasibility of robotic pick-and-place workcells from the operation point of view. This means that not only the manufacturing tolerances of the workpiece and the placing environment are considered, but the tolerance characteristics of the manipulation and metrology process (in case of flexible applications) as well. Correspondingly, the contribution of the paper is a novel tolerance modeling approach, where the tolerance stack-up is set up as a transformation chain of low-order kinematic pairs between the workpiece, manipulator, and other workcell components, based on manipulation, seizing, releasing, manufacturing, and metrology tolerances. Using this representation, the fulfillment of functional requirements (e.g., picking or placing precision) can be validated based on the tolerance range of corresponding chain members. By having a generalized underlying model, the proposed method covers generic industrial pick-and-place applications, including both conventional and flexible ones. The application of the method is presented in a semi-structured pick-and-place scenario.

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“…The robot configuration is evaluated to improve the machining accuracy by a stiffness-based method [18,19]. Applying quaternion interpolation algorithm and concluding all cutting tool angles influence, an efficient model is developed to predict achievable precision for drilling [20]. A new parallel robot for minimally invasive surgery is constructed [21].…”

Section: Introductionmentioning

confidence: 99%

A Fast Task Planning System for 6R Articulated Robots Based on Inverse Kinematics

Lai

2022

Preprint

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Robots are becoming increasingly popular in workplaces to work next to humans and may always need humans to program them for tasks. Technicians could make mistakes that cause unexpected consequences. Robots bring eventful impacts to the workplace, benefiting from the advantages of implementing any technology should be based on the premise of safety. This work proposes a systematic method to establish a postprocessor module for any specified 6R Articulated Robot. Instead of obsessively emphasizing to achieve the desired locations and orientations by hand guiding grab and drag a robotic arm for operation in teaching mode, the significant end-effector poses are calculated to form a new path for the joints efficiently in this study. Since robotic motion control is usually a complex system whose users must be well trained and acquainted with using them. There is a need for a GUI solution that can provide convenient and intuitive robotic motion control on the current location of the user independently, easy setup, arrangement, adjustment, and monitoring robot motion tasks. The proposed system simplifies the interaction between the technician and the industrial robotic arm in the case of robotic motion control and tracking at a distant location. The presented method is fully adapted to alternate between joint angles and end-effector poses on a graphic user interface system. Examples with specified posture and predefined movements are demonstrated for corroborating the algorithm. The results show considerable efficiency and reliability in path planning and are fully supported for automatic path generation.

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Robot-process precision modelling for the improvement of productivity in flexible manufacturing cells

Cited by 23 publications

References 29 publications

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Tolerance analysis for robotic pick-and-place operations

A Fast Task Planning System for 6R Articulated Robots Based on Inverse Kinematics

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