Production intralogistics automation based on 3D simulation analysis

• Autorzy: Mahmood Kashif , Karjust Kristo , Raamets Tonis

Identyfikatory

DOI

10.36897/jme/137081

• Języki publikacji: EN

Abstrakty

EN

Recent trends in manufacturing such as Industry 4.0 and Smart Manufacturing have brought the researchers' attention to the smart intralogistics in production facilities. Automated guided vehicles (AGV), especially mobile robots play a vital role in this development. On the other hand, industrial internet technologies offered new possibilities for the information exchange between devices, data integration platforms and communication interfaces to advance and facilitate the intralogistics for effective material handling and transportation. In order to analyse the feasibility and effectiveness of the mobile robots in the production area, 3D visualization should be combined with simulation, which provides a comprehensive possibility to evaluate and review the potential solution performance and its consistency before implementing practically into the production floor area. This paper describes a conceptual model based on 3D visualization and simulation and experimental study which help to make the decision according to the input data from the factory environment of the movement of mobile robots in production logistics. Moreover, the Key Performance Indicators (KPIs) are defined to analyse the use-case's process improvement in terms of the time reduction, which leads to increase productivity and cut-down the workers' fatigue.

Słowa kluczowe

EN

production logistics; mobile robot; intralogistics automation; 3D simulation; IoT sensor

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2021
• Tom: Vol. 21, No. 2
• Strony: 102--115
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Mahmood Kashif

• Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia

autor

Karjust Kristo

• Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia

autor

Raamets Tonis

• Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia

Bibliografia

• [1] GUNAL M.M., 2019, Simulation for Industry 4.0: Past, Present, and Future, Springer.
• [2] ERBOZ G., 2017, How to Define Industry 4.0: Main Pillars of Industry 4.0, 7th International Conference on Management, Nitra, Slovakia.
• [3] CLAUSEN U., LANGKAU S., KREUZ F., 2019, Advances in Production, Logistics and Traffic, Proceedings of 4th Interdisciplinary Conference on Production Logistics and Traffic, Springer.
• [4] VENKATAPATHY A.K., BAYHAN H., ZEIDLER F., HOMPEL M., 2017, Human Machine Synergies in Intra-Logistics: Creating a Hybrid Network for Research and Technologies, Proceedings of the Federated Conference on Computer Science and Information Systems, 1065–1068.
• [5] MASIK S., SCHULZE T., RAAB M., LEMESSI M., 2016, Comprehensive 3D Visualization of Simulated Processes in Virtual Factories, International Conf. Modeling, Sim. and Vis. Methods, 50–56.
• [6] MAHMOOD K., KARAULOVA T., OTTO T., SHEVTSHENKO E., 2019, Development of Cyber-Physical Production Systems Based on Modelling Technologies, Proceedings of the Estonian Academy of Sciences, 68, 348– 355.
• [7] MÖRTHA O., EMMANOUILIDIS C., HAFNER M., SCHADLER M., 2020, Cyber-Physical Systems for Performance Monitoring in Production Intralogistics, J. of Computers & Industrial Eng., 142, 1–10.
• [8] MAHMOOD K., KARAULOVA T., OTTO T., SHEVTSHENKO E., 2017, Performance Analysis of a Flexible Manufacturing System, Procedia CIRP, 63, 424–429.
• [9] PAAVEL M., KARJUST K., MAJAK J., 2017, PLM Maturity Model Development and Implementation in SME, Procedia CIRP, 63, 651–657.
• [10] FRAGAPANE G., IVANOV D., PERON M., SGARBOSSA F., STRANDHAGEN J.O., 2020, Increasing Flexibility and Productivity in Industry 4.0 Production Networks with Autonomous Mobile Robots and Smart Intralogistics, Annals of Operations Research, Springer.
• [11] SULE D.R., 2019, Manufacturing Facilities: Location, Planning, and Design, Boca Raton, CRC Press.
• [12] MOSALLAEIPOUR S., NEJAD M.G., SHAVARANI S.M., NAZERIAN R., 2018, Mobile Robot Scheduling for Cycle Time Optimization in Flow-Shop Cells, a Case Study, Production Engineering, 12, 83–94.
• [13] KUTS V., TAHEMAA T., OTTO T., SARKANS M., LEND H., 2016, Robot Manipulator Usage for Measurement in Production Areas, Journal of Machine Engineering, 16/1, 57–67.
• [14] KANGRU T., RIIVES J., OTTO T., KUTS V., MOOR M., 2020, Optimisation of Decision-Making Process in Industrial Robot Selection, Journal of Machine Engineering, 20/1, 70–81.
• [15] MAHMOOD K., LANZ M., TOIVONEN V., OTTO T., 2018, A Performance Evaluation Concept for Production Systems in an SME Network, Procedia CIRP, 72, 603–608.
• [16] WURLLA C., FRITZB T., HERMANNB Y., HOLLNAICHERB D., 2018, Production Logistics with Mobile Robots, ISR, 50th International Symposium on Robotics, 213–218.
• [17] MICHALOS G., KOUSI N., MAKRIS S., CHRYSSOLOURIS G., 2016, Performance Assessment of Production Systems with Mobile Robots, Procedia CIRP, 41, 195–200.
• [18] FISCHER M., RENKEN H., LAROQUE C., SCHAUMANN G., DANGELMAIER W., 2010, Automated 3D-Motion Planning for Ramps and Stairs in Intra-Logistics Material Flow Simulations, Proceedings of the 2010 Winter Simulation Conference, 1648–1660.
• [19] SCHOLZ M., et al., 2016, Integrating Intralogistics into Resource Efficiency Oriented Learning Factories, Procedia CIRP, 54, 239–244.
• [20] NIELSEN I., DANG Q., BOCEWICZ G., BANASZAK Z., 2017, A Methodology for Implementation of Mobile Robot in Adaptive Manufacturing Environments, Journal of Intelligent Manufacturing, 28, 1171–1188.
• [21] KAGANSKI S., MAJAK J., KARJUST K., TOOMPALU S., 2017, Implementation of Key Performance Indicators Selection Model as Part of the Enterprise Analysis Model, Procedia CIRP, 63, 283−288.
• [22] KAGANSKI S., MAJAK J., KARJUST K., 2018, Fuzzy AHP as a Tool for Prioritization of Key Performance Indicators, Procedia CIRP, 72, 603–608.
• [23] VISUAL COMPONENTS PREMIUM 4.2, https://www.visualcomponents.com/, (accessed 15 March 2020).
• [24] SNATKIN A., EISKOP T., KARJUST K., MAJAK J., 2015, Production Monitoring System Development and Modification, Proceedings of the Estonian Academy of Sciences, 64, 567−580.
• [25] HERRANEN H., KUUSIK A., SAAR T., REIDLA M., LAND R., MÄRTENS O., MAJAK J., 2014, Acceleration Data Acquisition and Processing System for Structural Health Monitoring, Proceedings of the IEEE International Workshop on Metrology for Aerospace, 244−249.
• [26] MAJAK J., POHLAK M., 2010, Optimal Material Orientation of Linear and Non-Linear Elastic 3D Anisotropic Materials, Meccanica, 45/5, 671−680.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).