Meat Factory Cell: Assisting meat processors address sustainability in meat production

Mason, Alex; Korostynska, O.; Cordova-Lopez, L. E.; Esper, Ian de Medeiros; Romanov, Dmytro; Ross, Steven C.; Takacs, Kristof; Haidegger, Tamás

doi:10.1109/cinti53070.2021.9668392

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…Input identification module -the function is to provide information to the control system about which part is at the input. [19][20][21] Module with translational movement and 2D measurement -intended for checking elements in 2D view (Figure 4a).…”

Section: Design Of Control Modulesmentioning

confidence: 99%

Modular Intelligent Control System in the Pre-assembly Stage

Micieta,

Macek,

Binasova

et al. 2024

Preprint

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This paper addresses the approach of fully automated intelligent control systems in production organizations, focusing on intelligent production systems research. The overview highlights a gap in control operations ahead of assembly as single-purpose control machines are frequently employed, which is precisely their drawback. Conversely, there are accurate multipurpose measurement centers that do not provide total quality control over the manufactured parts because of the cost and length of the measuring procedure. The study's primary objective was to create an intelligent modular control system, whose concept enables it to control various components. The purpose of the modular intelligent control system is to keep an eye on each module's quality during the pre-assembly phase. This system ensures exact control over the production process of modules by integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms. The technology makes it possible to monitor several parameters and important quality features. Integrated sensors and diagnostic technologies identify possible inaccuracies and disparities, guaranteeing prompt diagnosis of issues in specific modules. The system's intelligent control algorithms streamline the production process and guarantee synchronization between individual modules to achieve consistent quality and performance. One advantage of implementing the solution is that, on average, inspection time is reduced by 40 to 60% as compared to the prior manual inspection approach. In addition, full measurement data archiving was made available, and finally, the human element—which introduced a substantial error rate into this process—was removed. This system also helps to increase the project's overall efficiency, predictability, and safety while allowing for quick adjustments by standards and requirements.

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Section: Design Of Control Modulesmentioning

confidence: 99%

Modular Intelligent Control System in the Pre-assembly Stage

Micieta,

Macek,

Binasova

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Input identification module-the function is to provide information to the control system, some of which is used as the input [19][20][21].…”

Section: Design Of Control Modulesmentioning

confidence: 99%

Modular Intelligent Control System in the Pre-Assembly Stage

Micieta,

Macek,

Binasova

et al. 2024

Electronics

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This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability.

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“…When deciding whether to deploy a cobot in an enterprise, the advantages and disadvantages of such a move should be weighed. Cobots can increase productivity and improve the quality of production, but they can also impose high upfront costs and may require modifications to existing processes [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Interfacing the Control Systems of Enterprise-Level Process Equipment with a Robot Operating System

Micieta,

Binasova,

Marcan

et al. 2023

Electronics

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The analytical section of this paper deals with theoretical knowledge, considering the latest trends in the subject area. To achieve the successful implementation of cobots based on operating systems in manufacturing systems, it is necessary to pay attention specifically to the interfacing of Robot Operating Systems with the control systems of manufacturing systems at the process level of an enterprise. In the practical section, an algorithm with well-defined steps towards the successful implementation of cobots in holonic manufacturing processes is proposed. By setting up an experimental workstation in a laboratory, the proposed procedures are verified at the end of the paper. The creation of a design for the implementation of a collaborative robot with a human represents the missing link in the whole chain of commercial applications of the latest trends from the field of robotic systems in the industrial sphere. The main contributions of this study include the establishment of a communication channel between cobots and programmable logic PLCs (Programmable logic controllers), and an experimental verification of the proposed solution for the implementation of a cooperating robot with a human in a manufacturing system in the laboratory. Another advantage of this paper is the creation of a new procedure for conducting a risk analysis of cooperating robots and multipurpose autonomous devices.

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Meat Factory Cell: Assisting meat processors address sustainability in meat production

Cited by 10 publications

References 9 publications

Modular Intelligent Control System in the Pre-assembly Stage

Modular Intelligent Control System in the Pre-assembly Stage

Modular Intelligent Control System in the Pre-Assembly Stage

Interfacing the Control Systems of Enterprise-Level Process Equipment with a Robot Operating System

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