Production Intralogistics Automation Based on 3D Simulation Analysis
 
More details
Hide details
1
Mechanical and Industrial Engineering, Tallinn University of Technology, Estonia
 
 
Submission date: 2020-10-29
 
 
Final revision date: 2021-05-12
 
 
Acceptance date: 2021-05-13
 
 
Online publication date: 2021-06-10
 
 
Publication date: 2021-06-25
 
 
Corresponding author
Kashif Mahmood   

Mechanical and Industrial Engineering, Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn, Estonia
 
 
Journal of Machine Engineering 2021;21(2):102-115
 
KEYWORDS
TOPICS
ABSTRACT
Recent trends in manufacturing such as Industry 4.0 have brought the researchers' attention to smart production intralogistics. AGVs, especially mobile robots play a vital role in this development. Contrarily, industrial internet technologies offered new possibilities for information exchange, data integration and communication interfaces to advance and facilitate the intralogistics for effective material handling and transportation. To analyse the effectiveness of the mobile robots in the production area, 3D visualization should be combined with simulation, which provides a comprehensive possibility to evaluate the potential solution performance and its consistency before implementing it into the production floor. This paper describes a conceptual model for automation based on 3D visualization and simulation and experimental study which help to make the decision according to the input data from the production factory environment. Moreover, KPIs are analysed in terms of time reduction, which leads to an increase in productivity and cut-down the workers' fatigue.
REFERENCES (26)
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.c..., (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.
 
 
CITATIONS (5):
1.
Development of process optimization model for autonomous mobile robot used in production logistics
Tõnis Raamets, Jüri Majak, Kristo Karjust, Kashif Mahmood, Aigar Hermaste
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON RESEARCH ADVANCES IN ENGINEERING AND TECHNOLOGY - ITechCET 2022
 
2.
Simulation-based approach to analyze modular intralogistic systems in the chemical industry
Maik Pannok, Stefan Lier
Flexible Services and Manufacturing Journal
 
3.
Vehicle and Automotive Engineering 4
Tamás Bányai, Ákos Cservenák
 
4.
Safety System Assessment Case Study of Automated Vehicle Shuttle
Heiko Pikner, Raivo Sell, Jüri Majak, Kristo Karjust
Electronics
 
5.
What do we know about material handling in library? An empirical assessment in the Nordic region
Niloofar Jefroy, Fabio Sgarbossa
Digital Transformation and Society
 
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
Scroll to top